{ "description": "Browse aymeric's, an independent, searchable archive of readable snapshots from followed feeds, preserved in Git with aggr.", "feed_url": "https://aggr.aymericbeaumet.com/sources/qwen.ai/feed.json", "home_page_url": "https://aggr.aymericbeaumet.com/sources/qwen.ai/", "items": [ { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"Qwen-Image-2.1

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GITHUB HUGGING FACE MODELSCOPE

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We are excited to open-source Qwen-Image-2.1, an image model in the Qwen family that balances generation quality, inference efficiency, and cost. Qwen-Image-2.1 unifies text-to-image generation and image editing in a single model, with just 7B parameters in its visual generation component and native support for generating and editing transparent images.

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This update introduces four key improvements:

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Compact and Efficient

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Qwen-Image-2.1 uses a lightweight architecture. Its visual generation component contains 32 Single-Stream DiT layers and 7B parameters. Despite its compact size, the model delivers strong image generation quality. The Qwen-Image-Bench comparison below shows its performance alongside other open-source and closed-source models.

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\"Qwen-Image-Bench

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Qwen-Image-Bench evaluation comparison

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Inference efficiency is another focus, particularly when editing with multiple input images. This optimization comes from a mixed-granularity attention architecture. Text, including the system prefix and editing instructions, uses a token-level causal mask, while image generation uses a chunk-level mask. KV cache reuse allows input images and editing instructions to serve as static context, computed and cached in the first step to improve inference efficiency and reduce memory usage.

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\"Qwen-Image-2.1

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Qwen-Image-2.1 mixed-granularity attention architecture

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Native Transparency, Unified Creation and Editing

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Native transparency is a major addition in this release. In December 2025, we introduced Qwen-Image-Layered as a dedicated model supporting transparent image generation. Qwen-Image-2.1 now integrates this capability into a unified model, using the prompt to determine whether to output a regular image or an image with a transparency channel.

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Here are examples of transparent images generated directly from text:

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\"Transparent \"Transparent \"Transparent

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Beyond individual subjects, the model can generate more complex transparent images composed of multiple elements, opening up further possibilities for design and asset creation.

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\"Transparent \"Transparent

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By unifying generation and editing, Qwen-Image-2.1 also supports editing transparent images directly. For example, it can change a subject’s expression while retaining the transparent background. The input is on the left and the edited result on the right.

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\"Expression \"Expression

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Text within a transparent layer can also be edited. In the following example, “BLOOM” is replaced with “Qwen-Image”.

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\"Transparent \"Transparent

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This capability extends to real photographs. Given an RGB image, the model can extract the desired subject as an RGBA layer with transparency, making it easier to reuse elements in subsequent design and composition work.

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\"Extracting \"Extracting

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Versatile Editing

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Qwen-Image-2.1 improves editing in four areas: multiple reference images, local editing, fidelity preservation, and task coverage.

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Multiple References: Up to 10 Input Images

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Qwen-Image-2.1 supports up to 10 reference images, combining multiple subjects and assets into a coherent composition. In the group portrait example below, six individual portraits on the left are brought together in one photograph.

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\"A

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A group photograph generated from six portrait references

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For virtual try-on, five inputs—a model, clothing, shoes, a bag, and a hat—can be combined into a complete outfit.

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\"A

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A complete outfit generated from five reference images

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For interior design, the model can use 10 images of furnishings to generate a complete room arrangement.

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\"An

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An interior generated from ten furnishing references

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Local Editing: Flexible Region Selection

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Qwen-Image-2.1 supports circles, painted annotations, and separate masks to specify where an edit should take place.

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The first example uses circles of different colors to identify three regions at once: “Remove the metal watch in the blue circle, change the hair in the red circle to black, and replace the area in the green circle with gray short-sleeved linen pajamas.” The annotated input is on the left, and the result is on the right.

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\"Circle-guided \"Circle-guided

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Painted annotations provide another way to identify a region. In this example, the model adds a diver to the area marked in white on the right side of the input image.

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\"Paint-guided \"Paint-guided

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Circles and painted annotations obscure some of the original content. To preserve the complete input, Qwen-Image-2.1 also accepts the original image and a separate mask as two inputs. The following example asks the model to generate a cowboy on horseback, using these two images:

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\"Local \"Local

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The model edits the region specified by the mask, producing the following result:

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\"Mask-guided

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Mask-guided editing result: a cowboy on horseback

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Local editing can also support sequential creation. By making successive changes while preserving the rest of the scene, edited images can be assembled into simple animations, as illustrated by the capybara Calabash Brothers example below.

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Capybara Calabash Brothers: animation through successive local edits

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Fidelity: Preserving People and Products

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Fidelity improvements focus on portrait identity and product consistency. For portraits, the model better preserves facial features so that a person’s identity remains more consistent across edits. Each pair below shows the input on the left and the edited result on the right.

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\"Portrait \"Portrait

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\"Portrait \"Portrait

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\"Portrait \"Portrait

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For product editing, the model aims to preserve text, textures, and shape, keeping the product’s defining features consistent in a new image.

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\"Product \"Product

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\"Product \"Product

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\"Product \"Product

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Task Coverage: Panoramas, Infographics, and Storyboards

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Qwen-Image-2.1 supports a broad range of editing tasks, including panorama, infographic, and storyboard generation, balancing capabilities across different applications.

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For example, starting from this selfie:

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\"Input

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Input selfie for panorama generation

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The model generates the following panorama:

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\"Panorama

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Panorama generated from the selfie

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The panorama can then be explored from different viewing directions in a visualization tool.

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Panorama visualization demonstration

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For infographic generation, a model photograph can be expanded into a detailed, information-rich composition.

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\"Input

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Input model photograph for infographic generation

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\"Complex

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Complex infographic generated from the model photograph

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The model can also turn a three-view character reference into a complete storyboard, providing material for storytelling and visual narratives.

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\"Three-view

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Three-view character reference for storyboard generation

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\"Complete

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Complete storyboard generated from the character reference

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Realistic Textures and Refined Aesthetics

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Qwen-Image-2.1 further improves visual quality, with particular attention to typography and portraits. Text rendering considers not only the content itself, but also type styles, layout, and their relationship to the overall composition. The following examples showcase text rendering across different settings.

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\"Text

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Text rendering example one

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\"Text

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Text rendering example two

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\"Text

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Text rendering example three

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\"Text

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Text rendering example four

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For portraits, improved lighting and fine details give generated images a more realistic appearance.

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\"Portrait

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Portrait lighting and detail example one

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\"Portrait

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Portrait lighting and detail example two

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Conclusion

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With a compact 7B visual generation component, Qwen-Image-2.1 brings image generation, transparency, and a wide range of editing capabilities together in one model. Faster inference, support for up to 10 reference images, flexible local editing, and improved fidelity for people and products make it a practical tool for design, content creation, e-commerce, and visual storytelling.

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We hope you enjoy creating with Qwen-Image-2.1!

\n", "date_published": "2026-09-20T12:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen-image-2.1", "id": "urn:aggr:item:d9b80432196d17341a4c91c7124d7e940a57c118", "image": "https://img.alicdn.com/imgextra/i1/O1CN01XLHYPGwKvuI29R4a_!!6000000005961-2-tps-1060-636.png", "summary": "We are excited to open-source Qwen-Image-2.1, an image model in the Qwen family that balances generation quality, inference efficiency, and cost. Qwen-Image-2.1 unifies text-to-image generation and image editing in a single model, with…", "tags": [ "ai", "open-source" ], "title": "Qwen-Image-2.1: Compact, Efficient, and Unified Image Creation", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-09-20-qwen-image-2-1-compact-efficient-and-unified-image-creation/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"Qwen3.8-LiveTranslate

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API Demo

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Simultaneous interpretation is not only about translating fast — it must also hear clearly and translate accurately. Qwen3.8-LiveTranslate rebuilds real-time simultaneous interpretation with an Interleave architecture, improving faithfulness, fluency, and conciseness across the board, while average lagging (LAAL) drops from 2.8 seconds to 2.3 seconds. We want simultaneous interpretation to convey not only language but also the people and context behind a conversation. Building on support for 60 languages, Qwen3.8-LiveTranslate adds three capabilities that bring simultaneous interpretation to a wider range of real-world scenarios: real-time speaker separation, with clear attribution for every sentence and more stable voice cloning; synchronized source-and-translation output on one bilingual screen; and long-context disambiguation that reads the present in light of what came before, making names and terminology more precise.

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Key Highlights

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\"Qwen3.8-LiveTranslate

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Model Architecture

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Qwen3.8-LiveTranslate adopts a Hybrid-MoE-based Thinker–Talker two-module design, connecting streaming understanding, text output, and speech generation through interleaving. Here, the Thinker arranges video, audio, source text, and translation into a single causal sequence — interleaved in temporal order and produced end to end — so that understanding and translation happen within one sequence; the Talker then combines the translation and the source audio to synthesize the translation into speech that preserves the original speaker’s timbre.

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\"Qwen3.8-LiveTranslate

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Performance

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Multi-Speaker Long-Audio

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On Omnilingua-MSpeaker, a multi-speaker long-audio evaluation set covering 14 language directions, Qwen3.8-LiveTranslate outperforms today’s mainstream real-time interpretation systems across four dimensions: translation faithfulness, fluency, and conciseness, together with the Diarization Error Rate (DER).

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\"Multi-Speaker

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Multilingual Performance

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We evaluate real-time interpretation performance across 70 language directions on the public FLEURS audio test set. Qwen3.8-LiveTranslate leads both the previous generation and current mainstream real-time interpretation systems across four dimensions: translation quality, average lagging, speech recognition accuracy, and speech synthesis quality.

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\"Multilingual

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🎬 See It in Action

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Using two dialogues from Journey to the West and a set of homophone examples, the demos below showcase speaker attribution and voice cloning, synchronized bilingual output, and how context and visual information help with translation disambiguation.

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Supported Languages

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
CategorySupported languages
Input audio & output text60 languages Chinese, English, German, Italian, Portuguese, Spanish, Japanese, Korean, French, Russian, Thai, Indonesian, Arabic, Vietnamese, Turkish, Finnish, Polish, Hindi, Dutch, Czech, Urdu, Filipino, Swedish, Danish, Hebrew, Icelandic, Malay, Norwegian, Persian, Afrikaans, Asturian, Azerbaijani, Belarusian, Bengali, Bosnian, Bulgarian, Cantonese, Catalan, Cebuano, Croatian, Estonian, Galician, Gujarati, Greek, Hungarian, Javanese, Kannada, Kazakh, Kyrgyz, Latvian, Macedonian, Malayalam, Marathi, Punjabi, Romanian, Slovak, Slovenian, Swahili, Tajik, Ukrainian
Output audio29 languages Chinese, English, German, Italian, Portuguese, Spanish, Japanese, Korean, French, Russian, Thai, Indonesian, Arabic, Vietnamese, Turkish, Finnish, Polish, Hindi, Dutch, Czech, Urdu, Filipino, Swedish, Danish, Hebrew, Icelandic, Malay, Norwegian, Persian
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Using Qwen3.8-LiveTranslate via DashScope API

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Below is a complete real-time interpretation client example: it captures microphone audio, streams it to the server, and receives and plays back the translation. This generation’s new speaker separation and synchronized source-and-translation output capabilities are both enabled through session configuration; the server returns the speaker identifier and the source-language text alongside the translation, with no need to call a separate ASR interface.

\n
import os\nimport time\nimport base64\nimport asyncio\nimport json\nimport websockets\nimport pyaudio\nimport queue\nimport threading\nimport traceback\n\n\nclass LiveTranslateClient:\n    """Client for the DashScope live-translation service: captures mic audio, sends it to the server, and plays back the translated speech."""\n\n    def __init__(self, api_key: str, workspace_id: str, target_language: str = "en", *, audio_enabled: bool = True):\n        if not api_key:\n            raise ValueError("API key cannot be empty.")\n        if not workspace_id:\n            raise ValueError("Workspace ID cannot be empty.")\n\n        self.api_key = api_key\n        self.workspace_id = workspace_id\n        self.target_language = target_language\n        self.audio_enabled = audio_enabled\n        self.ws = None\n        # URL for the China (Beijing) region; replace {workspace_id} with your Model Studio workspace ID. URLs differ by region.\n        self.api_url = (\n            f"wss://{workspace_id}.cn-beijing.maas.aliyuncs.com"\n            "/api-ws/v1/realtime?model=qwen3.8-livetranslate-flash-realtime"\n        )\n\n        # Audio input parameters (microphone capture)\n        self.input_rate = 16000\n        self.input_chunk = 1600\n        self.input_format = pyaudio.paInt16\n        self.input_channels = 1\n\n        # Audio output parameters (local playback)\n        self.output_rate = 24000\n        self.output_chunk = 2400\n        self.output_format = pyaudio.paInt16\n        self.output_channels = 1\n\n        # Runtime state and playback resources\n        self.is_connected = False\n        self.audio_player_thread = None\n        self.audio_playback_queue = queue.Queue()\n        self.pyaudio_instance = pyaudio.PyAudio()\n        # Set once the server returns session.finished, so close() can wait for a clean shutdown.\n        self.session_finished_event = asyncio.Event()\n\n    async def connect(self):\n        """Open a WebSocket connection to the translation service."""\n        headers = {"Authorization": f"Bearer {self.api_key}"}\n        try:\n            self.ws = await websockets.connect(self.api_url, additional_headers=headers)\n            self.is_connected = True\n            print(f"Successfully connected to server: {self.api_url}")\n            await self.configure_session()\n        except Exception as e:\n            print(f"Connection failed: {e}")\n            self.is_connected = False\n            raise\n\n    async def configure_session(self):\n        """Configure the translation session: target language, audio formats, and optional features."""\n        config = {\n            "event_id": f"event_{int(time.time() * 1000)}",\n            "type": "session.update",\n            "session": {\n                # `output_modalities` decides what the server returns:\n                #   ["text", "audio"] — both translated text and synthesized speech (recommended)\n                #   ["text"]          — translated text only\n                "output_modalities": ["text", "audio"] if self.audio_enabled else ["text"],\n                "input_audio_format": "pcm",\n                "output_audio_format": "pcm",\n                # `input_audio_transcription`: enable source-language ASR.\n                # Setting `model` to 'qwen3-asr-flash-realtime' also streams back the source transcript.\n                # "input_audio_transcription": {\n                #     "model": "qwen3-asr-flash-realtime",\n                #     "language": "zh"  # source language; defaults to 'en'\n                # },\n                "translation": {\n                    "language": self.target_language,\n                    # `corpus`: register hotwords to boost accuracy on proper nouns and domain-specific terms.\n                    # "corpus": {\n                    #     "phrases": {\n                    #         "人工智能": "Artificial Intelligence",\n                    #         "机器学习": "Machine Learning"\n                    #     }\n                    # }\n                }\n            }\n        }\n        print(f"Sending session config: {json.dumps(config, indent=2, ensure_ascii=False)}")\n        await self.ws.send(json.dumps(config))\n\n    async def send_audio_chunk(self, audio_data: bytes):\n        """Base64-encode an audio chunk and send it to the server."""\n        if not self.is_connected:\n            return\n\n        event = {\n            "event_id": f"event_{int(time.time() * 1000)}",\n            "type": "input_audio_buffer.append",\n            "audio": base64.b64encode(audio_data).decode()\n        }\n        await self.ws.send(json.dumps(event))\n\n    async def send_image_frame(self, image_bytes: bytes, *, event_id: str | None = None):\n        """Send an image frame to the server as visual context for translation."""\n        if not self.is_connected:\n            return\n        if not image_bytes:\n            raise ValueError("image_bytes cannot be empty")\n\n        image_b64 = base64.b64encode(image_bytes).decode()\n        event = {\n            "event_id": event_id or f"event_{int(time.time() * 1000)}",\n            "type": "input_image_buffer.append",\n            "image": image_b64,\n        }\n        await self.ws.send(json.dumps(event))\n\n    def _audio_player_task(self):\n        """Background thread task: drain PCM chunks from the playback queue and write them to the speaker output stream."""\n        stream = self.pyaudio_instance.open(\n            format=self.output_format,\n            channels=self.output_channels,\n            rate=self.output_rate,\n            output=True,\n            frames_per_buffer=self.output_chunk,\n        )\n        try:\n            while self.is_connected or not self.audio_playback_queue.empty():\n                try:\n                    audio_chunk = self.audio_playback_queue.get(timeout=0.1)\n                    if audio_chunk is None:  # sentinel: stop the playback loop\n                        break\n                    stream.write(audio_chunk)\n                    self.audio_playback_queue.task_done()\n                except queue.Empty:\n                    continue\n        finally:\n            stream.stop_stream()\n            stream.close()\n\n    def start_audio_player(self):\n        """Spin up the background audio playback thread (no-op when audio output is disabled)."""\n        if not self.audio_enabled:\n            return\n        if self.audio_player_thread is None or not self.audio_player_thread.is_alive():\n            self.audio_player_thread = threading.Thread(target=self._audio_player_task, daemon=True)\n            self.audio_player_thread.start()\n\n    async def handle_server_messages(self, on_text_received):\n        """Continuously receive and dispatch event messages pushed by the server."""\n        try:\n            async for message in self.ws:\n                event = json.loads(message)\n                event_type = event.get("type")\n                if event_type == "response.audio.delta" and self.audio_enabled:\n                    audio_b64 = event.get("delta", "")\n                    if audio_b64:\n                        audio_data = base64.b64decode(audio_b64)\n                        self.audio_playback_queue.put(audio_data)\n\n                elif event_type == "response.done":\n                    print("\\n[INFO] Response complete.")\n                    usage = event.get("response", {}).get("usage", {})\n                    if usage:\n                        print(f"[INFO] Token usage: {json.dumps(usage, indent=2, ensure_ascii=False)}")\n                # Receive source-language ASR results (requires input_audio_transcription.model to be enabled)\n                # elif event_type == "conversation.item.input_audio_transcription.delta":\n                #     delta = event.get("delta", "")  # incremental transcript\n                #     print(f"[Recognizing] {delta}", end="", flush=True)\n                # elif event_type == "conversation.item.input_audio_transcription.completed":\n                #     transcript = event.get("transcript", "")  # final transcript for an utterance\n                #     print(f"[Source] {transcript}")\n                # In voice + text mode, the incremental translation text arrives alongside synthesized audio\n                elif event_type == "response.audio_transcript.delta":\n                    on_text_received(event.get("delta", ""))\n                # In text-only mode, the incremental translation arrives via response.text.delta\n                elif event_type == "response.text.delta":\n                    on_text_received(event.get("delta", ""))\n                # The server acknowledges session.finish and completes the shutdown handshake\n                elif event_type == "session.finished":\n                    print("\\n[INFO] Session finished.")\n                    self.session_finished_event.set()\n\n        except websockets.exceptions.ConnectionClosed as e:\n            print(f"[WARNING] Connection closed: {e}")\n            self.is_connected = False\n        except Exception as e:\n            print(f"[ERROR] Unknown error during message handling: {e}")\n            traceback.print_exc()\n            self.is_connected = False\n\n    async def start_microphone_streaming(self):\n        """Continuously capture microphone audio and stream it to the server in real time."""\n        stream = self.pyaudio_instance.open(\n            format=self.input_format,\n            channels=self.input_channels,\n            rate=self.input_rate,\n            input=True,\n            frames_per_buffer=self.input_chunk\n        )\n        print("Microphone started, please begin speaking...")\n        try:\n            while self.is_connected:\n                audio_chunk = await asyncio.get_event_loop().run_in_executor(\n                    None, stream.read, self.input_chunk\n                )\n                await self.send_audio_chunk(audio_chunk)\n        finally:\n            stream.stop_stream()\n            stream.close()\n\n    async def close(self):\n        """Gracefully close the WebSocket connection and release audio resources."""\n        # Ask the server to finish the session, then wait for its session.finished acknowledgement.\n        if self.is_connected and self.ws:\n            finish_event = {\n                "event_id": f"event_{int(time.time() * 1000)}",\n                "type": "session.finish",\n            }\n            await self.ws.send(json.dumps(finish_event))\n            print("Sent session.finish, waiting for the server to finish processing...")\n            try:\n                await asyncio.wait_for(self.session_finished_event.wait(), timeout=15)\n                print("Server finished processing.")\n            except asyncio.TimeoutError:\n                print("Timed out waiting for session.finished.")\n\n        self.is_connected = False\n        if self.ws:\n            await self.ws.close()\n            print("WebSocket connection closed.")\n\n        if self.audio_player_thread:\n            self.audio_playback_queue.put(None)  # signal the playback thread to exit\n            self.audio_player_thread.join(timeout=1)\n            print("Audio playback thread stopped.")\n\n        self.pyaudio_instance.terminate()\n        print("PyAudio instance released.")\n\n\ndef print_banner():\n    print("=" * 60)\n    print("  Powered by Qwen qwen3.8-livetranslate-flash-realtime")\n    print("=" * 60 + "\\n")\n\n\ndef get_user_config():\n    """Collect runtime parameters from the user via CLI: output mode and target language."""\n    print("Select mode:")\n    print("1. Voice + Text [default] | 2. Text only")\n    mode_choice = input("Enter option (press Enter for Voice + Text): ").strip()\n    audio_enabled = (mode_choice != "2")\n\n    if audio_enabled:\n        lang_map = {\n            "1": "en", "2": "zh", "3": "ru", "4": "fr", "5": "de", "6": "pt",\n            "7": "es", "8": "it", "9": "ko", "10": "ja", "11": "yue"\n        }\n        print("Select target translation language (Voice + Text mode):")\n        print("1. English | 2. Chinese | 3. Russian | 4. French | 5. German | 6. Portuguese | 7. Spanish | 8. Italian | 9. Korean | 10. Japanese | 11. Cantonese")\n    else:\n        lang_map = {\n            "1": "en", "2": "zh", "3": "ru", "4": "fr", "5": "de", "6": "pt", "7": "es", "8": "it",\n            "9": "id", "10": "ko", "11": "ja", "12": "vi", "13": "th", "14": "ar",\n            "15": "yue", "16": "hi", "17": "el", "18": "tr"\n        }\n        print("Select target translation language (Text only mode):")\n        print("1. English | 2. Chinese | 3. Russian | 4. French | 5. German | 6. Portuguese | 7. Spanish | 8. Italian | 9. Indonesian | 10. Korean | 11. Japanese | 12. Vietnamese | 13. Thai | 14. Arabic | 15. Cantonese | 16. Hindi | 17. Greek | 18. Turkish")\n\n    choice = input("Enter option (default is the first one): ").strip()\n    target_language = lang_map.get(choice, next(iter(lang_map.values())))\n\n    return target_language, audio_enabled\n\n\nasync def main():\n    """Program entry point: connect, configure the session, and drive the live-translation loop."""\n    print_banner()\n\n    api_key = os.environ.get("DASHSCOPE_API_KEY")\n    if not api_key:\n        print("[ERROR] Please set the environment variable DASHSCOPE_API_KEY")\n        print("  Example: export DASHSCOPE_API_KEY='your_api_key_here'")\n        return\n\n    workspace_id = os.environ.get("DASHSCOPE_WORKSPACE_ID")\n    if not workspace_id:\n        print("[ERROR] Please set the environment variable DASHSCOPE_WORKSPACE_ID (your Model Studio workspace ID)")\n        print("  Example: export DASHSCOPE_WORKSPACE_ID='your_workspace_id_here'")\n        return\n\n    target_language, audio_enabled = get_user_config()\n    print("\\nConfiguration complete:")\n    print(f"  - Target language: {target_language}")\n    if not audio_enabled:\n        print("  - Output mode: Text only")\n\n    client = LiveTranslateClient(api_key=api_key, workspace_id=workspace_id, target_language=target_language, audio_enabled=audio_enabled)\n\n    # Callback fired as translated text arrives — stream it to stdout, character by character\n    def on_translation_text(text):\n        print(text, end="", flush=True)\n\n    try:\n        print("Connecting to the translation service...")\n        await client.connect()\n\n        # Launch the audio playback thread (only does real work when audio output is enabled)\n        client.start_audio_player()\n\n        print("\\n" + "-" * 60)\n        print("Connected! Please speak into the microphone.")\n        print("The program will translate your speech in real time and play the results. Press Ctrl+C to exit.")\n        print("-" * 60 + "\\n")\n\n        # Run two coroutines concurrently: server-message handling + microphone audio upload\n        message_handler = asyncio.create_task(client.handle_server_messages(on_translation_text))\n        tasks = [message_handler]\n        # Microphone capture is the translation input source — required regardless of output mode\n        microphone_streamer = asyncio.create_task(client.start_microphone_streaming())\n        tasks.append(microphone_streamer)\n\n        await asyncio.gather(*tasks)\n\n    except KeyboardInterrupt:\n        print("\\n\\nUser interrupted, exiting...")\n    except Exception as e:\n        print(f"\\nFatal error occurred: {e}")\n    finally:\n        print("\\nCleaning up resources...")\n        await client.close()\n        print("Program exited.")\n\n\nif __name__ == "__main__":\n    asyncio.run(main())\n
\n

Future Directions

\n

Simultaneous interpretation is far from its final destination. Guided by “hear it more fully, convey it more faithfully, use it more widely,” we are looking toward the following directions:

\n\n

Citation

\n

Feel free to cite the following article if you find Qwen3.8-LiveTranslate helpful:

\n
@misc{qwen38livetranslateblog,\n    title = {Qwen3.8-LiveTranslate: Names the speaker. Carries the meaning.},\n    url = {https://qwen.ai/blog?id=qwen3.8-livetranslate},\n    author = {Qwen Team},\n    month = {September},\n    year = {2026}\n}\n
\n", "date_published": "2026-09-18T09:30:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen3.8-livetranslate", "id": "urn:aggr:item:669698b16090a1acd5a5520f91ba0759d19ba779", "image": "https://img.alicdn.com/imgextra/i1/O1CN01WiBmdpYSfUD29R4a_!!6000000000594-2-tps-1060-636.png", "summary": "API Demo Simultaneous interpretation is not only about translating fast — it must also hear clearly and translate accurately. Qwen3.8-LiveTranslate rebuilds real-time simultaneous interpretation with an Interleave architecture, improving…", "tags": [ "ai", "release" ], "title": "Qwen3.8-LiveTranslate: Names the speaker. Carries the meaning.", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-09-18-qwen3-8-livetranslate-names-the-speaker-carries-the-meaning/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"Qwen3.8-Omni-Flash:

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Introduction

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Today, we are launching Qwen3.8-Omni-Flash, our next-generation native omnimodal model. Its core objective is to strengthen agent capabilities in real-world productivity scenarios, advancing omnimodal models from “understanding omnimodal content” to “planning tasks, calling tools, and completing creative work.” Building on general agentic capabilities in coding, text-based knowledge work, and GUI operation, Qwen3.8-Omni-Flash further extends agentic applications centered on audio and video, delivering strong results across workflows such as video editing, music video creation, film production and commentary, audio-visual summarization, and real-time conversations.

\n

\"Qwen3.8-Omni-Flash

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Figure 1. Qwen3.8-Omni-Flash and its applications in production.

\n\n

Qwen3.8-Omni-Flash supports a 1M-token context window while maintaining text performance comparable to a text-only model of the same size and delivering significant improvements in omnimodal capabilities. Across 29 evaluations1, its average score improves by more than 25% over Qwen3.5-Omni-Plus; the API price per hour of audio input decreases by more than 98%, and the price per hour of audio-visual input decreases by more than 93%2. For audio-visual agents, coding, and long-horizon tasks, the model improves by 36.5 points on WildClawBench-MM and 22.3 points on AgenticVBench, while scoring a strong 69.6 on UniClawBench. Its core capabilities also improve significantly in long-form audio and audio-visual understanding, audio-visual reasoning, audio-visual captioning, and multi-speaker recognition. For example, it gains 8.3 points on LongAudioSpan and 9.6 points on OmniVideoBench; its OmniCap-IF CSR and ISR improve by 8.5 and 14.1 points, respectively; and its AliMeeting DER and cpWER decrease from 88.11 / 89.61 to 3.35 / 17.18. By scaling data, context, and agentic environments, Qwen3.8-Omni-Flash achieves audio-visual performance close to Gemini 3.8 Flash and overall audio performance that exceeds Gemini 3.8 Flash. These advances also mean that audio and video are evolving from perceptual inputs into core media through which agents understand their environment, reason, and execute tasks.

\n

1. The scope includes audio reasoning benchmarks: AliMeeting-test, AISHELL-4, MagicData-RAMC, MLC-SLM (en), WenetSpeech (Net | Meeting), FLEURS-60 ASR, FLEURS-60 S2TT, SpotSoundBench, MMAU, MMAR, MMSU, MuchoMusic-RUL, HumMusQA, MusTBench, Audio-MultiChallenge, WildSpeech, and VoiceBench; audio-visual reasoning benchmarks: DailyOmni, WorldSense, AVUT, JointAVBench, OmniCloze, OmniCap-IF, QIVD, OmniVideoBench, and StreamingBench; and audio-visual agent benchmarks: WildClawBench-MM, UniClawBench, and OmniGAIA.
\n2. Pricing methodology: hourly audio or audio-visual input prices are estimated as 30 times the input cost of two minutes of source material; audio-visual input uses 720p at 1 fps. Gemini 3.8 Flash uses media_resolution=high, Seed 2.0 Lite uses max_frame_tokens=384, and all other API parameters use their default values. Text input and output prices are in CNY per 1M tokens; Gemini and Muse prices are converted from USD at an exchange rate of 1 USD = 6.7191 CNY.

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\"Qwen3.8-Omni-Flash

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Audio and video are important media for bringing agents into real-world productivity scenarios, but they also introduce a new set of system-level challenges. Long-form audio and video are costly to store, transmit, and process across multiple rounds of inference; existing agent harness frameworks lack native support for these modalities; and workflows that connect omnimodal understanding with end-to-end task execution are still at an early stage. Addressing these challenges requires models, harness tools, and runtime environments to evolve together.

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To address these challenges, we use Qwen3.8-Omni-Flash to explore how to connect source understanding, task planning, tool execution, and result delivery into a complete pipeline. It supports end-to-end, long-horizon workflows such as video editing, translation, film commentary, and content creation, advancing Omni from audio-visual understanding toward autonomous action and task completion.

\n

To this end, we have further expanded Qwen-MM-Plugins with on-demand perception, tool use, and workflow execution for long-form audio and video. We have also open-sourced Qwen-Live Harness as a native runtime for continuous, real-time omnimodal interaction. Together, they address long-horizon workflows and real-time interaction while continuing to expand the capabilities of omnimodal agents alongside the model.

\n

PLUGIN Qwen-MM-Plugins The gateway to audio-visual productivity—connecting multimodal understanding, content creation, and agent harnesses. Get started → HARNESS Qwen-Live Harness The gateway to real-time interaction—connecting audio-visual conversations, task delegation, memory, and context management. Get started →

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Long-Form Audio-Visual Understanding

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Qwen3.8-Omni-Flash brings a major upgrade to long-form audio-visual understanding—from controllable descriptions and agentic evidence gathering, to understanding meetings and advancing follow-up tasks, and finally to producing video-centered deep research reports. It does not merely process longer content, but finds relevant evidence more precisely, reasons more deeply, and acts more efficiently.

\n

Controllable Audio-Visual Captioning

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There is no single answer to how a video should be described. Content creation prioritizes narrative, footage retrieval focuses on specific segments, and asset management depends on structure. Different applications need different video descriptions. In Qwen3.8-Omni-Flash, we have upgraded video captioning from answering "what the model saw" to understanding "what the user wants to know." Users can freely specify the subject, time range, level of detail, and output format. For the same video, the model can provide an overview, locate key segments, or analyze character actions, camera shots, lighting, and sound in depth, producing structured results as needed. You define what to look at, how closely to look, and how to present it.

\n

Agentic Long-Form Audio-Visual Understanding

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For videos lasting several hours, conventional approaches require the model to process the entire recording from beginning to end, even when the answer appears in only a few minutes of footage. The native Qwen3.8-Omni-Flash agent starts from the question, independently decides what to watch and listen to, and locates key information through multiple rounds of coarse-to-fine evidence gathering. Without processing every frame, it can focus limited compute and token budgets on the relevant segments, enabling more efficient long-form video understanding. On OmniVideoBench, Agentic Understanding improves accuracy from 63.4 to 67.8 while reducing token consumption from 145,736 to 79,117, a reduction of approximately 45.7%. The table below compares the accuracy and token consumption of Static Understanding and Agentic Understanding on OmniVideoBench:

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Note: Agentic mode preserves context across turns.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Static UnderstandingAgentic Understanding
Accuracy (↑)63.467.8
Tokens per query (↓)145,73679,117
\n

Long Meetings: From Minutes to Action

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Multi-participant meetings are among the most complex audio-visual understanding scenarios: speakers take turns and overlap, while identities, references, and discussion topics continuously change. Qwen3.8-Omni-Flash jointly recognizes speakers across audio and video and natively supports up to one hour of audio-visual input. It can perform speaker segmentation, content transcription, and identity alignment end to end. Given a complete meeting video and a request, the model can map participant relationships, generate meeting minutes, identify action items, and analyze project risks, using visual information to resolve references and entity ambiguity in the audio. Combined with agents and tool use, it can also send emails, organize tasks, and even begin coding in response to meeting requirements—moving from understanding a meeting to acting on it.

\n

Conducting Deep Research with Audio and Video

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When users watch a video with a specific question in mind, the answer often extends beyond the video itself. Qwen3.8-Omni-Flash combines the user’s needs with the video content to identify questions worth deeper investigation, organize the key material, and search multimodal sources across the web—including images, videos, and documents. It then produces a video-centered, richly illustrated research report that helps users understand the content and solve practical problems. For example, when a user encounters color fringing around a Photoshop hair cutout, the model can break down the tutorial steps, study the principles behind Multiply and Screen blend modes, compare alternative edge-repair techniques, and explain which approach best fits the user’s situation.

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Qwen3.8-Omni-Flash Conducting Deep Research with Audio and Video Demo

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Audio-Visual Production and Editing

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Qwen3.8-Omni-Flash is taking audio-visual agents into a new stage: from understanding sounds and images to independently planning, calling tools, and delivering finished videos, bringing omnimodal intelligence into professional audio-visual content production workflows.

\n

Music2MV

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For music video (MV) creation, Qwen3.8-Omni-Flash can understand the structure, rhythm, mood, vocals, and instrumental changes of a user-provided song in fine detail, informing the design of characters, scenes, and shots. It can also output line-level lyrics with timestamps to align singing, subtitles, and visuals. Combined with creative tools such as Qwen-MM-Plugins, the model supports the complete workflow from music understanding and creative planning to final quality review, demonstrating strong audio-visual understanding, reasoning, and creation capabilities.

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Workflow Next

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Final Result 1 Next

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Final Result 2 Next

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Final Result 3 Next

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Short Drama Translation

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Traditional video translation often requires repeatedly switching between transcription, translation, dubbing, and editing platforms. This complicates API calls and workflow coordination and makes it difficult to maintain consistency across character voices, dialogue duration, and visual pacing. With an agent built on Qwen3.8-Omni-Flash, users can describe their needs in a single sentence to perform speaker-aware dialogue recognition, conversational translation, character voice cloning and dubbing, audio remixing, and final quality review. These otherwise fragmented localization steps become a complete workflow, enabling automated delivery of short dramas for international audiences.

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Workflow Next

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Final Result 1 Next

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Source

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Translated Result

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Final Result 2 Next

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Source

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Translated Result

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Final Result 3 Next

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Source

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Translated Result

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Long-Form Film Commentary

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Producing commentary videos for full-length films of two or three hours often requires repeatedly watching the film and reconstructing its plot, followed by shot selection, scriptwriting, voiceover, music, and editing. This is a complex and time-consuming process. With an agent built on Qwen3.8-Omni-Flash, users need only provide a film and describe their creative requirements in one sentence to perform long-form video understanding, key-plot extraction, commentary planning, voiceover and music production, editing, rendering, and final quality review. The agent can also intelligently interleave original dialogue with commentary, automatically adjusting speech rate and volume so that narration, original audio, background music, and visuals flow naturally together, creating a more authentic, immersive, and cinematic commentary video.

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Workflow Next

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Final Result 1 Next

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Final Result 2 Next

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From Using Models to Optimizing Models

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Real-world multimodal applications are often complex and cost-sensitive, requiring models to balance quality, latency, compute, and deployment costs.

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Customizing smaller models for specific scenarios is therefore an important path to deploying applications at scale. Yet traditional workflows involve data construction, problem diagnosis, multiple rounds of training, and evaluation, making them time-consuming and heavily dependent on human expertise. This time, we extend Qwen3.8-Omni-Flash into model development itself, exploring a new approach in which large models drive research and development while smaller models serve business needs.

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We gave Qwen3.8-Omni-Flash a task: improve Qwen2.5-Omni-3B's Sichuan dialect speech recognition within 12 hours and deliver a usable model. It independently selected the WenetSpeech-Chuan evaluation set, fixed the evaluation criteria, and established a baseline. It then listened directly to audio samples, diagnosed problems using the recognition results, and constructed targeted training data. Across four consecutive rounds of experiments, the agent created 3,413 training examples, adjusted its approach based on evaluation feedback, retained effective improvements, and rolled back unsuccessful attempts. Qwen2.5-Omni-3B's character error rate on the same evaluation set ultimately fell from 25.79% to 15.30%, a relative reduction of approximately 40.7%.

\n

This experiment demonstrates another possibility for model evolution: general-purpose multimodal models understand data, plan experiments, and drive iteration, while smaller models acquire specialized capabilities for specific applications. Agents can go beyond using models to help solve practical business problems.

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Audio-Visual Information Compression

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Audio and video carry rich information, but their linear, unstructured form makes retrieval and reuse difficult. Qwen3.8-Omni-Flash understands content across sound, visuals, and timelines, using an agentic workflow to extract information, reorganize its structure, and verify results. It transforms the core knowledge and practical experience in long videos into denser information assets that are easier to consume and reuse.

\n

Video2Note

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To turn video knowledge into structured resources, we have open-sourced Video2Note in Qwen-MM-Plugins. Drawing on Qwen3.8-Omni-Flash's joint understanding of speech, visuals, and procedures, it automatically organizes knowledge, breaks down key steps, selects representative frames, and generates PDF notes with corresponding text and images. Automated review and iterative correction further condense hours of video into clear, readable documents that are easy to revisit.

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Workflow Next

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Final Result 1 Next

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Example Video

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Final PDF

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Final Result 2 Next

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Example Video

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Final PDF

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Final Result 3 Next

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Example Video

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Final PDF

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Final Result 4 Next

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Example Video

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Final PDF

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Omni Skill Creator

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Videos record not only "how to do something," but also the practical expertise accumulated by specialists. With this in mind, we introduce Omni Skill Creator as a new open-source capability in Qwen-MM-Plugins. It can extract standard operating procedures (SOPs) from demonstrations to perform reusable automated work, or learn tool usage, decision criteria, and key insights from expert instruction. A single demonstration becomes an agent skill that has been verified and evaluated, enabling reusable, shareable skills built from omnimodal content.

\n

Real-Time Audio-Visual Interaction

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Qwen3.8-Omni-Flash is designed for deep understanding and creation with complete audio-visual content. For continuous, low-latency interaction, we further introduce Qwen3.8-Omni-Flash-Realtime. It perceives and responds while receiving live audio-visual streams, and uses real-time context to call tools and execute tasks, taking omnimodal capabilities from “understanding a piece of content” to “participating in an interaction.”

\n

Real-Time Speaking Practice

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Spoken language has no standard input. Accents, vowel and consonant substitutions, and tonal deviations can cause word-for-word transcription to diverge from the intended meaning. Qwen3.8-Omni-Flash-Realtime jointly models pronunciation and semantics, understands nonstandard expressions affected by accents, aligns them with the correct words, and generates standard-pronunciation demonstrations in real time. Across multiple practice turns, the model updates its judgment with new audio, correcting errors that affect understanding while preserving natural rhythm, tone, and emotion.

\n

Omnimodal Spatial Audio Perception

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In real-world spaces, sound provides another coordinate axis beyond vision. Qwen3.8-Omni-Flash-Realtime combines spatial sound with visual information to continuously determine the direction and distance of sound sources while perceiving obstacles, navigable areas, and changes in the scene, making it the first omnimodal model capable of locating targets by sound.

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For instructions such as “come over here” or “go see what is making that sound,” the model can isolate voices and target sounds from environmental noise, ground their meaning in the surrounding space, and call tools to perform localization, search, path planning, and navigation—from hearing a target to reaching it.

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External Knowledge for Audio-Visual Interaction

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Real-time interaction requires not only low latency, but also the ability to load knowledge and behavior dynamically for each application. Qwen3.8-Omni-Flash-Realtime supports injecting identity settings, expression styles, business knowledge, and interaction rules through Skills, while tool use extends these capabilities into task execution.

\n

In scenarios such as customer service, the model can load brand language and service procedures in real time, understand the user's speech, visuals, and context, generate responses that follow business requirements, and execute actions. The same real-time model can therefore take on different knowledge, roles, and ways of acting.

\n

Benchmark Results

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Omni

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Qwen3.8-Omni-FlashQwen3.5-Omni-PlusGemini 3.8 FlashSeed 2.0 LiteMuse Spark 1.2
Agentic Omni Intelligence
WildClawBench-MM Multimodal tool use71.034.558.941.9--
UniClawBench Multimodal tool use69.667.169.061.2--
AgenticVBench Multimodal tool use36.814.545.010.0--
OmniGAIA Web Search74.057.278.664.4--
General Audio-Visual Capabilities
DailyOmni Audio-Visual Understanding85.185.184.081.479.6
WorldSense Audio-Visual Understanding68.563.969.667.365.0
AVUT Audio-Visual Understanding86.685.988.081.582.4
JoinAVBench Audio-Visual Understanding75.974.170.470.671.8
OmniVideoBench Audio-Visual Reasoning63.453.865.258.562.2
Video-MME-v2 Audio-Visual Reasoning65.047.971.064.9--
LVOmniBench Long Video Reasoning63.353.270.7----
OmniCloze Audio-Visual Caption63.264.260.956.365.3
OmniCap-IF Audio-Visual CaptionCSR 80.6 ISR 28.2CSR 72.1 ISR 14.1CSR 81.9 ISR 28.3CSR 74.6 ISR 18.1CSR 77.9 ISR 26.8
QIVD Audio-Visual Interaction69.665.669.162.062.0
StreamingBench Audio-Visual Interaction80.857.179.977.277.8
General Audio Capabilities
AliMeeting Test Multi-Speaker ASR (DER | cpWER, ↓)3.4 | 17.288.1 | 89.672.6 | 53.175.1 | 76.193.7 | 92.7
AISHELL-4 Multi-Speaker ASR (DER | cpWER, ↓)2.8 | 11.2100.0 | 100.066.4 | 56.964.8 | 64.291.3 | 86.0
MagicData-RAMC Multi-Speaker ASR (DER | cpWER, ↓)5.7 | 14.198.4 | 97.167.9 | 33.843.4 | 35.182.1 | 75.3
MLC-SLM (en) Multi-Speaker ASR (DER | cpWER, ↓)4.0 | 14.268.6 | 63.960.8 | 26.640.4 | 45.574.3 | 52.9
WenetSpeech (Net) ASR (WER, ↓)4.83.714.24.368.2
WenetSpeech (Meeting) ASR (WER, ↓)4.64.816.74.742.6
FLEURS-ASR Multilingual ASR (WER, ↓)9.37.27.932.123.6
FLEURS-S2TT Multilingual S2TT (BLEU)31.832.233.024.828.8
SpotSoundBench Audio Grounding67.264.239.759.616.9
MMAU Audio Understanding81.881.976.977.263.5
MMAR Audio Understanding79.879.878.577.767.3
MMSU Audio Understanding82.183.083.380.259.9
LongAudioSpan Long Audio ReasoningAccuracy 82.7 Rubric 71.8 Chain 48.2Accuracy 74.4 Rubric 49.8 Chain 45.1Accuracy 79.3 Rubric 65.5 Chain 64.6----
MuchoMusic-RUL Music Understanding72.671.653.761.740.1
HumMusQA Music Understanding75.875.571.266.063.3
MusTBench Music Understanding50.649.140.344.029.4
Audio MultiChallenge Audio Interaction71.557.671.963.457.9
WildSpeech Audio Interaction74.375.776.474.573.4
VoiceBench Audio Interaction91.692.992.384.179.8
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1. Evaluation harnesses for Agentic Omni Intelligence: WildClawBench-MM and AgenticVBench use Claude Code, UniClawBench uses OpenClaw, and OmniGAIA uses no harness. For WildClawBench-MM, we evaluate only the multimodal tasks in WildClawBench that involve images, video, or audio.
\n2. FLEURS: ASR and S2TT evaluation results both cover the following 60 languages: Chinese (Mandarin), English, Cantonese, Arabic, German, French, Spanish, Portuguese, Indonesian, Italian, Korean, Russian, Thai, Vietnamese, Japanese, Turkish, Hindi, Malay, Dutch, Urdu, Norwegian, Swedish, Danish, Hebrew, Finnish, Polish, Icelandic, Czech, Filipino, Persian, Greek, Afrikaans, Asturian, Belarusian, Bulgarian, Bengali, Bosnian, Catalan, Cebuano, Estonian, Galician, Gujarati, Croatian, Hungarian, Javanese, Kazakh, Kannada, Kyrgyz, Latvian, Macedonian, Malayalam, Marathi, Punjabi, Romanian, Slovak, Slovenian, Swahili, Tajik, Azerbaijani, and Ukrainian.
\n3. Empty cells (--): scores are not yet available or are not applicable.

\n

Agentic Omni Understanding

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Key information in long audio and video recordings is often scattered across different segments, while complex questions require multiple steps of reasoning across sound and images. Agentic Omni Understanding enables the model to start from the question, plan its approach, call tools, and progressively locate and verify evidence. By focusing computation on relevant content, it improves the accuracy and efficiency of long-form audio-visual understanding. To evaluate this capability, we compare Qwen3.8-Omni-Flash and Gemini 3.8 Flash on OmniVideoBench, Video-MME-v2, and LVOmniBench under two settings: Static, where the model directly interprets the input, and an agent mode using Qwen Code. This comparison shows how introducing agent workflows affects each model’s performance.

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Qwen3.8-Omni-Flash (Static)Qwen3.8-Omni-Flash (Qwen Code)Gemini 3.8 Flash (Static)Gemini 3.8 Flash (Qwen Code)
OmniVideoBench Audio-Visual Reasoning63.467.865.270.1
Video-MME-v2 Audio-Visual Reasoning65.071.371.072.7
LVOmniBench Long Video Reasoning63.373.670.770.7
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Text

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Qwen3.8-Omni-FlashQwen3.8- FlashQwen3.8- 27BQwen3.7- PlusDeepSeek-V4-Flash-0731Claude-Opus-4.6 (Max)
Coding and Agent
DeepSWE 1.1 Long-horizon software engineering57.858.742.216.554.4--
SWE-bench Pro Long-horizon software engineering63.362.561.755.856.053.4
SWE-bench Multilingual Multilingual software engineering80.581.073.875.8--77.5
NL2Repo-Bench Repo-level code generation48.948.142.341.154.247.6
CoWorkBench Long-horizon office work75.373.970.765.145.168.2
General Text Capabilities
IFBench Instruction following81.581.379.579.179.262.5
GPQA Diamond Scientific reasoning91.091.789.290.390.891.3
HLE Multidisciplinary reasoning36.535.930.834.733.840.0
LiveCodeBench v6 Competitive coding92.691.990.389.690.688.8
\n

1. DeepSWE 1.1: evaluated with the Claude Code and mini-SWE-agent harnesses, temp=1.0, top_p=0.95, 256K context window. We report the highest score across the two harnesses; notably, Qwen3.8-Flash-Next performs best on mini-SWE-agent.
\n2. SWE-bench Pro: except for Claude-Opus-4.6 (Max), for which we report the officially published score, all models are evaluated with the Claude Code harness, temp=1.0, top_p=0.95, 256K context window. Problematic tasks were corrected and all baseline models were re-evaluated on the refined benchmark.
\n3. SWE-bench Multilingual: evaluated with the mini-SWE-agent harness, temp=1.0, top_p=0.95, 256K context window.
\n4. NL2Repo-Bench: evaluated with the Claude Code harness. To prevent reward hacking, we disable Bash commands that attempt to access the specific repository, such as pip download, pip install and git clone.
\n5. CoWorkBench: an in-house cowork benchmark for evaluating long-horizon office and productivity agent tasks across computer science, finance, law, medical and other productivity domains.
\n6. HLE: judged by GPT-4o.
\n7. Empty cells (--): scores are not yet available or are not applicable.

\n

Vision

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Qwen3.8-Omni-FlashQwen3.8- FlashQwen3.8- 27BQwen3.7- PlusClaude-Opus-4.6 (Max)
Agentic Vision Intelligence
ClawEval-MM Multimodal tool usePass@3 60.4 Average 61.9Pass@3 64.4 Average 60.4Pass@3 57.4 Average 56.9Pass@3 57.4 Average 60.1Pass@3 52.5 Average 54.7
AndroidWorld Mobile use87.184.581.981.062.0
Vision2Web Visual web development62.964.062.942.1--
General Vision Capabilities
ERQA Embodied intelligence71.072.365.569.840.8
LVBench Long video understanding76.976.672.476.263.0
RealWorldQA Real-world perception87.788.585.986.973.9
MathVision Visual math problem solvingWithout CI 91.8 With CI 96.2Without CI 90.6 With CI 95.7Without CI 90.0 With CI 94.6Without CI 90.3 With CI 88.4Without CI 65.5 With CI --
CharXiv (RQ) Scientific chart analysisWithout CI 83.5 With CI 91.4Without CI 84.6 With CI 90.6Without CI 83.7 With CI 90.2Without CI 85.8 With CI 85.9Without CI 66.0 With CI --
\n

1. ClawEval-MM: scores are reported as "pass@3 / average score". Pass@3 measures the percentage passed in at least one of three trials, and the average score is the mean score across the three trials.
\n2. RecreationBench: an in-house long-horizon application-recreation benchmark for evaluating hybrid-agent abilities spanning five platforms — desktop (Ubuntu, macOS, Windows), mobile (Android) and web.
\n3. OSWorld 2.0: scores are reported as "binary / partial". The binary score is the percentage of tasks that receive the full task reward, while the partial score aggregates the partial rewards obtained across all tasks.
\n4. Vision2Web: scores are reported as the average over the frontend, webpage and website categories, using the Claude Code harness and judged by gpt-5.4-2026-03-05.
\n5. MathVision, CharXiv (RQ): scores are reported as "without CI / with CI". A small number of incorrect ground-truth annotations in MathVision were corrected after manual verification. Our model's score is evaluated using a fixed prompt, e.g. "Please reason step by step, and put your final answer within \\boxed{}." For other models, we report the higher score between runs with and without the \\boxed{} formatting.
\n6. Empty cells (--) indicate scores not yet available or not applicable.

\n

Throughput & Latency

\n

The following results show the observed throughput and latency of the Qwen3.8-Omni-Flash-Realtime API under different input conditions, reflecting the performance users experience in production environments.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Input ScenarioText Output TPS (Tokens/s)Time to First Token (ms)Time to First Audio Packet (ms)Audio Generation RTF
Realtime API Performance
Audio 6s84.87591.26978.360.1538
Audio 12s83.18604.80982.740.1537
Audio 20s81.06617.981026.390.1538
Audio-Visual 6s84.89837.961214.730.1524
Audio-Visual 12s84.33911.851268.070.1527
Audio-Visual 20s83.00981.011350.490.1528
\n

Supported Languages

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
CapabilityLanguagesChinese Dialects
Speech Recognition74 languages: Afrikaans, Arabic, Asturian, Azerbaijani, Basque, Belarusian, Bengali, Bosnian, Bulgarian, Cantonese, Catalan, Cebuano, Chinese, Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Interlingua, Italian, Japanese, Javanese, Kannada, Kazakh, Korean, Kyrgyz, Lingala, Latvian, Lithuanian, Macedonian, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Norwegian Bokmål, Norwegian Nynorsk, Oriya, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tajiki, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uyghur, and Vietnamese39 dialects: Northeastern Mandarin, Guizhou dialect, Guangdong Cantonese, Henan dialect, Hong Kong Cantonese, Shanghainese, Shaanxi dialect, Tianjin dialect, Taiwanese Mandarin, Yunnan dialect, Anhui dialect, Fujian dialect, Gansu dialect, Guangdong Mandarin, Hubei dialect, Hunan dialect, Jiangxi dialect, Shandong dialect, Shanxi dialect, Sichuanese, Guangxi dialect, Hainan dialect, Chongqing dialect, Changsha dialect, Hangzhou dialect, Hefei dialect, Yinchuan dialect, Zhengzhou dialect, Shenyang dialect, Wenzhou dialect, Wuhan dialect, Kunming dialect, Taiyuan dialect, Nanchang dialect, Jinan dialect, Lanzhou dialect, Nanjing dialect, Hakka, and Southern Min
Speech Generation29 languages: Chinese, English, German, Italian, Portuguese, Spanish, Japanese, Korean, French, Russian, Thai, Indonesian, Arabic, Vietnamese, Turkish, Finnish, Polish, Hindi, Dutch, Czech, Urdu, Tagalog, Swedish, Danish, Hebrew, Icelandic, Malay, Norwegian, and Persian7 dialects: Sichuanese, Beijing dialect, Tianjin dialect, Nanjing dialect, Shaanxi dialect, Cantonese, and Southern Min
\n

Getting Started with Qwen3.8-Omni-Flash

\n

API Usage

\n

Qwen3.8-Omni-Flash officially supports reasoning_effort to adjust reasoning depth and control costs:

\n\n

In addition, preserve_thinking is enabled by default across all scenarios for the best out-of-the-box experience.

\n

Qwen3.8-Omni-Flash supports industry-standard protocols, including OpenAI-compatible Chat Completions and Responses APIs. Examples follow:

\n
"""\nEnvironment variables:\n  DASHSCOPE_API_KEY: Your API Key from https://platform.qianwenai.com/home\n  DASHSCOPE_BASE_URL: (optional) Base URL for compatible-mode API.\n    - Beijing: https://dashscope.aliyuncs.com/compatible-mode/v1\n    - Singapore: https://dashscope-intl.aliyuncs.com/compatible-mode/v1\n"""\nfrom openai import OpenAI\nimport os\n\napi_key = os.environ.get("DASHSCOPE_API_KEY")\nif not api_key:\n    raise ValueError(\n        "DASHSCOPE_API_KEY is required. "\n        "Set it via: export DASHSCOPE_API_KEY='your-api-key'"\n    )\n\nclient = OpenAI(\n    api_key=api_key,\n    base_url=os.environ.get(\n        "DASHSCOPE_BASE_URL",\n        "https://dashscope-intl.aliyuncs.com/compatible-mode/v1",\n    ),\n)\n\nmessages=[\n    {\n        "role": "user",\n        "content": [\n            {\n                "type": "image_url",\n                "image_url": {\n                    "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241022/emyrja/dog_and_girl.jpeg"\n                },\n            },\n            {\n                "type": "input_audio",\n                "input_audio": {\n                    "data": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20250211/tixcef/cherry.wav",\n                    "format": "wav"\n                },\n            },\n            {"type": "text", "text": "Please describe the image and tell me what is being said in the audio."},\n        ],\n    },\n]\n\ncompletion = client.chat.completions.create(\n    model="qwen3.8-omni-flash",\n    messages=messages,\n    extra_body={\n        "enable_thinking": True,\n        # "preserve_thinking": True,\n    },\n    reasoning_effort="xhigh",  # supported levels are xhigh, medium, and low\n    stream=True,\n)\n\nreasoning_content = ""\nanswer_content = ""\nis_answering = False\nprint("\\n" + "=" * 20 + "Reasoning" + "=" * 20 + "\\n")\n\nfor chunk in completion:\n    if not chunk.choices:\n        print("\\nUsage:")\n        print(chunk.usage)\n        continue\n\n    delta = chunk.choices[0].delta\n\n    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:\n        if not is_answering:\n            print(delta.reasoning_content, end="", flush=True)\n        reasoning_content += delta.reasoning_content\n\n    if hasattr(delta, "content") and delta.content:\n        if not is_answering:\n            print("\\n" + "=" * 20 + "Answer" + "=" * 20 + "\\n")\n            is_answering = True\n        print(delta.content, end="", flush=True)\n        answer_content += delta.content\n
\n

Best Practices for Controllable Audio-Visual Captioning

\n

Content organization and level of detail: Organize video descriptions, OCR extraction, audio descriptions, and speech transcripts into sections covering visuals, speech, music, sound effects, and ambient sounds. Preserve the original text, speaker identities, and corresponding time ranges to make retrieval and verification easier. Specify a target length in the prompt to control detail, for example, Describe the video in approximately 2000–3000 words., and adjust it to the video’s duration and information density.

\n

Prompt Example

\n
Provide a detailed description of the video.\n\nMake sure your description covers every one of the following dimensions:\n\nVisual\n- Subjects and characters: appearance, clothing, gender/age cues, identity, distinctive features\n- Actions and events in chronological order, and how the scene evolves over time\n- Setting and background: location, environment, time of day\n- Spatial layout and relations between subjects/objects; counts and quantities\n- On-screen text: captions, titles, subtitles, logos, UI — exact content and appearance\n- Visual style: colors, lighting, camera shots, angles, and camera movement\n\nAudio\n- Speech: the exact spoken content, transcribed verbatim\n- Speakers: who is speaking (mapped to the on-screen person or voice-over), with accent, tone, gender/age cues\n- Speaking state: prosody, emotion, volume, and speaking style\n- Music: presence, genre/mood, and lyrics if any\n- Sound effects and ambient/background sounds\n- Non-speech vocalizations: laughter, crying, applause, etc.\n\nAudio-visual correspondence\n- Which speech or sound aligns with which on-screen person or visual event\n- The timing of each event, expressed with timestamps\n\nIt should explicitly include three sections:\n\n1. A structured chronological storyline of **every noticeable audio and visual details**\n2. A structured list of all visible text. For each text element, include start timestamp, end timestamp, the exact text content, the appearance characteristics. If no text appears, explicitly state so.\n3. A structured speech-to-text transcription, include speaker (corresponding to the character or voice-over in Section 1, including their accent and tone), exact spoken content, start timestamp, end timestamp, and speaking state (prosody, emotion, and style). If no speech appears, explicitly state so.\n\nAside from these three required sections, you are free to organize any additional content in any way you find helpful. This additional content can include global information about the entire video or localized information about specific moments. You may choose the topic of this extra content freely.\n\nRules:\n\n- Add as much descriptive detail as possible.\n- Do not use Markdown bold formatting.\n- Carefully look at frames and listen to the audio, making sure no detail is overlooked.\n\nOutput Format:\n```\n## Storyline\n\n<xx:xx.xxx> - <xx:xx.xxx>\n<an unstructured long paragraph in natural language describing what happened during this period, blending both audio and video details.>\n\n<xx:xx.xxx> - <xx:xx.xxx>\n<an unstructured long paragraph in natural language describing what happened during this period, blending both audio and video details.>\n\n<xx:xx.xxx> - <xx:xx.xxx>\n<an unstructured long paragraph in natural language describing what happened during this period, blending both audio and video details.>\n\n...\n\n## Visible Text\n\n<xx:xx.xxx> - <xx:xx.xxx>\n“<element>”: <appearance>\n“<element>”: <appearance>\n\n<xx:xx.xxx> - <xx:xx.xxx>\n“<element>”: <appearance>\n“<element>”: <appearance>\n“<element>”: <appearance>\n\n<xx:xx.xxx> - <xx:xx.xxx>\n“<element>”: <appearance>\n\n...\n\n## Speakers and Transcript\n\nSpeaker profiles:\n<speaker> - <profile>\n<speaker> - <profile>\n<speaker> - <profile>\n...\n\n<xx:xx.xxx> - <xx:xx.xxx>\nSpeaker: <speaker>\nState: <description>\nContent: “<content>”\n\n<xx:xx.xxx> - <xx:xx.xxx>\nSpeaker: <speaker>\nState: <description>\nContent: “<content>”\n\n<xx:xx.xxx> - <xx:xx.xxx>\nSpeaker: <speaker>\nState: <description>\nContent: “<content>”\n\n...\n\n## <another section>\n\n<paragraphs>\n\n## <another section>\n\n<paragraphs>\n\n...\n```\n
\n

Structured output and schema constraints: Include task instructions and a complete JSON Schema in the prompt, specifying field meanings, types, required fields, and constraints to support programmatic parsing and downstream use.

\n

Prompt Example

\n
Describe the audio and visual content in detail in English, organized into scenes and events, following the JSON Schema below.\nAll timestamps must be relative to the beginning of the video. End times must not precede start times or exceed the video duration. Each event must fall within the time range of its parent scene.\nInclude only information directly supported by the audio or video. Do not guess or invent details. Do not infer causality merely because a sound and an action occur at the same time.\nReturn only valid JSON, without Markdown fences or commentary.\n\nJSON Schema:\n{\n  "$defs": {\n    "Event": {\n      "additionalProperties": false,\n      "properties": {\n        "time_range": {\n          "$ref": "#/$defs/TimeRange",\n          "description": "Time range of the event"\n        },\n        "participants": {\n          "description": "People, animals, or objects involved, named by observable features; use consistent names for the same participant",\n          "items": {\n            "type": "string"\n          },\n          "title": "Participants",\n          "type": "array"\n        },\n        "action": {\n          "description": "Specific actions, interactions, and observable outcomes",\n          "title": "Action",\n          "type": "string"\n        },\n        "sounds": {\n          "description": "Sounds heard during the event; use an empty list if none are discernible",\n          "items": {\n            "type": "string"\n          },\n          "title": "Sounds",\n          "type": "array"\n        }\n      },\n      "required": [\n        "time_range",\n        "participants",\n        "action",\n        "sounds"\n      ],\n      "title": "Event",\n      "type": "object"\n    },\n    "Scene": {\n      "additionalProperties": false,\n      "properties": {\n        "time_range": {\n          "$ref": "#/$defs/TimeRange",\n          "description": "Time range of the scene"\n        },\n        "setting": {\n          "description": "Environment, spatial layout, and main visual features",\n          "title": "Setting",\n          "type": "string"\n        },\n        "events": {\n          "description": "Events in chronological order; use an empty list if there are none",\n          "items": {\n            "$ref": "#/$defs/Event"\n          },\n          "title": "Events",\n          "type": "array"\n        }\n      },\n      "required": [\n        "time_range",\n        "setting",\n        "events"\n      ],\n      "title": "Scene",\n      "type": "object"\n    },\n    "TimeRange": {\n      "additionalProperties": false,\n      "properties": {\n        "start_seconds": {\n          "description": "Start time in seconds relative to the beginning of the video",\n          "minimum": 0,\n          "title": "Start Seconds",\n          "type": "number"\n        },\n        "end_seconds": {\n          "description": "End time in seconds; must not precede the start time",\n          "minimum": 0,\n          "title": "End Seconds",\n          "type": "number"\n        }\n      },\n      "required": [\n        "start_seconds",\n        "end_seconds"\n      ],\n      "title": "TimeRange",\n      "type": "object"\n    }\n  },\n  "additionalProperties": false,\n  "properties": {\n    "summary": {\n      "description": "An overview of the main content of the video",\n      "title": "Summary",\n      "type": "string"\n    },\n    "scenes": {\n      "description": "Scenes in chronological order; group continuous footage with a consistent setting into one scene",\n      "items": {\n        "$ref": "#/$defs/Scene"\n      },\n      "title": "Scenes",\n      "type": "array"\n    }\n  },\n  "required": [\n    "summary",\n    "scenes"\n  ],\n  "title": "CaptionResult",\n  "type": "object"\n}\n
\n

Installing Qwen-MM-Plugins in an Agent Harness

\n

Qwen-MM-Plugins is a multimodal plugin suite for agent harnesses. It gives agents the ability to understand images, audio, video, and documents, as well as maintain memory for long-form video and create content. It supports agent harnesses including Codex, Claude Code, Qwen Code, Gemini CLI, Qoder, CodeBuddy, and OpenClaw. We also welcome contributions from community developers.

\n

You can enter the following request directly in your usual office agent:

\n
Help me install the core, api, and omni-related plugins from https://github.com/QwenLM/Qwen-MM-Plugins.\n
\n

\"Installing

\n

Alternatively, install from the command line:

\n
# Run the official guided installer:\ncurl -fsSL https://raw.githubusercontent.com/QwenLM/Qwen-MM-Plugins/main/install.sh | bash\n
\n

In the menu, select:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
PluginCapability
coreRead images, video frames, PDFs, Office documents, code, data, and 3D files
apiImage understanding, OCR, object localization, audio-visual transcription, speaker diarization, event analysis, and image segmentation
omni-chatcutCreate MVs, long-form film commentary, and video speech translations
omni-video2noteTurn video tutorials into PDF notes with key screenshots
omni-skill-creatorTurn instructional videos, screen recordings, or operation demonstrations into reusable Agent Skill.md files
omni-memoryBuild memory for people, dialogue, sounds, and events in long-form videos
\n

After installation, restart the agent harness or create a new task.

\n
@song.mp3 Generate a complete MV based on the song's rhythm and content.\n@short_drama.mp4 Translate the video into English while preserving the original speakers' voice characteristics where possible.\n@movie.mp4 Create a film commentary video with Chinese narration and subtitles.\n\n@weekly_report_sop.mp4 Turn this screen recording of writing a weekly report into a Skill.md file.\n@tutorial.mp4 Turn the tutorial into PDF notes with key screenshots, timestamps, and step-by-step instructions.\n@documentary.mp4 Build audio-visual memory that records people, dialogue, sounds, and important events.\n
\n

Getting Started with Qwen3.8-Omni-Flash-Realtime

\n

API Usage

\n

Qwen3.8-Omni-Flash-Realtime supports connections over WebSocket and WebRTC. Running the basic examples below opens the camera and microphone for a real-time audio-visual conversation. We recommend using headphones.

\n
# Run pip install websocket-client pyaudio dashscope opencv-python -U to install dependencies\n"""\nEnvironment variables:\n  DASHSCOPE_API_KEY: Your API Key from https://platform.qianwenai.com/home\n  DASHSCOPE_BASE_URL: (optional) Base URL for realtime API.\n    - Beijing: wss://dashscope.aliyuncs.com/api-ws/v1/realtime\n    - Singapore: wss://dashscope-intl.aliyuncs.com/api-ws/v1/realtime\n"""\nimport os\nimport base64\nimport time\nimport pyaudio\nimport cv2\nfrom dashscope.audio.qwen_omni import MultiModality, AudioFormat,OmniRealtimeCallback,OmniRealtimeConversation\nimport dashscope\n\nurl = f'wss://dashscope-intl.aliyuncs.com/api-ws/v1/realtime'\ndashscope.api_key = os.getenv('DASHSCOPE_API_KEY')\n# Determine the voice\nvoice = 'Tina'\n# Determine the model\nmodel = 'qwen3.8-omni-flash-realtime'\n# Determine the model role\ninstructions = "You are Qwen-Omni, a helpful assistant."\nvideo_fps = 1\nvideo_size = (1280, 720)\nclass SimpleCallback(OmniRealtimeCallback):\n    def __init__(self, pya):\n        self.pya = pya\n        self.out = None\n    def on_open(self):\n        # Initialize audio output stream\n        self.out = self.pya.open(\n            format=pyaudio.paInt16,\n            channels=1,\n            rate=24000,\n            output=True\n        )\n    def on_event(self, response):\n        if response['type'] == 'response.audio.delta':\n            # Play audio\n            self.out.write(base64.b64decode(response['delta']))\n        elif response['type'] == 'conversation.item.input_audio_transcription.delta':\n            # Streaming preview: text is the confirmed prefix, stash is the confirmed suffix\n            preview = response.get('text', '') + response.get('stash', '')\n            print(f"\\r[User] {preview}", end='', flush=True)\n        elif response['type'] == 'conversation.item.input_audio_transcription.completed':\n            # Transcription completed, print the final text and a new line\n            print(f"\\r[User] {response['transcript']}")\n        elif response['type'] == 'response.audio_transcript.done':\n            # Print the assistant's response text\n            print(f"[LLM] {response['transcript']}")\n\n# 1. Initialize audio device\npya = pyaudio.PyAudio()\n# 2. Create callback function and session\ncallback = SimpleCallback(pya)\nconv = OmniRealtimeConversation(model=model, callback=callback, url=url)\n# 3. Establish connection and configure session\nconv.connect()\nconv.update_session(output_modalities=[MultiModality.AUDIO, MultiModality.TEXT], voice=voice, instructions=instructions)\n# 4. Initialize audio input stream\nmic = pya.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True)\ncamera = cv2.VideoCapture(0)\nnext_frame_at = 0.0\n# 5. Main loop to process audio and video input\ntry:\n    if not camera.isOpened():\n        raise RuntimeError("Cannot open camera 0.")\n    camera.set(cv2.CAP_PROP_FRAME_WIDTH, video_size[0])\n    camera.set(cv2.CAP_PROP_FRAME_HEIGHT, video_size[1])\n    camera.set(cv2.CAP_PROP_BUFFERSIZE, 1)\n    print(f"Conversation started with camera ({video_fps} fps, {video_size[0]}x{video_size[1]}), speak into the microphone (Ctrl+C to exit)...")\n    while True:\n        audio_data = mic.read(3200, exception_on_overflow=False)\n        conv.append_audio(base64.b64encode(audio_data).decode())\n        success, frame = camera.read()\n        if not success:\n            raise RuntimeError("Cannot read a camera frame.")\n        if time.monotonic() >= next_frame_at:\n            frame = cv2.resize(frame, video_size)\n            success, image = cv2.imencode('.jpg', frame, [cv2.IMWRITE_JPEG_QUALITY, 80])\n            if not success:\n                raise RuntimeError("Cannot encode a camera frame.")\n            conv.append_video(base64.b64encode(image).decode())\n            next_frame_at = time.monotonic() + 1 / video_fps\n        time.sleep(0.01)\nexcept KeyboardInterrupt:\n    pass\nfinally:\n    # Clean up resources\n    camera.release()\n    conv.close()\n    mic.close()\n    if callback.out:\n        callback.out.close()\n    pya.terminate()\n    print("\\nConversation ended")\n
\n

Qwen-Live Harness

\n

Qwen-Live Harness is a comprehensive open-source harness designed around the Qwen3.8-Omni-Flash-Realtime API. It can be installed with a single command and integrated into mainstream agent workflows. It supports task delegation, proactive interaction, long-term memory, and context management, and welcomes contributions from the community.

\n

\"Qwen-Live

\n

Figure 2. Qwen-Live Harness Interaction Framework.

\n

Install and get started:

\n
npm install -g qwen-live-harness\nqwen-live-harness init\nqwen-live-harness\n
\n

Citation

\n
@misc{qwen38omniflash,\n    title = {Qwen3.8-Omni-Flash: Omni Senses. Agentic Delivery.},\n    url = {https://qwen.ai/blog?id=qwen3.8-omni-flash},\n    author = {{Qwen Team}},\n    month = {September},\n    year = {2026}\n}\n
\n", "date_published": "2026-09-18T07:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen3.8-omni-flash", "id": "urn:aggr:item:68d832950a358a8a4fd8195eae6db1c7f1f65974", "image": "https://img.alicdn.com/imgextra/i1/O1CN01OvSn2RXYZRD29R4a_!!6000000000892-2-tps-1060-636.png", "summary": "Introduction Today, we are launching Qwen3.8-Omni-Flash, our next-generation native omnimodal model. Its core objective is to strengthen agent capabilities in real-world productivity scenarios, advancing omnimodal models from…", "tags": [ "ai", "release" ], "title": "Qwen3.8-Omni-Flash: Omni Senses. Agentic Delivery.", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-09-18-qwen3-8-omni-flash-omni-senses-agentic-delivery/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"\"

\n

PAPER GITHUB PROJECT

\n

Agent benchmarks over the past few years have mostly followed one pattern. A goal is handed to the model, and the model tries to reach it within a bounded number of turns, whether that means finding the treasure in a maze, producing a report, or fixing a piece of code. Performance is then scored on the quality of the deliverable or on how much of the task got done, and evaluations of this kind usually come with a well-defined natural stopping point.

\n

Most long-horizon tasks in the real world have no such natural stopping point. Weather forecasting, stock trading and running a business are all like that. An online store is never “finished operating” one morning. Inventory piled up last month, a price negotiated the day before yesterday and yesterday’s promotion all move today’s sales, and the agent has to keep adjusting its strategy inside a shifting market to hit a long-run profit goal.

\n

To measure long-horizon operating ability, we partnered with Taobao & Tmall Group to release E-Commerce Bench, which evaluates how well a model runs online stores as a merchant in a realistic market. The model makes a long series of business decisions under limited time and limited capital, and has to absorb whatever the market throws at it.

\n

¥100,000 in Capital, 365 Days of Continuous Operation

\n

The agent starts with ¥100,000 and may run several stores at once. Across a full simulated year it does what a real seller does every day, researching categories, haggling with suppliers, pricing and listing goods, watching the promotion calendar, and managing inventory and cash flow. The environment never reveals where the demand ceiling sits or what the true cost floor is, so both have to be discovered. At the end of the year we score the run along several dimensions, covering profit, cash-flow management, supplier negotiation, fraud avoidance, operational efficiency, execution, and learning over the horizon.

\n

\"E-Commerce

\n

Figure 1: The four-layer architecture. The agent loop layer manages turns and context, the tool layer provides the e-commerce toolbox, the deterministic environment layer models customer demand and supplier behavior, and the data layer is driven by real Taobao & Tmall platform data

\n

To reproduce a real e-commerce platform, we built in the following:

\n\n

The Deterministic Negotiation Kernel and the Demand Model

\n

If market demand and supplier behavior were both random, the gaps between models would drown in noise, leaving the benchmark neither discriminative nor reproducible.

\n

The customer side therefore does no sampling at all. How many units a product sells on a given day is computed from a set of named factors, the category’s base demand, the price response, weekends, promotions, seasonality, that day’s events, store reputation, and the market’s demand ceiling.

\n

\"Demand

\n

Figure 2: The four price-elasticity curves, and how one SKU's sales volume is decided by the chain of factors

\n

On the supplier side, a deterministic kernel produces the quotes and the concession policy, and a model renders them into natural dialogue. Letting an LLM play the supplier directly breaks two things. First, the same negotiation strategy draws inconsistent quotes, so one agent meets different prices on two runs. Second, an LLM can be hacked, and a dishonest agent can talk or jailbreak the price below the cost floor, which turns the benchmark into a jailbreaking contest. Every supplier is therefore split into two layers:

\n\n

The texture of multi-round haggling survives, and the sampling noise does not.

\n

\"Negotiation

\n

Figure 3: Inside a session the two sides' offers converge toward the middle. Across a year of restocking, some agents forget the low price they already won while others keep pushing the settled price down

\n

Year-End Total Assets Are Only the Tip of the Iceberg: Seven Capability Axes

\n

We ran five complete episodes for each of 18 models. From the same ¥100,000 start, the outcomes come out orders of magnitude apart. GPT-5.6 Sol finished at ¥1.43M, 14.31 times its stake, while Qwen3.5-Plus averaged just ¥1,100 left. The strongest open-weight entry is Qwen3.8-Max-Preview at 4.16 times the stake, though the top four overall are all closed-source. Strong models are not guaranteed to win either. Ten of the 90 episodes ended in bankruptcy, and in two GPT-5.5 episodes the cash chain snapped in January after it stocked up too heavily, at a point when not a single yuan of sales revenue had settled.

\n

\"Year-end

\n

Figure 4: Year-end total assets across 5 episodes for each of the 18 models, log axis. Red crosses mark bankrupt episodes, and the right-hand column gives the asset multiple and the bankruptcy rate

\n

The year-end number by itself says nothing about how the year was spent. The 365-day curves fall into roughly three shapes. The leaders climb steadily from January onward. Bankrupt episodes drop and never come back, the earliest hitting bottom in January. The models in the middle and lower half hug the ¥100,000 line all year, a full year of work that ends where it began.

\n

\"Full-year

\n

Figure 5: Total assets across the year for all 18 models, ordered by year-end mean. Thin lines are single episodes, the black line is the episode mean, and the dashed red line is the ¥100,000 stake

\n

The road into bankruptcy is almost always the same one. Fill the warehouse in January, then never sell your way out of it.

\n

\"One

\n

Figure 6: One bankruptcy case day by day. Within a month buying far outran selling, storage costs climbed, and the cash chain snapped

\n

Earning more is not the same as operating well. So alongside year-end total assets we score six further dimensions independently, negotiation quality, fraud avoidance, cash flow and solvency, operational efficiency, operations execution, and learning over the horizon. Drawn as a radar over seven axes, the shapes come out visibly uneven. Taking one model per vendor family, six of the seven fall below the 18-model median on at least one axis.

\n

\"Seven-axis

\n

Figure 7: Capability profiles for one model from each of seven vendor families. The dashed polygon is the 18-model median, and not one profile fills it

\n

Read axis by axis, several findings turn out to be more interesting than the asset total itself.

\n

Negotiation quality: no model pushes a price down to the supplier’s cost floor. We score bargaining from 0 to 1, where 0.5 means never countering at all and simply taking whatever the supplier opens with. Claude Opus 4.7 reaches 0.811 and genuinely bargains a lot off the table, while Kimi K2.6 manages only 0.596, barely better than accepting the opening quote. One more result is worth noting. A single model’s negotiation performance varies little across the six supplier behavior styles it can meet, while the spread between models is four times as large.

\n

Fraud avoidance: this is where the field diverges most. The share of procurement spend that reaches fraudulent suppliers differs more than 160-fold between the extremes, 0.12% for Claude Opus 4.7 against 20.11% for Qwen3.5-Plus. For reference, 152 of the 576 suppliers on the roster are fraudulent, or 26.4%, so a buyer that screens nobody and spreads its procurement budget blindly across every supplier would land right around that 26.4% line. All 18 models come in below it, which means every one of them screens out at least some of the fraudsters. GPT-5.6 Sol, the biggest earner, ranks only 16th here, and its year survived anyway. The real dividing line is not which suppliers a model chooses to talk to. In every model’s outreach, fraudulent suppliers make up roughly the same 26.4%. What differs is whether a conversation turns into an order. Of the fraudulent suppliers Claude Opus 4.7 contacted, only 4.0% ended up receiving an order, against 31.7% for GPT-5.6 Sol.

\n

\"Procurement

\n

Figure 8: The share of procurement spend reaching fraudulent suppliers. The red line is the 26.4% no-screening reference, and the right-hand panel shows how that money gets divided among the five scams

\n

Operational efficiency: dividing a year of profit by the tool calls that produced it, Fable5 returns ¥479 per call, above the ¥363 of GPT-5.6 Sol, the leader on total assets, and on 59.9% fewer calls. Shipping, skipping to the next day, withdrawing cash and listing inventory take 71.8% of all calls, while the supplier conversations that can actually change procurement cost take only 6.0%, and price revisions 0.66%. The bulk of the budget goes into actions that leave the cost structure untouched.

\n

\"Profit

\n

Figure 9: Profit per tool call, and how six models spread their calls over eight groups of actions

\n

After a Full Year, Almost No Model Buys Any Cheaper

\n

Learning over a long horizon is hard to measure, largely because it is hard to say what “progress” ought to look like. This environment happens to come with a ready-made yardstick. A seller kernel never settles below its own reservation price, so the best price an agent has already won is an upper bound on that supplier’s cost floor, and the bound only tightens. For the same supplier and the same product, whether the next purchase settles above or below the best price already won can be judged without any extra annotation.

\n

We express each repeat purchase price as its position inside the bargaining range, then compare it against a null hypothesis that reshuffles the prices the agent already won. That gives AnchorRatio, where 1.0 means the model’s ordering of its own prices is indistinguishable from a random ordering. Across 8,647 repeat purchases, only 2 of the 18 models fall below 1.0, the median is 1.369, and 15 models miss the null by more than two standard deviations in the more expensive direction. Supplier choice drifts the wrong way as well, with fraudulent suppliers taking a larger share of late-year deals than early-year ones for 17 of the models. Over a full year, the models do not get better at buying. Qwen3.8-Max-Preview is the only one of the 18 showing a clear sign of long-horizon learning, at an AnchorRatio of 0.834, which means it does walk repeat purchase prices lower as the year goes on.

\n

Closing Thoughts

\n

One question we went back and forth on while building E-Commerce Bench was whether to let an LLM play the supplier directly. The answer ended up being no. Once the counterparty is also a probabilistic model, the game between agent and supplier becomes two sampling processes stacked on top of each other, the same agent strategy produces entirely different outcomes on two runs, and the evaluation loses its comparability. Our approach freezes every economic decision into a deterministic kernel and leaves only a layer of LLM rendering to put the numbers into human words. The feel of multi-round bargaining is preserved, and the random noise stays outside the score. The idea should carry beyond e-commerce negotiation. Using a pseudo-random deterministic kernel to hold reproducibility looks like a general recipe for long-horizon benchmarks.

\n

The other lesson is that a single metric hides what a model is really like. Not one of the 18 models holds up across all seven axes. Each of the top three carries a weakness that falls outside the top ten, and the model at the bottom is not necessarily the worst on every dimension. Looking only at year-end total assets, you would think GPT-5.6 Sol leads across the board, yet it ranks 16th on fraud avoidance. Failure modes in long-horizon tasks are too scattered, and without pulling them apart there is no way to know where a model is actually weak.

\n

Finally, E-Commerce Bench is far from saturated. On negotiation, fraud avoidance and learning over the horizon alike, the best model on each dimension still sits a clear distance from a perfect score. The best results across the seven dimensions are spread over six different models, frontier models still have substantial room to improve, and the benchmark has plenty of headroom left for measuring business capability.

\n

Citation

\n
@misc{fan2026ecommercebenchevaluatingllm,\n  title={E-Commerce Bench: Evaluating LLM Agents on Long-Horizon Autonomous Business Operation},\n  author={Wei Fan and Xinjie Shen and Xudong Guo and Jianhong Tu and Yang Su and Yinger Zhang and Lianghao Deng and Fengyu Wang and Baohua Dong and Yangqiu Song and Dayiheng Liu},\n  year={2026},\n  eprint={2608.30730},\n  archivePrefix={arXiv},\n  primaryClass={cs.LG},\n  url={https://arxiv.org/abs/2608.30730},\n}\n
\n", "date_published": "2026-09-03T02:00:00+00:00", "external_url": "https://qwen.ai/blog?id=e-commerce-bench", "id": "urn:aggr:item:4837fa895985f79ed9acc3d60d46a912c7eb01a4", "image": "https://img.alicdn.com/imgextra/i1/O1CN01QlThshCJ7uE3E9bs_!!6000000000358-2-tps-1590-954.png", "summary": "PAPER GITHUB PROJECT Agent benchmarks over the past few years have mostly followed one pattern. A goal is handed to the model, and the model tries to reach it within a bounded number of turns, whether that means finding the treasure in a…", "tags": [ "ai", "open-source" ], "title": "E-Commerce Bench: Long-Horizon Operations, Multi-Dimensional Evaluation", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-09-03-e-commerce-bench-long-horizon-operations-multi-dimensional/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"Qwen-Drive-1.0

\n

Paper GitHub Hugging Face Model Scope

\n

Introduction

\n

We introduce Qwen-Drive-1.0, the first vision-language foundation model for autonomous driving that unifies 3D perception and visual question answering at the pretraining stage and further extends to motion planning, while keeping the pretrained VLM architecture entirely untouched. Built on the natively multimodal Qwen3.5-4B, it attaches two external modules. A BEV perception head serves as an explicit, inspectable 3D probe, jointly performing 3D object detection, semantic occupancy prediction, and BEV map segmentation, and a Planning Expert generates future ego trajectories through flow matching. Through staged training, we substantially boost the autonomous driving capability of a general-purpose VLM and validate it on 3D perception, driving visual question answering, and motion planning tasks, forming a unified driving vision-language model that offers a new-generation VLM base for driving-scenario adaptation.

\n

Highlights

\n\n

Model Architecture

\n

\"Unified

\n

Qwen-Drive-1.0 builds on the natively multimodal Qwen3.5-4B. A shared vision encoder and VLM process single-view and multi-view driving images, temporal image sequences, and general images. Without changing the pretrained architecture, two external modules read from this shared pathway. The BEV perception head builds a BEV representation from multi-view single-frame inputs and jointly performs 3D object detection, semantic occupancy prediction, and BEV map segmentation. It acts as an explicit, inspectable 3D probe, and its losses provide an additional gradient path into the shared visual pathway during joint training. The Planning Expert is a diffusion transformer tailored to VLM representations. It generates 5-second ego trajectories through flow matching, with an optional textual planning reason as condition. Perception, question answering, and planning thus reside in one pretrained VLM.

\n

Performance

\n

\"Benchmark

\n

Driving Scene Understanding without Losing General Capability

\n

Qwen-Drive-1.0-SFT reaches a driving QA average of 69.43, leading both general-purpose VLMs and driving or embodied specialists, and demonstrating its strong driving scene understanding capability.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
InternVL3.5-8B-Inst.LLaVA-OV2-8BQwen3.5-4BCosmos-Reason2-8BCosmos3-nanoMiMo-Embodied-7BAlpamayo-1.5-10BQwen-Drive-1.0-SFT
Driving VQA
LingoQA46.4041.2070.4059.6065.0072.0064.0077.80
Ego3D RMSE ↓23.0124.9713.1712.6222.419.8525.317.78
VLAD54.4758.7165.3856.3757.7350.339.1366.52
SURDS32.8038.6052.9519.5439.7243.063.1066.13
WaymoQA Safety54.4749.6562.4657.6856.9366.5442.6170.70
WaymoQA All58.0955.2367.1057.9358.3669.5644.3774.47
CoC All--0.572.581.724.01--3.4441.26
IH47.5054.0059.0056.002.0061.003.0071.00
Knowledge, Reasoning, and Recognition
MMBench80.0382.6687.0782.8279.57--7.5185.53
MMStar64.1364.9375.3365.2766.6722.4026.1375.87
MMMU62.0054.6773.4459.1160.89--27.4472.67
MMMU-Pro Std46.4236.3064.8636.0746.3627.4015.6162.72
MMMU-Pro Vis42.2525.9561.2743.5340.7528.0913.4759.71
CharXiv41.7040.1065.1042.5042.1057.501.5064.40
OCRBench83.2079.3086.9087.0085.2078.803.2086.40
RealWorldQA66.9371.7676.3467.4569.6728.5046.9378.95
SimpleVQA40.7736.6847.8445.2544.99----46.12
CountQA20.9422.5835.8622.3223.6322.644.7131.74
Spatial Understanding and Grounding
EmbSpatial74.2078.4375.9977.6177.8845.0520.5878.85
ERQA42.0042.2546.2543.2541.2539.7527.5048.50
RefSpatial----54.5151.81--2.17--50.78
Omni3D----47.4032.8532.26----45.79
ODinW13----40.7840.1935.87----45.87
\n

* All benchmarks use the same high-certainty decoding settings (greedy=false, top-p=0.001, top-k=1, temperature=0.01, repetition_penalty=1.0, presence_penalty=0.0) to more directly reflect model capability.
\n* LingoQA is scored with Qwen-Plus as the judge instead of the official LingoJudge, which we found to score leniently and inconsistently across scenarios. Under the official LingoJudge protocol, Qwen-Drive-1.0-SFT obtains a LingoScore of 79.4.
\n* The same judge scores every method on each benchmark.
\n* -- marks an invalid or unparsable response.

\n

Motion Planning on Open-Loop and Closed-Loop

\n

Qwen-Drive-1.0 demonstrates outstanding performance in motion planning, both in open-loop and closed-loop settings. The training is entirely based on publicly available data, comprising a total of 2.83 million samples. Due to differences in annotation styles across various datasets, we unified the trajectory format to achieve stable 5-second trajectory predictions at 10 Hz.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
AutoVLASpanVLAMindVLA-U1Alpamayo-1.5SimWAMILQwen-Drive-1.0-SFTQwen-Drive-1.0-RL
Open-loop
WOD-E2E (RFS val/test ↑)--/7.56--8.20/7.87----7.95/7.788.45/7.91
WOD-E2E (ADE 5s val/test ↓)--/2.96--2.28/2.66----2.31/2.651.27/2.67
PAI-AV (Avg. ADE 3s ↓)------0.350.410.370.42
PAI-AV (Avg. ADE 5s ↓)------1.05--1.071.11
Pseudo-closed-loop
NAVSIM (PDMS ↑)89.690.3----90.388.290.7
NAVSIM best-of-6 (PDMS ↑)----------89.391.4
Closed-loop
AlpaSim (at-fault score ↑)------0.450.300.270.37
\n

* AutoVLA and SimWAM train a separate model on each dataset.
\n* The SFT column reports Qwen-Drive-1.0-SFT conditioned on planning reasoning.
\n* IL denotes imitation learning.
\n* -- indicates that the method does not report a result on the corresponding benchmark.

\n

What’s Next

\n

We consider Qwen-Drive-1.0 an initial step towards a vision-language foundation model for autonomous driving. Specifically, we introduce a BEV perception head as an explicit, inspectable 3D probe and a Planning Expert that generates future ego trajectories through flow matching. Through staged training, we substantially boost the autonomous driving capability of a general-purpose VLM and validate this route on 3D perception, driving visual question answering, and motion planning, forming a unified driving vision-language model that offers a new-generation VLM base for driving-scenario adaptation. Still, the consistency between textual reasoning and the generated trajectory remains to be strengthened, which we leave as a focus of future work.

\n

Citation

\n
@misc{zhou2026qwendrive10initialstepvisionlanguage,\n      title={Qwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous Driving}, \n      author={Xin Zhou and Zongchuang Zhao and Zhibo Yang and Mingsheng Li and Humen Zhong and Shuai Bai and Du Chu and Ruizhe Chen and Zhaohai Li and Jun Tang and Qiuyue Wang and Mingkun Yang and Jiazhao Zhang and Dayiheng Liu and Dingkang Liang and Xiang Bai},\n      year={2026},\n      eprint={2609.00111},\n      archivePrefix={arXiv},\n      primaryClass={cs.CV},\n      url={https://arxiv.org/abs/2609.00111}, \n}\n
\n", "date_published": "2026-09-03T00:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen-drive-1.0", "id": "urn:aggr:item:996e6ba5f03d0c082e238a0166f4713483fe24fc", "image": "https://img.alicdn.com/imgextra/i2/O1CN01BEtEP74nVrF3E9bs_!!6000000002925-2-tps-1590-954.png", "summary": "Introduction We introduce Qwen-Drive-1.0, the first vision-language foundation model for autonomous driving that unifies 3D perception and visual question answering at the pretraining stage and further extends to motion planning, while…", "tags": [ "ai", "open-source" ], "title": "Qwen-Drive-1.0: An Initial Step towards a Vision-Language Foundation Model for Autonomous Driving", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-09-03-qwen-drive-1-0-an-initial-step-towards-a-vision-language/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"Qwen3.8-Flash-Next\"

\n

HUGGING FACE MODELSCOPE TECH REPORT FLASHQLA DISCORD

\n

Introduction

\n

In this release we are opening the weights of Qwen3.8-Flash-Next, a multimodal MoE model that also serves as an early preview of the architecture used in Qwen4. It plays the same role that Qwen3-Next played for Qwen3.5: the hybrid Gated DeltaNet + Gated Attention design introduced at that time has since been used across the Qwen3.5, Qwen3.6, Qwen3.7 and Qwen3.8 series. We are again releasing the architectural changes early, so that the community can examine them before the full Qwen4 model family is built on top of them.

\n

Qwen3.8-Flash-Next upgrades the model systematically along four aspects — attention, residual, embedding and optimization — improving model capability while further optimizing computational efficiency, model capacity and training stability:

\n\n

Qwen3.8-Flash-Next features a 125B-parameter main model, supplemented by an additional 51B N-gram embeddings, with 6B parameters activated per token. Compared with Qwen3.7-Plus, Qwen3.8-Flash-Next substantially reduces both training and inference cost — training takes only about 1/9 as much, yet it delivers superior capabilities in coding and office tasks.

\n

It natively supports 262,144 tokens of context and is extensible to 1,000,000 tokens with YaRN. For more technical details on the architecture, training methodology, and experimental analysis of Qwen3.8-Flash-Next, please refer to the technical report in our GitHub repository.

\n

Qwen3.8-Flash-Next weights are now available on Hugging Face and ModelScope. The production version, with 1M context by default and official built-in tools, is served as Qwen3.8-Flash on QwenCloud, priced at 0.15 USD per million input tokens and 0.47 USD per million output tokens.

\n

Performance

\n

Language

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Qwen3.8-Flash-NextQwen3.8-27BQwen3.7-PlusDeepSeek-V4-Flash-0731Claude-Opus-4.6 (Max)
# Params125B27B397B284B--
# Activated params6B27B17B13B--
# N-gram embedding params51B--------
Coding
Agentic coding DeepSWE 1.158.742.216.554.4--
Agentic coding SWE-bench Pro62.561.755.856.053.4
Multilingual software engineering SWE-bench Multilingual81.073.875.8--77.5
Repo-level code generation NL2Repo-Bench48.142.341.154.247.6
Agent
Long-horizon office work CoWorkBench73.970.765.145.168.2
Professional job tasks JobBench55.733.427.641.336.6
Frontier agentic tasks Agents' Last ExamPass@1 24.3 Score 51.2Pass@1 20.4 Score 42.9Pass@1 13.2 Score 33.6Pass@1 25.2 Score ----
Real-world tool use Toolathlon Verified (Pass@1)73.567.150.670.3--
General
Instruction following IFBench81.379.579.179.262.5
Scientific reasoning GPQA Diamond91.789.290.390.891.3
Multidisciplinary reasoning HLE35.930.834.733.840.0
Competitive coding LiveCodeBench v691.990.389.690.688.8
\n

1. DeepSWE 1.1: evaluated with the Claude Code and mini-SWE-agent harnesses, temp=1.0, top_p=0.95, 256K context window. We report the highest score across the two harnesses; notably, Qwen3.8-Flash-Next performs best on mini-SWE-agent.
\n2. SWE-bench Pro: except for Claude-Opus-4.6 (Max), for which we report the officially published score, all models are evaluated with the Claude Code harness, temp=1.0, top_p=0.95, 256K context window. Problematic tasks were corrected and all baseline models were re-evaluated on the refined benchmark.
\n3. SWE-bench Multilingual: evaluated with the mini-SWE-agent harness, temp=1.0, top_p=0.95, 256K context window.
\n4. NL2Repo-Bench: evaluated with the Claude Code harness. To prevent reward hacking, we disable Bash commands that attempt to access the specific repository, such as pip download, pip install and git clone.
\n5. CoWorkBench: an in-house cowork benchmark for evaluating long-horizon office and productivity agent tasks across computer science, finance, law, medical and other productivity domains.
\n6. HLE: judged by GPT-4o.
\n7. The best result in each row is shown in bold.
\n8. Empty cells (--): scores are not yet available or are not applicable.

\n

Vision Language

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Qwen3.8-Flash-NextQwen3.8-27BQwen3.7-PlusClaude-Opus-4.6 (Max)
Agentic Multimodal Intelligence
Multimodal tool use ClawEval-MMPass@3 64.4 Average 60.4Pass@3 57.4 Average 56.9Pass@3 57.4 Average 60.1Pass@3 52.5 Average 54.7
Application recreation RecreationBench49.947.130.2--
Mobile use AndroidWorld84.581.981.062.0
Computer use OSWorld 2.0Binary 19.4 Partial 52.3Binary 19.4 Partial 48.0Binary 2.8 Partial 21.5--
Visual web development Vision2Web64.062.942.1--
General Multimodal Intelligence
Embodied intelligence ERQA72.365.569.840.8
Long video understanding LVBench76.672.476.263.0
Real-world perception RealWorldQA88.585.986.973.9
Visual math problem solving MathVisionWithout CI 90.6 With CI 95.7Without CI 90.0 With CI 94.6Without CI 90.3 With CI 88.7Without CI 65.5
Scientific chart analysis CharXiv (RQ)Without CI 84.6 With CI 90.6Without CI 83.7 With CI 90.2Without CI 85.8 With CI 85.9Without CI 66.0
\n

1. ClawEval-MM: scores are reported as "pass@3 / average score". Pass@3 measures the percentage passed in at least one of three trials, and the average score is the mean score across the three trials.
\n2. RecreationBench: an in-house long-horizon application-recreation benchmark for evaluating hybrid-agent abilities spanning five platforms — desktop (Ubuntu, macOS, Windows), mobile (Android) and web.
\n3. OSWorld 2.0: scores are reported as "binary / partial". The binary score is the percentage of tasks that receive the full task reward, while the partial score aggregates the partial rewards obtained across all tasks.
\n4. Vision2Web: scores are reported as the average over the frontend, webpage and website categories, using the Claude Code harness and judged by gpt-5.4-2026-03-05.
\n5. MathVision, CharXiv (RQ): scores are reported as "without CI / with CI". A small number of incorrect ground-truth annotations in MathVision were corrected after manual verification. Our model's score is evaluated using a fixed prompt, e.g. "Please reason step by step, and put your final answer within \\boxed{}." For other models, we report the higher score between runs with and without the \\boxed{} formatting.
\n6. The best result in each row is shown in bold.
\n7. Empty cells (--) indicate scores not yet available or not applicable.

\n

Model Architecture

\n

\"Qwen3.8-Flash-Next

\n

Attention: GDN + QSA for Efficient Memory and Precise Retrieval

\n

Traditional Full Attention provides direct access to all previous tokens, but as the context grows longer, both computation and KV Cache memory-access costs increase substantially.

\n

Following the architecture design introduced in Qwen3.5, Qwen3.8-Flash-Next adopts a GDN [1] + Attention Hybrid architecture: three out of every four layers use Gated DeltaNet (GDN) to continuously compress historical information into a fixed-size state, while the remaining layer uses global Attention for precise retrieval of information across the full context.

\n

For global Attention, we further introduce Qwen Sparse Attention (QSA). Sparse Attention reduces long-sequence computation by attending only to important context. However, existing approaches such as DSA [2] still rely on a token-level indexer to identify important positions; as the context grows, the indexer itself becomes a non-negligible source of computation.

\n

QSA further compresses this process: a lightweight indexer first aggregates the sequence into micro-blocks, estimates context importance at the block level, and then selects the most relevant regions for Attention. This reduces not only the cost of Attention itself, but also the indexing overhead required to identify important context. Compared with approaches that share indices across layers [3], QSA performs sequence compression independently within each layer, reducing its dependence on cross-layer Attention similarity and making it particularly well suited to Hybrid architectures where GDN and Attention layers are interleaved.

\n

\"Overview

\n

Put simply: GDN efficiently “remembers,” while QSA precisely “retrieves.”

\n

At 1M tokens, QSA’s Attention Kernel achieves up to 7.6× and 4.9× speedups in Prefill and Decode, respectively. In an experimental setup representative of online serving scenarios with high cache reuse (a 90% Prefix Cache hit rate), Qwen3.8-Flash-Next achieves 8.6× the Prefill throughput of Qwen3.7-Plus at a 1M-token context length.

\n

\"Relative

\n

Gated Residual: More Paths for Information Flow

\n

In a traditional Transformer, all layers continuously read from and write to the same Residual Stream. As the network becomes deeper, early features are repeatedly mixed with later information, making important signals more likely to be gradually diluted.

\n

Gated Residual (GR) can be viewed as a combination of two ideas: it follows Hyper-Connection [4] in widening the residual stream into multiple branches, while incorporating the element-wise dynamic gating of GatedNorm [5] into the residual read. The original single residual stream is expanded into four parallel branches, allowing the model to dynamically determine how much information to read from each branch and how much to write back to each branch based on the current content.

\n

This can be conceptualized as expanding a single information channel into multiple parallel pathways: some branches handle local information flow, while others preserve early information directly deep into the network layers. Empirical analysis also reveals that one of these branches naturally emerges as a long-range pathway connecting the first Attention layer to most of the middle and subsequent layers.

\n

GR also further simplifies Hyper-Connection. Once the read and write operations are expressive enough, additional branch mixing yields no significant benefits and can thus be directly removed, thereby reducing memory access overhead and sources of instability. The Gate also effectively suppresses activation outliers and improves training stability. In addition, the Residual State supports FP8 storage, further reducing memory-access overhead.

\n

N-gram Embedding: Expanding Model Capacity at Low Cost

\n

Inspired by Per-Layer Embedding in Gemma 3n and works such as DeepSeek Engram [6], we further introduce N-gram Embedding to scale model capacity beyond the parameters of the Transformer backbone.

\n

A standard Embedding performs a lookup based on a single token. N-gram Embedding instead performs lookups using the local context formed by the current token and several preceding tokens, providing additional representations for common phrases and local patterns.

\n

Its key advantage is that it can add a large number of parameters with almost no additional computation per token.

\n

Qwen3.8-Flash-Next introduces an additional 51B N-gram Embedding parameters. Because lookup locations can be determined in advance, these parameters can be stored in Host Memory and asynchronously prefetched in parallel with model computation, without permanently occupying GPU memory.

\n

The final model uses only a single N-gram Embedding layer near the beginning of the network, effectively adding a large-scale “local-pattern memory” at relatively low additional cost.

\n

Optimization: Co-designing Architecture and Optimization

\n

Qwen3.8-Flash-Next is trained with the Muon Optimizer [7], with further improvements around three key aspects of applying Muon to large-scale model training: orthogonalization accuracy, parameter assignment between Muon and AdamW, and splitting fused parameter matrices.

\n

For parameters that genuinely act as two-dimensional linear maps, such as the main weights in Attention, GDN, and MoE Experts, we use Muon. Embeddings, the MoE Router, and the low-rank parameters in GR continue to use AdamW. For QKV, SwiGLU, and GDN projections that are fused in the implementation, we first split them according to the independent linear transformations they represent, and then perform orthogonalization separately.

\n

For the new architecture and Optimizer, we refit the Scaling Law. The results show that the model can stably use larger Learning Rates and Batch Sizes, further improving convergence efficiency and large-scale parallel training throughput.

\n

We also find that Batch Size Warmup, a common practice in large-scale model training, is no longer necessary: gradually increasing from a small Batch to the target Batch does not improve the final result, but instead requires 18.8% more optimizer steps. In the final training Recipe, we therefore start directly with the target Batch Size.

\n

Other Architecture Optimizations

\n

The remaining components follow the design established in Qwen3-Next and refined through the Qwen3.5–Qwen3.8 series.

\n\n

Base Model Performance

\n

We compare Qwen3.8-Flash-Next-Base with the base models of Qwen3.8-27B and Qwen3.7-Plus.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Qwen3.8-Flash-Next-BaseQwen3.8-27B-BaseQwen3.7-Plus-Base
# Params125B27B397B
# Activated params6B27B17B
# N-gram embedding params51B----
General tasks
MMLU90.3687.5190.43
MMLU-Redux90.6887.2691.47
MMLU-Pro73.2368.6070.90
SuperGPQA51.3644.8648.42
BBH90.8789.5689.41
Math & STEM tasks
GPQA51.4245.0151.52
GSM8K93.2993.1892.95
MATH72.7860.5474.38
Coding tasks
EvalPlus78.7676.0578.06
MultiPL-E79.0974.5081.68
SWEBench-Pretrain50.9941.6649.24
Multilingual tasks
MGSM89.3386.3785.42
MMMLU84.8679.7484.53
INCLUDE78.4074.3778.90
\n

1. The best result in each row is shown in bold.
\n2. Empty cells (--): scores are not yet available or not applicable.

\n

With 6B activated parameters, Qwen3.8-Flash-Next-Base achieves the best result on 8 of the 14 benchmarks, including MMLU-Pro, SuperGPQA, BBH, GSM8K, EvalPlus, SWEBench-Pretrain, MGSM and MMMLU, and remains close to Qwen3.7-Plus-Base on MMLU, MMLU-Redux, GPQA, MATH and MultiPL-E. The 51B N-gram embedding parameters are deterministically addressed and do not enter the per-token matrix-multiplication budget.

\n

Develop with Qwen3.8-Flash-Next

\n

Qwen3.8-Flash-Next is available as an open-weight model on HuggingFace and ModelScope, with official managed APIs on QwenCloud. Designed to balance capability, latency, and cost, it is well suited for high-volume applications, tool-driven workflows, and coding & coworking assistants. In the following, you can explore how to call the QwenCloud API and integrate Qwen3.8-Flash-Next into agentic systems and coding assistants.

\n

API Usage

\n

Qwen3.8-Flash-Next is available via API:

\n

QwenCloud

\n

On QwenCloud, the model is served under the name qwen3.8-flash. QwenCloud supports industry-standard protocols, including OpenAI-compatible Chat Completions and Responses APIs, alongside an Anthropic-compatible interface.

\n
"""\nEnvironment variables:\n  DASHSCOPE_API_KEY: Your API Key from https://home.qwencloud.com/\n  DASHSCOPE_BASE_URL: (optional) Base URL for compatible-mode API.\n    - Beijing: https://dashscope.aliyuncs.com/compatible-mode/v1\n    - Singapore: https://dashscope-intl.aliyuncs.com/compatible-mode/v1\n    - US (Virginia): https://dashscope-us.aliyuncs.com/compatible-mode/v1\n"""\nfrom openai import OpenAI\nimport os\n\napi_key = os.environ.get("DASHSCOPE_API_KEY")\nif not api_key:\n    raise ValueError(\n        "DASHSCOPE_API_KEY is required. "\n        "Set it via: export DASHSCOPE_API_KEY='your-api-key'"\n    )\n\nclient = OpenAI(\n    api_key=api_key,\n    base_url=os.environ.get(\n        "DASHSCOPE_BASE_URL",\n        "https://dashscope-intl.aliyuncs.com/compatible-mode/v1",\n    ),\n)\n\nmessages = [{"role": "user", "content": "Write a Python function to merge two sorted linked lists."}]\n\ncompletion = client.chat.completions.create(\n    model="qwen3.8-flash",\n    messages=messages,\n    extra_body={\n        "enable_thinking": True,\n        # "preserve_thinking": True,\n    },\n    reasoning_effort="xhigh",  # supported levels are xhigh, medium, and low\n    stream=True,\n)\n\nreasoning_content = ""\nanswer_content = ""\nis_answering = False\nprint("\\n" + "=" * 20 + "Reasoning" + "=" * 20 + "\\n")\n\nfor chunk in completion:\n    if not chunk.choices:\n        print("\\nUsage:")\n        print(chunk.usage)\n        continue\n\n    delta = chunk.choices[0].delta\n\n    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:\n        if not is_answering:\n            print(delta.reasoning_content, end="", flush=True)\n        reasoning_content += delta.reasoning_content\n\n    if hasattr(delta, "content") and delta.content:\n        if not is_answering:\n            print("\\n" + "=" * 20 + "Answer" + "=" * 20 + "\\n")\n            is_answering = True\n        print(delta.content, end="", flush=True)\n        answer_content += delta.content\n
\n

Agent Frameworks & Coding Assistants

\n

Qwen3.8-Flash-Next integrates seamlessly with popular agent frameworks and coding assistants:

\n

QwenWork

\n

QwenWork is Alibaba’s flagship AI productivity platform, designed to help individuals and enterprises automate daily tasks and accelerate operational efficiency.

\n

We are excited to share that QwenWork has integrated Qwen3.8-Flash-Next to power its newly launched “Standard” mode, leveraging the model’s cutting-edge capabilities to deliver a seamless, cost-effective experience that sets a new standard for AI agents in the workplace.

\n

\"Qwen3.8-Flash

\n

Learn more in the official documentation!

\n

Claude Code

\n

Qwen APIs support the Anthropic API protocol, enabling direct use with Claude Code:

\n
npm install -g @anthropic-ai/claude-code\n\nexport ANTHROPIC_MODEL="qwen3.8-flash"\nexport ANTHROPIC_SMALL_FAST_MODEL="qwen3.8-flash"\nexport ANTHROPIC_BASE_URL=https://dashscope-intl.aliyuncs.com/apps/anthropic\nexport ANTHROPIC_AUTH_TOKEN=<your_api_key>\n\nclaude\n
\n

Codex

\n

Qwen APIs support the OpenAI Responses protocol, enabling use with Codex:

\n

In ~/.codex/model-catalog.local.json

\n
{\n  "models": [\n    {\n      "slug": "qwen3.8-flash",\n      "display_name": "qwen3.8-flash",\n      "description": "QwenCloud: Qwen3.8-Flash",\n      "default_reasoning_level": "xhigh",\n      "supported_reasoning_levels": [\n        {\n          "effort": "low",\n          "description": "Fast responses with lighter reasoning"\n        },\n        {\n          "effort": "medium",\n          "description": "Greater reasoning depth for complex problems"\n        },\n        {\n          "effort": "xhigh",\n          "description": "Extra high reasoning depth for complex problems"\n        }\n      ],\n      "context_window": 1000000,\n      "effective_context_window_percent": 95,\n      "supports_parallel_tool_calls": true,\n      "supports_image_detail_original": true,\n      "input_modalities": ["text", "image"],\n      "shell_type": "default",\n      "visibility": "list",\n      "supported_in_api": true,\n      "priority": 1,\n      "base_instructions": "",\n      "support_verbosity": false,\n      "supports_reasoning_summaries": false,\n      "experimental_supported_tools": [],\n      "truncation_policy": {\n        "mode": "bytes",\n        "limit": 10000\n      }\n    }\n  ]\n}\n
\n

In ~/.codex/config.toml

\n
model_catalog_json = "~/.codex/model-catalog.local.json"\n\nmodel_provider = "QwenCloud"\nmodel = "qwen3.8-flash"\n\n[model_providers.QwenCloud]\nname = "QwenCloud"\nbase_url = "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"\nenv_key = "OPENAI_API_KEY"\nwire_api = "responses"\n
\n
npm install -g @openai/codex\n\nexport OPENAI_API_KEY=<your_api_key>\n\ncodex\n
\n

Qoder CLI

\n

Qoder co-evolves with Qwen for agentic coding:

\n
curl -fsSL https://qoder.com/install | bash\n\nqoder\n
\n

Qwen Code

\n

Qwen Code is deeply optimized for the Qwen series:

\n
npm install -g @qwen-code/qwen-code@latest\n\nqwen\n
\n

OpenClaw

\n

Connect to OpenClaw via QwenCloud:

\n
curl -fsSL https://openclaw.ai/install.sh | bash\n\nexport DASHSCOPE_API_KEY=<your_api_key>\n\nopenclaw dashboard\n
\n

Configure ~/.openclaw/openclaw.json:

\n
{\n  "models": {\n    "mode": "merge",\n    "providers": {\n      "qwencloud": {\n        "baseUrl": "https://dashscope-intl.aliyuncs.com/compatible-mode/v1",\n        "apiKey": "DASHSCOPE_API_KEY",\n        "api": "openai-completions",\n        "models": [\n          {\n            "id": "qwen3.8-flash",\n            "name": "qwen3.8-flash",\n            "reasoning": true,\n            "input": ["text", "image"],\n            "contextWindow": 1000000,\n            "maxTokens": 65536\n          }\n        ]\n      }\n    }\n  },\n  "agents": {\n    "defaults": {\n      "model": {\n        "primary": "qwencloud/qwen3.8-flash"\n      }\n    }\n  }\n}\n
\n

Summary

\n

Qwen3.8-Flash-Next extends the hybrid architecture introduced in Qwen3-Next along four directions: attention, residual, embedding and optimization. QSA compresses the sequence into micro-blocks within each layer, reducing both the attention cost and the indexing cost at long context while keeping precise retrieval. Gated Residual widens the residual stream into several parallel branches and controls reads and writes with an elementwise, data-dependent gate, improving cross-layer information flow and training stability at negligible arithmetic cost; the residual state can additionally be kept in FP8, which further reduces memory traffic. N-gram embedding scales capacity through deterministically addressed lookup memory, which can be scaled with negligible per-token computation and offloaded to host memory. On the optimization side, Muon is used as the main optimizer, with orthogonalization accuracy, parameter assignment and fused-matrix splitting as the decisive implementation choices, and the scaling law refitted for the new architecture.

\n

We release these weights early so that the architecture can be evaluated independently by the community, as we did with Qwen3-Next, and we will continue to refine it towards Qwen4.

\n

Citation

\n
@techreport{qwen2026design,\n    title       = {On the Design of {Qwen3.8-Next} Architecture: Evaluation, Efficiency, and Training Stability},\n    author      = {{Qwen Team}},\n    institution = {Alibaba Group},\n    month       = {August},\n    year        = {2026}\n}\n\n@misc{qwen3.8flashnext,\n    title  = {{Qwen3.8-Flash-Next}: A New Architecture, Towards Ultimate Cost-Efficiency},\n    author = {{Qwen Team}},\n    month  = {August},\n    year   = {2026},\n    url    = {https://qwen.ai/blog?id=qwen3.8-flash-next}\n}\n
\n

References

\n

[1] Gated Delta Networks: Improving Mamba2 with Delta Rule

\n

[2] DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models

\n

[3] IndexCache: Accelerating Sparse Attention via Cross-Layer Index Reuse

\n

[4] Hyper-Connections

\n

[5] A Unified View of Attention and Residual Sinks: Outlier-Driven Rescaling is Essential for Transformer Training

\n

[6] Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models

\n

[7] Muon: An Optimizer for Hidden Layers in Neural Networks

\n

[8] Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models

\n

[9] Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free

\n", "date_published": "2026-08-26T12:30:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen3.8-flash-next", "id": "urn:aggr:item:8a6057a8cf7d2b92eabe480c0bb4930b5a0f2699", "image": "https://img.alicdn.com/imgextra/i1/O1CN01ZxcR5nZsP9E3E9bs_!!6000000003536-2-tps-1590-954.png", "summary": "HUGGING FACE MODELSCOPE TECH REPORT FLASHQLA DISCORD Introduction In this release we are opening the weights of Qwen3.8-Flash-Next, a multimodal MoE model that also serves as an early preview of the architecture used in Qwen4. It plays the…", "tags": [ "ai", "open-source" ], "title": "Qwen3.8-Flash-Next: A New Architecture, Towards Ultimate Cost-Efficiency", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-08-26-qwen3-8-flash-next-a-new-architecture-towards-ultimate-cost/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

QWEN STUDIO DISCORD

\n

Today, we are officially releasing Qwen 3.8-Max, the most capable model in the Qwen family to date. This also marks the first time we will open-source the weights of a Qwen-Max-class model — the open weights will be released next week. Built upon the architectural foundation of Qwen 3.5, Qwen 3.8-Max scales to 2.4 trillion parameters, delivering comprehensive improvements across coding, work, research, and long-horizon tasks. It can not only answer more challenging questions, but also complete complex tasks end-to-end with greater reliability, producing dependable deliverables.

\n\n

\"\"

\n

Coding

\n

For a top model, coding today means far more than writing a function on request — it means taking a real, multi-day project from an empty folder all the way to a finished result, on its own. We tested Qwen3.8-Max on three such challenges, where every result had to be earned by actually writing and running code, with no human help at all. One thread runs through all three: Qwen3.8-Max doesn’t just follow a fixed plan — it self-evolves through feedback loops, whether that means building a harness that upgrades itself, refining a research method experiment after experiment, or climbing a competition leaderboard submission after submission.

\n

10+ Days of Autonomous Coding: Building a Self-Evolving Harness

\n

In this case, Qwen3.8-Max was asked to create the oh-my-cli project from scratch and, over a 10+ day long-horizon autonomous coding run, build a self-evolving harness. It brings user feedback, advanced community practices, and the model’s own self-test results into one engineering loop: requirements are normalized into issues, automatically claimed and executed by agents, and continuously iterated through code, tests, previews, and logs. The complete project trace is publicly available in the GitHub repository qwen-code-dev-bot/oh-my-cli.

\n

Key implementation details in the autonomous coding harness:

\n\n

As of July 30, 2026, after approximately 16 days of fully autonomous AI operation, the repository had accumulated 265 commits, 127 PRs, and 151 issues, demonstrating a continuously evolving autonomous coding capability.

\n

Video 1. In a 10+ day long-horizon autonomous coding run, Qwen3.8-Max autonomously builds a self-evolving harness, continuously completing community requirement collection, issue dispatch, code generation, verification, and self-repair.

\n

Reproduce a research paper — then improve it

\n

We handed Qwen3.8-Max a recent research paper — “Unified Data Selection for LLM Reasoning” — and asked it to: reproduce the paper’s experiment in code, then try to do better. The paper tackles a very practical question in AI training: when you have far more data than you can afford to train on, which examples are actually worth keeping? The paper’s answer is to prize the examples full of “hard decision points” — the moments in a worked solution where the model was genuinely unsure which way to go next.

\n

The catch: Qwen3.8-Max started from nothing but the paper and a set of GPUs — no starter code, no ready-made pipeline. The data-processing scripts, the training code, the evaluation setup — it had to design and write all of it from scratch, exactly the kind of work that takes skilled engineers days.

\n

Working completely on its own for about five days (~125 hours of continuous effort), Qwen3.8-Max wrote roughly 7,600 lines of code, took over 1,100 actions, and ran 33 rounds of GPU training. It first spent ~37 hours rebuilding the paper’s full pipeline from zero and reproduced its six main findings — repeatedly fine-tuning a Qwen3-8B model on the data it selected and confirming the gains on hard math benchmarks (for instance, the paper’s selection method beats picking data at random by +7.7% on AIME24).

\n

Then it went further, turning reproduction into self-evolution. Over the next ~88 hours it ran a self-improving research loop — form a hypothesis → write the code → run it on GPUs → analyze → try again — inventing and testing 18 improvement ideas of its own across four rounds. Each round’s results fed the next round’s hypotheses, and by diagnosing what went wrong with each attempt it finally evolved a new method that beats the paper’s own approach, a +2.7-point gain on the competition-level math benchmark AIME24.

\n

docs.qwenlm.ai

\n

How the improvement search unfolded — 4 rounds, 18 ideas

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
RoundBest idea that roundScore (AIME24)Gain vs. baseline
—Paper’s method, reproduced (baseline)49.58%—
1Split the data by difficulty before selecting50.42%+0.84
2Weight examples by an entropy–score gap51.67%+2.09
3Tune the selection width51.25%+1.67
4Count the hard decision points (“nhighgate”) ★52.29%+2.71
\n

Beat hundreds of human teams in 24 hours

\n

Next we entered Qwen3.8-Max into a real online contest — the WWW2025 Multimodal Dialogue Intent Recognition Challenge, hosted on Alibaba Cloud’s Tianchi platform, where 526 human teams were competing. The task: read customer-service chats — both the text and the screenshots — and correctly work out what the customer wants.

\n

Working entirely on its own and under a strict 24-hour time limit, Qwen3.8-Max read the competition rules and built a full solution in code. For the text side, it fine-tuned and ensembled several Chinese language models — BERT, MacBERT, and RoBERTa; for the product screenshots, it fine-tuned a vision-language model, Qwen2.5-VL-7B, backed by a Chinese-CLIP model for images its main model was unsure about. It then fused all of them into a single weighted-voting system, calibrating how much each model’s vote should count through cross-validation and adding extra image voters to break ties. Across 45 submissions — each round’s feedback steering the next round of fine-tuning and re-weighting — its accuracy climbed steadily from 0.60 to a final 0.853, beating 458 of the 526 human teams (87% of the field).

\n

docs.qwenlm.ai

\n

Together, these three cases show what makes Qwen3.8-Max stand out: it can stay focused on a hard, open-ended goal for days, come up with its own ideas, and turn them into working results — all without a human in the loop.

\n

Work

\n

Alongside coding, real work - the messy, multi-step, tool-heavy tasks that fill the working day in nearly every profession - is the other main track where frontier models create enormous economic value. Making Qwen3.8-Max broadly competent and reliably robust across these workflows is therefore central to our mission.

\n

Scaling Real-World RL Systems. By jointly scaling RL environments and compute, we lift general working competence uniformly across several popular harnesses (QwenWork / Claude Code / Codex / OpenClaw / Hermes). Achieving this required addressing three coupled challenges:

\n
    \n
  1. \n

    Continuously scaling decoupled real environments along independent axes — Task (single-task → multi-task → multi-day), Workspace (multi-file → hierarchical folders → complex heterogeneous folders), and Harness (category, version, skills) — so environment growth compounds combinatorially rather than requiring bespoke integration.

    \n
    2. \n
  2. \n

    A Universal Reward System that internalizes heterogeneous verification — spanning execution-based checking, rubric-conditioned adjudication over text and rendered visual output, and agentic inspection — under automatically scalable rubrics. By unifying these modalities within one reward system, it provides a coherent and reliable source of reward across all environments, eliminating the inconsistency inherent in maintaining task-specific verifiers.

    \n
    3. \n
  3. \n

    An online data balancer that shapes every batch to keep its distribution over tasks, difficulty, workspaces, and harnesses highly balanced, suppressing inter-batch gradient variance and thereby sustaining stable, continued scaling of RL compute.

    \n
    4. \n
\n

Together these supply breadth, reliable reward, and stability — turning joint environment-and-compute scale into a measurable, horizontal lift in real-world working ability.

\n

\"Fig

\n

Fig 1. Qwen3.8-Max shows steady, consistent gains across dozens of in-house and public working benchmarks as RL training continues to scale up.

\n

\"Fig

\n

Fig 2. Qwen3.8-Max achieves comparable performance across many harnesses, including QwenWork, Claude Code, Codex, OpenClaw, and Hermes.

\n

Testing the Breadth of Working Ability Across Hundreds of High-Value Professions

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As frontier models take on an ever-widening role in economically valuable work, we stress-tested the breadth of Qwen3.8-Max’s ability to deliver production-quality results in real workflows — spanning high-frequency tasks across several hundred high-economic-value professions. A few representative showcases:

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Video 1. Across hundreds of high-value professions, Qwen3.8-Max measurably boosts human productivity in real workflows — showcasing the breadth of its working ability.

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Building a Profitable End-to-End Quant Strategy in a Single Session

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Powered by its Dynamic Workflows construction capability, Qwen3.8-Max drives task planning programmatically and orchestrates large-scale sub-agent systems with precision — turning a single conversation into an end-to-end, automated quant-research loop.

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Depth — end-to-end ETF-rotation strategy R&D. From a one-line task description, Qwen3.8-Max autonomously planned a complex dynamic workflow and worked for hours to deliver a complete ETF-rotation strategy — building the data system, constructing base factors, and orchestrating multi-round greedy iteration, all while dynamically analyzing backtests and correcting course. Throughout, it acted on evidence instead of a fixed script:

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Breadth — massively parallel factor mining. Factor research entails a vast search space, and traditional workflows remain serial. Qwen3.8-Max parallelized the process: from just six short descriptions spanning the classic factor families of momentum, value, quality, investment, low-risk, and sentiment, it decomposed each into 50 research directions, dispatched ~330 sub-agents, completed ~6,000 backtests, and continuously adapted the workflow mid-run. The selected factors achieved excess Sharpe ratios of 0.64–1.48, with IC uniformly positive, ranging from 0.010 to 0.014.

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From coherent single-track R&D to parallel exploration of a huge hypothesis space, Qwen3.8-Max leverages Dynamic Workflows to freeze orchestration logic into reproducible programs — compressing quant research that once took researchers weeks to months of serial work into a scalable, automated loop delivered within a single conversation, demonstrating the model’s broad potential for long-horizon autonomous work.

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Video 2. Qwen3.8-Max promises to put a quant researcher's expertise within everyone's reach — showcasing the depth of its working ability.

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Long-Horizon Task

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When tackling highly complex, long-horizon, and multi-constraint tasks, Qwen3.8-Max demonstrates exceptional system-level autonomous planning and end-to-end closed-loop adaptive learning. Whether navigating stringent physical constraints in digital chip design or highly competitive, strategic business simulations, the model achieves deep algorithmic and strategic refactoring across thousands of rounds of interaction via an action-feedback-iteration loop.

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Autonomous Chip Design and Closed-Loop Feedback-Driven Optimization

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Qwen3.8-Max has independently achieved the autonomous execution of the entire silicon design flow, spanning logic restructuring, multi-constraint optimization, and physical layout generation. The target design is a GCD / RSA cryptographic hardware accelerator that integrates modular exponentiation and modular multiplication. Built on a GCD datapath and control path, this block represents a typically compact yet logic-dense digital circuit. Under a randomized cocotb verification framework, the model must maintain bit-exact functional correctness across 4-, 6-, 8-, and 16-bit configurations while minimizing the synthesized gate count (Yosys cell count)—a direct addressing of the classic trade-off between area and correctness in front-end hardware design. Area performance is evaluated based on the 16-bit (WIDTH = 16) configuration.

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Qwen3.8-Max optimized this design within a sandboxed environment integrated with simulation (Iverilog), synthesis (Yosys), and physical design (OpenROAD) toolchains. Starting with minimal inputs—a basic task description, a stub RTL workspace with empty module templates, and an evaluation script for verification and synthesis—Qwen3.8-Max operated completely autonomously. Without any golden reference designs or human intervention, the model independently executed the entire process from high-level algorithmic architecture design to RTL code generation and multi-round iterative refinement.

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Over a single continuous autonomous run, Qwen3.8-Max completed approximately 500 turns and 71 evaluations across 13 key milestones, executing an end-to-end restructure of the design. The model autonomously managed RTL editing, simulation debugging, synthesis analysis, redundancy localization, and iterative datapath re-architecting—advancing from initial bug-fixing to deep, algorithm-level rewrites. While its first functionally viable design measured 8,298 gates, Qwen3.8-Max drove this down to 678 gates, leading all evaluated models. This trajectory demonstrates that Qwen3.8-Max is capable of major structural breakthroughs even hundreds of turns into a run, rather than plateauing after early, low-hanging gains.

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Key Design Milestones Along the Trajectory: (The evolution records preserve the complete circuit topology and the corresponding code diff details at each stage)

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To verify whether front-end optimizations translate to physical implementation, Qwen3.8-Max ran the RTL design through a standard place-and-route (PR) flow using OpenROAD (Nangate45 PDK) to generate a physical silicon layout. In the physical layout representation, each chip demonstrates the actual routing results: standard cells are laid out on the physical plane of the die, with metal routing layers stacked above (each layer color-coded and connected by vertical vias). The starting design occupied a 106×106 µm² die with a total wirelength of 33,369 µm and severe timing violations (a negative slack of -4.46 ns). The final layout shrank to a 46×46 µm² die, with wirelength dropping to 4,187 µm, and successfully achieved timing closure at 500 MHz (+0.66 ns Slack). This represents an 81% reduction in physical die area, proving that high-level front-end architectural optimizations translate directly into highly compact, routable, and performant silicon implementation.

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docs.qwenlm.ai

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This case highlights two pivotal capabilities of Qwen3.8-Max as a foundational model for autonomous, long-horizon hardware agents:

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  1. Long-horizon Sustained Optimization: The model maintains a highly coherent, systematic strategy over hundreds of complex interaction turns, driving deep into algorithmic-level datapath rewrites rather than stalling at superficial syntax adjustments.
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  2. Feedback-driven Closed-loop Improvement: In the absence of prior reference designs, the model relies entirely on an “edit-simulate-synthesize-layout” feedback loop to drive optimization. Each design iteration is strictly validated through automated cocotb functional tests, with physical feasibility fully guaranteed by OpenROAD backend validation.
    3. \n
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Continuous Learning in Long-term Operations

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E-Commerce Bench is a 365-day long-cycle e-commerce operation simulation benchmark, designed to evaluate large language models’ business decision-making capabilities in sustained operational scenarios. Built on real, desensitized transaction data from Taobao and Tmall, this benchmark deeply replicates a complex ecosystem comprising 12 store types, 60 product categories, nearly 600 suppliers, and 7,000 products. The model is given ¥100,000 in starting capital to simultaneously operate multiple online stores. Throughout the year, it must contend with seasonal demand swings, sudden environmental events, and cash flow pressures from a highly realistic e-commerce settlement system. The model must autonomously make full-chain decisions, including product selection, supply chain negotiation, inventory management, dynamic pricing, and returns handling, with the ultimate goal of maximizing total balance by year-end. This also tests the model’s capital allocation strategy throughout the year. It must know when to invest proactively for growth. Just as importantly, it must convert inventory and operating gains into cash before the cycle ends. Otherwise, unconverted assets left on the books can hurt the final results.

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In price negotiations, the benchmark introduces a supplier matrix, driven by game theory principles, where each supplier possesses distinct personality traits and concession strategies. This requires the model to negotiate through multi-round natural language interactions. Qwen3.8-Max demonstrated continuous learning capability in negotiations. It conducted deep probing on the same products from the same suppliers, achieving progressive reductions in procurement prices and steady increases in profit round by round. This caused the negotiation efficiency (represented by the area in the radar chart) to continuously expand over time. Moreover, it effectively generalized this negotiation experience to similar products, while other models’ negotiation efficiency generally hit a plateau in the mid-term.

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Additionally, the model had to navigate hidden risks beneath the surface and complex market rhythms. Within the matrix of nearly 600 suppliers, the benchmark covertly embedded 152 fraudulent merchants, encompassing classic scam patterns such as “membership fee traps,” “low-price bait,” and “goods not as described.” This comprehensively tested the model’s risk control capabilities. At the same time, the pressure of surging orders during annual major promotions intertwined with random supply chain crises, like typhoons and material shortages, pushing the model’s stocking rhythm and crisis management abilities to the limit. Against this backdrop, Qwen3.8-Max exhibited exceptional forward-looking planning capability. It invested the most capital in the earliest stage of operations to establish its position, which accelerated its subsequent asset growth curve. It also achieved a net profit exceeding ¥100,000 during the year-end major promotion period—nearly 2.4 times that of the second-place GLM 5.2.

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Qwen3.8-Max ultimately achieved the highest total balance of ¥416,252 (a 4.16x return), surpassing the second-place GLM 5.2 by 38%. This also represents a 152% improvement over its previous flagship generation, Qwen3.7-Max. These results demonstrate that Qwen3.8-Max possesses advantages in long-horizon coherent decision-making. Furthermore, it has the ability to adaptively learn from transactional feedback, continuously iterating and evolving across more than 2,000 rounds of interaction, rather than rigidly adhering to strategies learned early on.

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Multimodal Agents

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From everything it sees to everything it does, Qwen3.8-Max is not merely capable of understanding images, documents, and videos. It delivers visual intelligence that runs through the entire task lifecycle.

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When working with financial reports and complex PDFs spanning more than 200 pages, Qwen3.8-Max can understand text, charts, and document layouts across pages, extract key insights from large volumes of information, and turn them into structured reports or production-ready web experiences. When processing videos longer than 100 hours, it can do more than locate specific moments and answer detailed questions. It can organize people, events, timestamps, and scenes into a video memory graph, continuously building connections across long time spans to reconstruct event progressions, character relationships, and critical moments.

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Whether the input is a hundreds-page document, a complete TV series, or a 100-hour livestream, information that would otherwise be difficult to consume can be transformed into a searchable, traceable, and interactive knowledge structure.

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Beyond understanding, Qwen3.8-Max can carry out real visual production tasks. It can edit personal footage into a vlog, turn a question into an immersive educational animation, reconstruct a complete frontend project from a single interface screenshot, transform a floor plan into a Blender-based 3D interior visualization, and develop interactive games and applications from a natural-language request.

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More importantly, vision is not limited to the input stage. During execution, Qwen3.8-Max continuously observes and evaluates its own intermediate results. It can inspect page layouts, object orientations, spatial relationships, animation quality, and interaction outcomes. When it detects issues—such as a television facing the wrong direction, a misaligned interface, or a visual result that does not match the intended design—it can identify the deviation, revise its plan, and correct the output autonomously.

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This means vision is no longer simply another modality that an agent uses to understand input. It becomes a native feedback loop across planning, execution, verification, and iteration. The model generates while observing, acts while reviewing, and repeatedly examines the result, identifies problems, and improves its work. This visual feedback loop moves an agent beyond merely completing a task toward completing it well.

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Qwen3.8-Max is helping multimodal agents evolve from understanding the world to continuously acting and creating within it through vision.

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In the digital world, finishing a complex task on its own often takes two things at once: writing code to implement the underlying logic, and operating the interface by hand to drive the task and observe the result. This Hybrid Agent capability — the pairing of coding and GUI operation — makes the two channels complementary: coding does the heavy lifting efficiently and at scale, while GUI operation reaches whatever a human can see and touch and, just as importantly, feeds back what actually happens in a live system — extending the visual feedback loop above from inspecting its own output to verifying against a real, running application.

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To measure this, we introduce RecreationBench, a long-horizon application-recreation benchmark spanning five platforms — desktop (Ubuntu, macOS, Windows), mobile (Android), and web. The model may observe a real, running application only as a black box — no source code, no internet access — making sense of it purely through interaction and feedback, then rebuilding the whole application from scratch. Here Qwen3.8-Max already demonstrates frontier-level Hybrid Agent capability, converging on the original step by step through repeated cycles of iterative coding and interactive feedback.

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To make these capabilities easier to integrate into existing agent systems, we are also introducing Qwen-MM-Plugins. It is a harness extension library designed for multimodal agents, providing agent frameworks with image and video processing, multimodal memory, dynamic-resolution support, visual tool use, and specialized capabilities for tasks such as video editing, Blender, and CAD. With Qwen-MM-Plugins, any existing agent harness can be extended into a more naturally multimodal-native system.

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User Feedback

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The most honest take on Qwen3.8-Max comes from people who actually put it to work. Top-tier agent platforms, leading open-source algorithm teams, professional firms in law, finance, and manufacturing, scrappy startups, solo developers, and academic researchers — all of them keep handing it their most complex, mission-critical, and long-horizon tasks.

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Enterprises use it to stand up large-scale agent systems. Knowledge workers dump their images, manuscripts, and video on it, and get everything processed. Developers hand it their heaviest engineering tasks outright. Research teams run the loop of literature, data, and simulation end to end. One model, reached for so often across such different work that it becomes indispensable. The verdict is the same: Qwen3.8-Max drives long, autonomous task chains and turns out ship-ready results in a single pass.

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Full Benchmark Table

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Opus4.8Fable5GPT5.6 Sol (max)Qwen3.7-MaxQwen3.8-Max
Coding Agent
Terminal Bench 2.184.684.688.874.586.6
SWE-bench Pro69.280.064.660.667.7
DeepSWE 1.159.070.073.021.656.6
NL2Repo-Bench69.4----47.255.9
FrontierSWE70.088.8--40.773.5
MLS-Bench-Lite42.849.946.231.741.0
PaperBench80.388.890.564.893.0
AndroidBench69.884.574.056.575.1
QwenSWEBench84.086.373.563.480.7
QwenQoderBench62.763.153.836.858.4
QwenReactBench16941770156415381724
QwenSVGBench16481690175814991713
General Agent
CoWorkBench72.375.971.564.674.8
WorkSpaceBench66.868.765.661.467.7
JobBench48.457.445.431.353.4
SkillsBench65.170.973.561.270.2
Agents' Last Exam (Pass / Score)27.0 / 45.1-- / --30.6 / 53.611.8 / 31.127.0 / 52.4
Automation-Bench (Pass@1)27.229.129.714.227.3
Toolathlon Verified (Pass@1)76.277.974.949.772.5
WideSearch72.981.2--75.281.9
HLE w/ tools57.964.558.053.556.2
General Capabilities
GPQA Diamond92.092.694.192.492.6
HLE45.753.347.241.443.6
IFBench62.263.572.779.182.8
$OneMillion-Bench (expert score)41.855.953.844.452.5
HealthBench52.4--55.354.560.2
PLawBench69.670.272.358.973.2
PRBench-Legal52.757.657.648.557.6
PRBench-Finance51.955.855.546.858.3
MRCR v2 256K (8-needle)83.2--93.886.792.9
LongBench v269.1--67.165.366.3
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1. Fable5 results may involve fallbacks.
\n2. Terminal Bench 2.1: Evaluated with Claude Code (avg@10), using a 5-hour timeout and max_tokens=131,072. For all other models, we report the best published score across harnesses: Claude Opus 4.8 and Claude Fable 5 with Terminus 2 from Artificial Analysis (https://artificialanalysis.ai/evaluations/terminalbench-v2-1); GPT-5.6 Sol with Codex (https://openai.com/index/previewing-gpt-5-6-sol/).
\n3. SWE-bench Pro: Evaluated with the Claude Code harness, temp=1.0, top_p=0.95, and a 256K context window. Problematic tasks corrected and all baselines evaluated on the refined benchmark.
\n4. DeepSWE 1.1: Evaluated with the Claude Code and mini-SWE-agent harnesses, temp=1.0, top_p=0.95, and a 256K context window. We report the highest score among both harnesses; notably, Qwen3.8-Max performs best on Claude Code.
\n5. NL2Repo-Bench: Evaluated with the Claude Code harness. To prevent reward hacking, we disable Bash commands that attempt to access the specific repository, such as pip download, pip install, and git clone.
\n6. FrontierSWE: Evaluated with the Claude Code harness. All other available MEAN@5 results are taken from the official FrontierSWE leaderboard (https://www.frontierswe.com) as of August 3, 2026. Dominance scores are recomputed from the raw scores using the official evaluation script. "--" indicates that no official MEAN@5 result was available as of that date.
\n7. MLS-Bench-Lite: Evaluated with Claude Code using a 5-hour timeout and max_tokens=131,072. All other model scores are taken from the official leaderboard.
\n8. PaperBench: Evaluated in the BasicAgent setting under Code-Dev mode, judged by Claude Opus 4.6, and averaged over 3 runs (max 12 hours per run).
\n9. AndroidBench: Evaluated on the 95-task public subset, reporting avg@3 scores.
\n10. QwenSWEBench: Inhouse coding benchmark to evaluate models' software engineering capabilities. Evaluated with the Claude Code harness. Reporting avg@3 with an 8-hour timeout, max_tokens=32,768, temperature=1.0, and a 256K-token context window.
\n11. QwenQoderBench: Inhouse coding benchmark to evaluate user experience on Qoder. Evaluated with the Claude Code harness. Reporting avg@5 with a 6-hour timeout, max_tokens=32,768, temperature=1.0, and a 256K-token context window.
\n12. QwenReactBench: Inhouse React project building benchmark using Claude Code as the harness, bilingual (EN/CN), 7 categories; auto-render + multimodal judge; BT/Elo rating.
\n13. QwenSVGBench: Inhouse SVG code generation benchmark; bilingual (EN/CN), auto-render + multimodal judge; BT/Elo rating.
\n14. CoWorkBench: Inhouse cowork benchmark for evaluating long-horizon tasks across computer science, finance, law, medical, and other productivity domains.
\n15. SkillsBench: Evaluated on the public SkillsBench v1.1 benchmark across 87 tasks, reporting the average score over three runs per task. Opus 4.8 and Fable 5 are evaluated on Claude Code; GPT-5.6 Sol is evaluated on Codex; the Qwen-series are evaluated on OpenCode. All results are from our own testing.
\n16. Automation-Bench: Evaluated on the 600-task public subset.
\n17. WideSearch: Evaluated with the Claude Code harness for external models and the Qwen-Agent harness for ours, reporting the average item-F1 over four runs.
\n18. $OneMillion-Bench: Evaluated using gemini-3.1-pro-preview.
\n19. PLawBench: Evaluated using gemini-3.1-pro-preview.
\n20. Empty cells (--): Scores are not yet available or are not applicable.

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Opus4.8Fable5Gemini3.1-ProGPT5.6-SolQwen3.7-PlusQwen3.8-Max
Multimodal Reasoning
MMMU-Pro75.681.280.583.079.082.3
MathVision87.1 / 97.192.7 / 98.687.4 / 95.790.8 / 97.890.3 / --95.2 / 97.7
BabyVision28.4 / 81.242.5 / 90.555.9 / 68.365.5 / 88.964.7 / 70.482.0 / 91.3
HLE-VL (w/ Tools)----43.951.225.652.2
ZeroBench (Pass@5)17.0 / 34.020.0 / 46.017.0 / 23.022.0 / 35.019.0 / 19.024.0 / 49.0
ZeroBench-Sub31.137.136.546.741.048.5
LogicVista76.785.782.689.784.391.9
HiPhO69.378.685.486.884.190.0
PhyX54.271.779.479.180.083.5
SLAKE75.986.682.985.183.290.8
MedXpertQA-MM71.780.080.781.571.080.4
PMC-VQA59.263.262.562.363.466.2
Visual Agent & Coding
OSWorld-Verified83.485.076.283.273.386.1
OSWorld 2.020.6 / 54.8-- / 66.17.8 / 30.6-- / 62.62.8 / 21.519.4 / 46.7
ScreenSpot Pro82.387.368.181.379.084.5
WebArena-Verified67.971.364.369.755.366.8
AndroidWorld75.088.870.777.681.085.3
MobileWorld67.585.558.176.951.277.8
ClawEval-MM73.3 / 73.881.2 / 77.550.5 / 55.281.2 / 78.957.4 / 60.177.2 / 74.8
Vision2Web62.470.5--62.142.169.0
QwenBlenderBench62.469.523.068.641.569.9
Parametric CAD Bench85.187.573.586.273.891.5
RecreationBench48.056.116.247.630.251.7
PresentBench80.979.855.482.965.779.6
Document & Office Intelligence
CharXiv (RQ)78.5 / 89.987.9 / 93.584.4 / 89.985.1 / 89.185.8 / 85.988.4 / 93.5
OmniDocBench 1.586.589.590.086.791.492.1
OCR-Bench-V2 (EN/ZH)53.9 / 55.365.3 / 58.164.6 / 58.269.0 / 57.370.7 / 67.174.2 / 68.3
CC-OCR-Bench-V260.372.468.968.072.779.6
MTVQA-Test48.141.654.352.751.256.6
MADQA86.886.081.187.887.191.8
QwenVisualOffice34.532.439.629.532.444.6
Real-World & Spatial Understanding
RealWorldQA76.685.983.583.786.988.0
ERQA57.270.068.070.069.877.8
LingoQA73.877.466.872.683.484.8
SURDS62.279.464.063.077.277.8
Visual Perception & Grounding
SimpleVQA67.373.473.166.670.375.0
WorldVQA33.953.554.045.143.953.2
MMStar76.780.584.082.583.285.9
PerceptionBench47.257.256.259.751.163.5
CountQA41.363.172.868.677.082.4
RefAdv-S61.768.671.969.273.080.2
Dense20020.831.169.755.360.787.0
COCO50.756.472.461.274.278.7
VisFactor30.154.539.862.842.860.8
VLMsAreBiased43.861.274.159.836.688.3
Video Intelligence & Agents
VideoMME (w/ Sub.)85.4--86.789.588.090.4
VideoMME v2 (w/ Sub.)49.052.266.971.159.768.3
VideoMMMU75.381.285.385.085.488.7
MMVU67.472.077.981.276.682.4
MLVU (M-Avg)53.4--84.787.687.490.8
TVBench61.5--73.083.278.281.9
LVBench67.3--75.178.876.281.8
LVBench (w/ Mem.)84.390.1--84.274.585.6
EgoLife (w/ Mem.)78.382.3--70.868.880.3
VideoDR (w/ Search)65.677.1--71.341.073.2
\n

1. MathVision, BabyVision, CharXiv (RQ), and ZeroBench: Scores are reported as “without CI / with CI.” A small number of incorrect ground-truth annotations in MathVision and CharXiv (RQ) were corrected following manual verification.
\n2. MathVision: Our model is evaluated using a fixed prompt, e.g., “Please reason step by step, and put your final answer within \\boxed{}.” For other models, we report the higher score obtained from runs with and without the \\boxed{} formatting requirement.
\n3. MMMU-Pro: Results for Gemini3.1-Pro and GPT5.6-Sol are taken from official model reports or system cards. All other models are evaluated in-house.
\n4. ClawEval-MM: Scores are reported as “Pass@3 / average score.” Pass@3 measures the percentage passed in at least one of the three trials, and average score is the mean score across the three trials.
\n5. Vision2Web: Scores are averaged across the frontend, webpage, and website categories, using the Claude Code harness and gpt-5.4-2026-03-05 as the judge.
\n6. HLE-VL (w/ Tools): Scores are evaluated with tool use, including both Code Interpreter (CI) and Search. Scores for the tool-enabled versions of Gemini3.1-Pro and GPT5.6-Sol are measured end-to-end through their official native tool-calling APIs.
\n7. OSWorld 2.0: Scores are reported as “binary / partial.” The binary score is the percentage of tasks receiving the full task reward, while the partial score aggregates the partial rewards obtained across all tasks.
\n8. ScreenSpot Pro: Scores for Opus4.8 and Fable5 are taken from official system cards. The Fable5 results refer to the corresponding Mythos Preview scores. All other models are evaluated in-house.
\n9. WebArena-Verified: Scores are reported using the official WebArena grader within the OSWorld scaffold.
\n10. RecreationBench: An internal long-horizon application-recreation benchmark for evaluating hybrid-agent capabilities across five platforms: Ubuntu, macOS, Windows, Android, and the web.
\n11. PerceptionBench: Scores for comparison models are taken from the benchmark’s official release report, while our model is evaluated in-house.
\n12. VideoMME (w/ Sub.) and VideoMME v2 (w/ Sub.): Scores are evaluated with subtitles enabled.
\n13. QwenBlenderBench and QwenVisualOffice: Both are internal benchmarks.
\n14. LVBench and EgoLife (w/ Mem.): Scores are evaluated using a memory system built with Qwen-MM-Plugins, enabling fine-grained, long-horizon video memory.
\n15. VideoDR (w/ Search): Scores are evaluated with access to a search tool.
\n16. Empty cells (--): Scores are not yet available or are not applicable.

\n

Build with Qwen3.8

\n

Qwen3.8-Max is now available through QwenCloud. You can integrate it with popular agent frameworks and coding assistants. The model weights will be open-sourced on Hugging Face and ModelScope next week — stay tuned.

\n

API Usage

\n

Qwen3.8-Max comes with the official support for reasoning_effort, which can be used to adjust reasoning depth and control cost:

\n\n

In addition, preserve_thinking is enabled by default for all workloads for best out-of-the-box experience.

\n

QwenCloud

\n

QwenCloud supports industry-standard protocols, including chat completions and responses APIs compatible with OpenAI’s specification, as well as an API interface compatible with Anthropic.

\n
"""\nEnvironment variables:\n  DASHSCOPE_API_KEY: Your API Key from https://home.qwencloud.com/\n  DASHSCOPE_BASE_URL: (optional) Base URL for compatible-mode API.\n    - Beijing: https://dashscope.aliyuncs.com/compatible-mode/v1\n    - Singapore: https://dashscope-intl.aliyuncs.com/compatible-mode/v1\n    - US (Virginia): https://dashscope-us.aliyuncs.com/compatible-mode/v1\n"""\nfrom openai import OpenAI\nimport os\n\napi_key = os.environ.get("DASHSCOPE_API_KEY")\nif not api_key:\n    raise ValueError(\n        "DASHSCOPE_API_KEY is required. "\n        "Set it via: export DASHSCOPE_API_KEY='your-api-key'"\n    )\n\nclient = OpenAI(\n    api_key=api_key,\n    base_url=os.environ.get(\n        "DASHSCOPE_BASE_URL",\n        "https://dashscope-intl.aliyuncs.com/compatible-mode/v1",\n    ),\n)\n\nmessages = [{"role": "user", "content": "Write a Python function to merge two sorted linked lists."}]\n\ncompletion = client.chat.completions.create(\n    model="qwen3.8-max",\n    messages=messages,\n    extra_body={\n        "enable_thinking": True,\n        # "preserve_thinking": True,\n    },\n    reasoning_effort="xhigh",  # supported levels are xhigh, medium, and low\n    stream=True,\n)\n\nreasoning_content = ""\nanswer_content = ""\nis_answering = False\nprint("\\n" + "=" * 20 + "Reasoning" + "=" * 20 + "\\n")\n\nfor chunk in completion:\n    if not chunk.choices:\n        print("\\nUsage:")\n        print(chunk.usage)\n        continue\n\n    delta = chunk.choices[0].delta\n\n    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:\n        if not is_answering:\n            print(delta.reasoning_content, end="", flush=True)\n        reasoning_content += delta.reasoning_content\n\n    if hasattr(delta, "content") and delta.content:\n        if not is_answering:\n            print("\\n" + "=" * 20 + "Answer" + "=" * 20 + "\\n")\n            is_answering = True\n        print(delta.content, end="", flush=True)\n        answer_content += delta.content\n
\n

For more information, please visit the API doc.

\n

Coding Assistants

\n

Qwen3.8-Max integrates seamlessly with popular agent frameworks and coding assistants:

\n

Claude Code

\n

Qwen APIs support the Anthropic API protocol, enabling direct use with Claude Code:

\n
npm install -g @anthropic-ai/claude-code\n\nexport ANTHROPIC_MODEL="qwen3.8-max"\nexport ANTHROPIC_SMALL_FAST_MODEL="qwen3.8-max"\nexport ANTHROPIC_BASE_URL=https://dashscope-intl.aliyuncs.com/apps/anthropic\nexport ANTHROPIC_AUTH_TOKEN=<your_api_key>\n\nclaude\n
\n

Codex

\n

Qwen APIs support the OpenAI Responses protocol, enabling use with Codex:

\n

In ~/.codex/model-catalog.local.json

\n
{\n  "models": [\n    {\n      "slug": "qwen3.8-max",\n      "display_name": "qwen3.8-max",\n      "description": "Model Studio: Qwen3.8-Max",\n      "default_reasoning_level": "xhigh",\n      "supported_reasoning_levels": [\n        {\n          "effort": "low",\n          "description": "Fast responses with lighter reasoning"\n        },\n        {\n          "effort": "medium",\n          "description": "Greater reasoning depth for complex problems"\n        },\n        {\n          "effort": "xhigh",\n          "description": "Extra high reasoning depth for complex problems"\n        }\n      ],\n      "context_window": 1000000,\n      "effective_context_window_percent": 95,\n      "supports_parallel_tool_calls": true,\n      "supports_image_detail_original": true,\n      "input_modalities": ["text", "image"],\n      "shell_type": "default",\n      "visibility": "list",\n      "supported_in_api": true,\n      "priority": 1,\n      "base_instructions": "",\n      "support_verbosity": false,\n      "supports_reasoning_summaries": false,\n      "experimental_supported_tools": [],\n      "truncation_policy": {\n        "mode": "bytes",\n        "limit": 10000\n      }\n    }\n  ]\n}\n
\n

In ~/.codex/config.toml

\n
model_catalog_json = "~/.codex/model-catalog.local.json"\n\nmodel_provider = "ModelStudio"\nmodel = "qwen3.8-max"\n\n[model_providers.ModelStudio]\nname = "Model Studio"\nbase_url = "https://dashscope-intl.aliyuncs.com/compatible-mode/v1"\nenv_key = "OPENAI_API_KEY"\nwire_api = "responses"\n
\n
npm install -g @openai/codex\n\nexport OPENAI_API_KEY=<your_api_key>\n\ncodex\n
\n

Qoder CLI

\n

Qoder co-evolves with Qwen for agentic coding:

\n
curl -fsSL https://qoder.com/install | bash\n\nqoder\n
\n

Qwen Code

\n

Qwen Code is deeply optimized for the Qwen series:

\n
npm install -g @qwen-code/qwen-code@latest\n\nqwen\n
\n

OpenClaw

\n

Connect to OpenClaw via QwenCloud:

\n
curl -fsSL https://molt.bot/install.sh | bash\n\nexport DASHSCOPE_API_KEY=<your_api_key>\n\nopenclaw dashboard\n
\n

Configure ~/.openclaw/openclaw.json:

\n
{\n  "models": {\n    "mode": "merge",\n    "providers": {\n      "modelstudio": {\n        "baseUrl": "https://dashscope-intl.aliyuncs.com/compatible-mode/v1",\n        "apiKey": "DASHSCOPE_API_KEY",\n        "api": "openai-completions",\n        "models": [\n          {\n            "id": "qwen3.8-max",\n            "name": "qwen3.8-max",\n            "reasoning": true,\n            "input": ["text", "image"],\n            "contextWindow": 1000000,\n            "maxTokens": 65536\n          }\n        ]\n      }\n    }\n  },\n  "agents": {\n    "defaults": {\n      "model": {\n        "primary": "modelstudio/qwen3.8-max"\n      }\n    }\n  }\n}\n
\n

Summary

\n

Qwen3.8-Max is our most capable model to date, and the first open-weight model at Max scale. Scaling to 2.4 trillion parameters, it delivers comprehensive gains across coding, real-world work, long-horizon tasks, and multimodal agents — able to take complex, open-ended goals from start to finish with minimal human involvement and produce dependable deliverables. The open weights will be released next week. We welcome community feedback and look forward to seeing what you build.

\n

Citation

\n
@misc{qwen38,\n    title = {Qwen3.8-Max: A New Bar for Coding and Cowork},\n    url = {https://qwen.ai/blog?id=qwen3.8},\n    author = {{Qwen Team}},\n    month = {August},\n    year = {2026}\n}\n
\n", "date_published": "2026-08-03T02:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen3.8", "id": "urn:aggr:item:2f52bbaf55e009ed843afd33705164890682ab64", "image": "https://img.alicdn.com/imgextra/i1/O1CN01ITn7j2cu9OF3E9bs_!!6000000000314-2-tps-1590-954.png", "summary": "QWEN STUDIO DISCORD Today, we are officially releasing Qwen 3.8-Max, the most capable model in the Qwen family to date. This also marks the first time we will open-source the weights of a Qwen-Max-class model — the open weights will be…", "tags": [ "ai", "open-source" ], "title": "Qwen3.8-Max: A New Bar for Coding and Cowork", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-08-03-qwen3-8-max-a-new-bar-for-coding-and-cowork/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"\"

\n

We are launching Qwen-Image-3.0, the third-generation foundational image generation model in the Qwen-Image series. If the keyword for Qwen-Image-1.0 was “Precision”, and the keywords for Qwen-Image-2.0 were “Precision, Variety, Completeness, Beauty, and Authenticity”, then the core of Qwen-Image-3.0 comes down to a single word — “Real” (实).

\n

This “Real” is embodied across three dimensions:

\n\n

In a word, Qwen-Image-3.0 is not just pursuing “good-looking” — it is pursuing “useful”, making image generation a truly deployable productivity tool.

\n

Rich Content

\n

Let’s start with the image below, generated by Qwen-Image-3.0:

\n

\"\"

\n

As you can see, Qwen-Image-3.0 can accurately render a math slide, including spatial relationships, mathematical symbols, theorem descriptions, and other rich visual content. This content is laid out reasonably, with proper relative positioning, and looks rich in information.

\n

However, this is not the true strength of Qwen-Image-3.0. In fact, this is only “1/9” of its real capability, because this image is actually just one cell of a complex 3×3 grid generated by Qwen-Image-3.0. Let’s look at the original image:

\n

\"\"

\n

That’s right — the entire image above was generated by Qwen-Image-3.0 in a single pass, rather than being stitched together from multiple images. The difficulty of this image lies in the fact that each cell is a complex infographic; to precisely describe the full 3×3 grid takes a full 3.7k tokens.

\n

These 3.7k tokens must fully depict a tunnel safety comic, a spatial geometry lesson, a stylistic analysis of “Chu Shi Biao” (Memorial on Dispatching the Troops), physics projectile motion, a biology parasitology explainer, a medical diagram of right-side chest pain, the Sylow theorems of group theory, a bank internal-control management infographic, and a cell DNA structure comparison — each cell containing precise Chinese and English text, formulas, charts, cartoon characters, and more.

\n

And yet this remains effortless for Qwen-Image-3.0 — because Qwen-Image-3.0 raises the acceptable instruction length to 4.5k tokens, which means the model can understand and render extremely complex, information-dense visual layouts.

\n

This is an important characteristic of the “Rich Content” we mentioned: content can expand horizontally. Horizontal expansion reflects the model’s strength in semantic juxtaposition and spatial control — the ability to lay out multiple concepts in an orderly fashion within a single image and render them without mutual interference.

\n

Beyond horizontal expansion, depth is another important characteristic of Rich Content.

\n

Horizontal expansion tests “how many parallel elements can be placed on a single canvas,” while depth tests the model’s semantic deconstruction and logical nesting — whether it can render multiple nested interfaces layer by layer within a single image. The following example uses a single instruction to display, from outer to inner: a VSCode programming interface → a Qwen Chat interface → a Wechat interface → a pour-over coffee poster. Each layer preserves the authentic style and details of its respective UI, forming a “picture-in-picture-in-picture” visual depth.

\n

\"\"

\n

The two examples above illustrate the meaning of “Rich Content” along both the horizontal and vertical dimensions.

\n

Authentic Details

\n

If “Rich Content” addresses the question of “how much to draw,” then “Authentic Details” addresses the question of “how finely to draw.” Qwen-Image-3.0 reaches a new height in the rendering precision of micro-level details: 10px small text is clearly legible, pores and hair strands are rendered in fine detail, and skin texture approaches photographic realism. Let’s start with the precise rendering of small text.

\n

Below is a knowledge infographic about whale sharks, containing a large amount of text and illustrations. Qwen-Image-3.0 is able to accurately render every region.

\n

\"\"

\n

Academic papers are the ultimate stress test for small-text rendering — dense LaTeX formulas, subscripts and superscripts, Greek letters, and theorem numbering, where not a single symbol can go wrong.

\n

\"\"

\n

The model renders a full page of an academic paper in the field of algebraic geometry, including multiple lines of complex formula derivations. LaTeX typesetting elements such as superscripts, subscripts, curly braces, fraction bars, and multi-line alignment are all accurately presented, maintaining excellent readability even at small font sizes.

\n

Qwen-Image-3.0 can also generate fine text on realistic paper. The example below is a newspaper generated by Qwen-Image-3.0, in which the model not only accurately generates dense text but also simulates the authentic look of a newspaper.

\n

\"\"

\n

In editing tasks, we can also generate fine text. For example, in the case below, the model produces annotations with a realistic style.

\n

\"Before \"After

\n

The model overlays realistic red handwritten annotations onto the book page — underlines, wavy lines, circles, arrows, and short comments — with natural, fluent handwriting that perfectly simulates the style of a high school student’s class notes.

\n

Beyond the fine reproduction of text and layout, “Authentic Details” also stands out in texture depiction. Below are two portrait photography examples in which the model captures extremely delicate textures.

\n

\"\" \"\"

\n

Beyond portraits, the model can also depict the delicate textures of other objects.

\n

\"\" \"\" \"\" \"\"

\n

In editing tasks, we can also generate images with rich details.

\n

\"image1\" \"image2\" \"image3\" \"After

\n

Given a damaged or incomplete traditional painting, the model can restore the missing parts while faithfully maintaining the original artistic style and brushwork.

\n

\"Before \"After

\n

The model completes the restoration of the eagle-combat painting with brushwork consistent with the original, preserving the ink-wash gradients, feather texture, and compositional balance while removing mold spots and signs of damage.

\n

Deep Knowledge

\n

“Rich Content” answers “how complex can it draw,” “Authentic Details” answers “how lifelike can it draw,” and “Deep Knowledge” answers “how broadly can it draw.” Qwen-Image-3.0 possesses rendering capabilities covering 12 languages, multiple fonts, 100+ artistic styles, and a variety of UI interfaces — all backed by the model’s deep understanding of world knowledge.

\n

In the three examples below, the model accurately renders Japanese, Korean, and Spanish respectively. \"\"

\n

\"\" \"\"

\n

Beyond accurate rendering of multiple languages, the model also possesses rich world knowledge. In particular, the model can generate various realistic UI interfaces. \"\"

\n

\"\"

\n

We can also leverage the model’s powerful world knowledge to create infographics. In the example below, we generate a complex infographic based on a real image.

\n

\"Before \"After

\n

While preserving the main subject of the original insect photograph, the model adds professional elements such as taxonomic information, morphological annotations, magnified detail views, and a scale bar, producing a research figure ready for direct use in academic publication.

\n

In addition to the world knowledge the model already possesses, it can also connect to the internet to retrieve the latest world knowledge. For example, we can ask the model to generate a weather forecast image for Hangzhou on July 21.

\n

\"After

\n

The model can also find specific IP figures and create based on them. For instance, we can generate an image of Qi Baishi and Van Gogh introducing Qwen-Image-3.0 in a livestream room.

\n

\"After

\n

Conclusion

\n

From the “Precision” of Qwen-Image-1.0, to the “Precision, Variety, Completeness, Beauty, and Authenticity” of Qwen-Image-2.0, and now to the “Real” of Qwen-Image-3.0 — the goal we have always pursued is to move image generation from “usable” to “practical,” and from “good-looking” to “useful.”

\n

Supported by its three core features — “Rich Content, Authentic Details, and Deep Knowledge” — Qwen-Image-3.0 achieves significant breakthroughs in high-value productivity scenarios such as newspaper PDFs, short-drama storyboards, and complex UI interfaces. We believe that as the capabilities of image generation models continue to improve, they will unlock genuine productivity value in even more fields, including design, content creation, education, and e-commerce.

\n

That concludes the main highlights of this update. We hope you enjoy using Qwen-Image-3.0!

\n", "date_published": "2026-07-21T06:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen-image-3.0", "id": "urn:aggr:item:7264c4ad3b2acf74d79f1c31c6edb6607796464f", "image": "https://img.alicdn.com/imgextra/i4/O1CN01xIf9ZH22o5MUhuDYF_!!6000000007166-2-tps-1590-954.png", "summary": "We are launching Qwen-Image-3.0, the third-generation foundational image generation model in the Qwen-Image series. If the keyword for Qwen-Image-1.0 was “Precision”, and the keywords for Qwen-Image-2.0 were “Precision, Variety,…", "tags": [ "ai", "release" ], "title": "Qwen-Image-3.0: Rich Content, Authentic Details, Deep Knowledge", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-07-21-qwen-image-3-0-rich-content-authentic-details-deep-knowledge/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"\"

\n

PAPER GITHUB HUGGING FACE MODELSCOPE

\n

Today we release Qwen-AgentWorld, a native language world model that simulates agent environments across seven domains:

\n\n

Together with the model, we release AgentWorldBench, a seven-domain evaluation benchmark with paired ground-truth observations from real environments. Both are available on Hugging Face and ModelScope.

\n
\n

Language agents are trained to act in interactive environments, but no language model has been explicitly trained to model the environments themselves — to predict what happens next given the current state and an agent’s action.

\n
\n

Roadmap: Qwen-AgentWorld represents our attempt to investigate how world modeling built on language models can further push the boundaries of general agent capabilities.

\n
\n

We explore both how to achieve language world modeling and how to apply it to advance general agents:

\n\n

\"Qwen-AgentWorld

\n

Qwen-AgentWorld: a native language world model across seven unified domains, with two complementary paradigms for enhancing general agents.

\n

Interactive Demo

\n

Explore real agent-environment conversations simulated by Qwen-AgentWorld across all seven domains. Click any thinking trace to see the model’s internal reasoning.

\n

Qwen-AgentWorld Demo — 7 Domains (Terminal, Search, MCP, SWE, Android, Web, OS)

\n
\n

Part I: Building the Foundation Model for Agentic Environment Simulation

\n

Seven Domains, One Model

\n

Qwen-AgentWorld covers seven categories of interactive environments. For the three GUI domains, environment observations take the form of renderable code (accessibility tree XML, HTML, UI hierarchy markup) rather than pixel frames, enabling text-only world modeling of visual environments.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DomainWhat the LWM simulatesRepresentative prediction
Text Environments
TerminalCommand-line environment: shell output, file system state, process behaviorComplete shell output for multi-step command pipelines
SearchSearch engine results: URLs, snippets, rankings, page contentRealistic URL identifiers, natural source ranking order, query-specific factual detail
MCPAPI server responses: tool call results, database state, service protocolsCross-call schema consistency across nine sequential Notion API calls
SWEIDE / code editing environment: git diff, test results, compilation errorsFile modifications and test outcomes for code changes
GUI Environments
WebBrowser DOM state changes after user interactionsHTML + accessibility tree updates
AndroidAndroid UI hierarchy changes after touch/gesture actionsUI hierarchy XML markup
OSDesktop OS state: file system, window management, application behaviorAccessibility tree XML updates
\n

Training Pipeline

\n

\"Training

\n

Three-stage training pipeline: CPT injects environment knowledge, SFT activates next-state-prediction reasoning, RL sharpens simulation fidelity.

\n

Qwen-AgentWorld is trained end-to-end with environment modeling as the explicit objective from continual pre-training onward. The three-stage pipeline follows one principle: CPT injects, SFT activates, RL sharpens.

\n

Stage 1: Continual Pre-Training (CPT) injects environment knowledge through non-thinking trajectories. The data draws from dedicated agent infrastructure (containerized execution sandboxes, MCP servers, Android/web/OS emulators), open environment interaction traces, and in-house agentic trajectories. Beyond environment data, we incorporate specialized-domain world knowledge corpora spanning industrial control, cybersecurity, law, medicine, finance, and current affairs. A key contribution is turn-level information-theoretic loss masking: four surface-level statistics per (action, observation) pair identify turns carrying genuine environment information, and mask the rest from the loss while retaining them as context.

\n

Stage 2: Supervised Fine-Tuning (SFT) activates next-state prediction as an explicit thinking pattern via <think>...</think> blocks. We use rejection sampling to select high-quality thinking trajectories, resulting in 7,094 training samples.

\n

Stage 3: Reinforcement Learning (RL) sharpens output quality with hybrid rewards. We use GSPO for RL training. The reward combines a rubric-based LLM judge evaluating multi-dimensional quality with rule-based verifiers for domains where exact correctness can be checked programmatically.

\n

AgentWorldBench

\n

\"AgentWorldBench

\n

Overview of AgentWorldBench: domain distribution, source benchmarks, evaluation dimensions, and per-domain trajectory statistics.

\n

To evaluate language world models, we introduce AgentWorldBench, a comprehensive benchmark constructed from real-world observations of 5 frontier model trajectories on 9 established benchmarks, such as Tool Decathlon, Terminal-Bench 1.0 & 2.0, and OSWorld-Verified. Every evaluation sample is paired with a ground-truth observation obtained from real environment execution, enabling reference-grounded scoring. AgentWorldBench evaluates world modeling quality through open-ended rubric judging across 5 dimensions — format, factuality, consistency, realism, and quality — probing the reasoning, knowledge, and long-context capabilities.

\n

Performance

\n

\"Qwen-AgentWorld

\n

AgentWorldBench results: five-dimensional rubric mean per domain. Qwen-AgentWorld-397B-A17B achieves the highest overall score (58.71), outperforming GPT-5.4 (58.25) and other frontier models.

\n

Qwen-AgentWorld-397B-A17B achieves the highest overall average (58.71), surpassing GPT-5.4 (58.25) and all other frontier models. The advantage is most pronounced on Terminal and SWE, the two domains where predictions require accurate modeling of code execution state and tool API behavior.

\n

At the 35B-A3B scale, the three-stage pipeline lifts the overall average by +8.66 points (47.73 → 56.39), bringing Qwen-AgentWorld-35B-A3B above Claude Sonnet 4.6 (56.04). The improvement is consistent across both text and GUI domains.

\n

Inside the World Model’s Mind

\n

Beyond aggregate performance, what makes a language world model interesting is how it reasons. We analyze 129 thinking traces across four text-based domains and find three emergent reasoning patterns.

\n

\"Qwen-AgentWorld

\n

LWM reasoning patterns: deliberative self-correction, information leakage prevention, and multi-step causal reasoning.

\n

Deliberative self-correction. The model uses “Wait!” as a cognitive interrupt to revise intermediate predictions. Across 129 turns, we count 1,347 such interrupts (10.4 per turn), spanning factual errors, epistemological limits (“I cannot actually execute np.random.seed(42)”), and perspective-taking.

\n

Information leakage prevention. In the Search domain, the model holds a reference answer that the agent is trying to find. When the query is unrelated, the model prevents leakage by ensuring snippets do not accidentally reveal the target — the world-model equivalent of theory of mind.

\n

Multi-step causal reasoning. Predicting the output of curl -s localhost:3000 | python3 -m json.tool requires a six-step chain: Node.js missing → server never started → no listener on port 3000 → curl fails silently → empty pipe → json.tool raises JSONDecodeError.

\n
\n

Part II: Investigating the Role of World Modeling in Agent Training

\n
\n

We investigate two complementary paradigms through which world modeling enhances general agents.

\n
\n

Why World Modeling Matters for Agents?

\n

Not to Replace Real Environments, Not for Cost Reduction, but as a Complementary Axis for Pushing the Frontier

\n

What a language world model does. In an agent-environment interaction loop, the policy decides what to do and the world model predicts what happens next. A language world model takes the current interaction history and an agent’s action, and predicts what the environment would return: the terminal output, the API response, the updated DOM. This is not template-based generation. Faithful simulation requires multi-step causal reasoning (chaining six system-knowledge steps to predict a curl pipeline failure), stateful tracking (maintaining referential integrity across nine sequential Notion API calls), and domain-specific knowledge (Unix semantics, API schemas, browser rendering rules).

\n

Why not just/only use real environments? Real-environment interaction remains the gold standard for grounding agent behavior. Language world models are not designed to replace it, nor are they primarily a cost-reduction measure. Instead, LWMs open a complementary axis to supplement real environments:

\n

(1) Scalability and controllability beyond real environments. An LWM enables turn-level scaling of diverse environments without dedicated infrastructure (sandboxes, GUI virtual machines), spanning extreme scenarios, real-world tasks, and high-value professional domains where real execution is infeasible due to irreversible operations or proprietary deployments. Beyond scalability, LWMs offer precise controllability: targeted perturbations that are rare or absent in real environments can systematically expose agent weaknesses. Training against these perturbations helps agents handle edge cases that real-environment training alone cannot cover, ultimately surpassing agents trained solely in real environments.

\n

(2) Internalized world prediction as an agent capability. A capable general agent should possess both decision-making and world-modeling abilities. World modeling enables agents to predict future environment states to refine action selection, effectively performing mental simulation as an internal planning step — whereas traditional agent training focuses solely on state-to-action decision-making. Next-state prediction is thus internalized as a meta-reasoning pattern similar to “reflection” but oriented toward the future: predict before you act. Furthermore, accurate next-state prediction itself requires reasoning, knowledge, instruction following, and long-context handling — capabilities that are foundational to general agents.

\n

What makes general-purpose language environment simulation possible? Building a general-purpose language world model requires three ingredients working together. First, environment diversity: training on trajectories from as many distinct environments as possible, so the model encounters the full spectrum of state-transition patterns rather than memorizing a narrow set. Second, cross-domain generalization: our experiments show that training on a single text domain yields gains on all other text domains, suggesting shared underlying environment modeling capabilities that compound as domain coverage grows. Third, world knowledge through CPT: environment trajectories alone cannot provide the factual grounding needed for faithful simulation. Simulating a regulatory compliance platform requires legal knowledge; simulating search-engine responses on current events requires up-to-date factual coverage. By incorporating specialized-domain world knowledge corpora (industrial control, cybersecurity, law, medicine, finance, current affairs) during continual pre-training, the model acquires the factual substrate on which environment simulation depends. These three ingredients, environment diversity, cross-domain transfer, and world knowledge, together enable a single model to serve as a general-purpose simulator across seven agent interaction domains.

\n

Paradigm I: Decoupled Simulation

\n

As a standalone simulator, where the policy agent and the world model are separate models, Qwen-AgentWorld provides scalability and controllability that real environments cannot. In this Sim RL setup, the world model replaces the real environment during agent RL training: the agent acts, the world model predicts the next observation, and the agent learns from these simulated rollouts. Key findings:

\n\n

Generalizable Environment Scaling

\n

We test whether the world model generalizes to environments entirely absent from training. OpenClaw is an open-source agent platform whose tasks span scheduling, coding, email triage, browser automation, and file management — entirely out of distribution for Qwen-AgentWorld. We simulate 4,000 OpenClaw environments for agent RL training, without any domain-specific adaptation, and also ablate the simulator itself: using Qwen3.6-Plus as the simulator yields negligible improvement, while Qwen-AgentWorld-397B-A17B produces substantial gains — confirming that world-model quality is the bottleneck for Sim RL. The agent learns little from interacting with an unfaithful simulator.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Claw-EvalQwenClawBench
Qwen3.5-35B-A3B65.447.9
+ Sim RL (w/ Qwen3.6-Plus)66.747.8
+ Sim RL (w/ Qwen-AgentWorld-397B-A17B)69.755.0
Δ+4.3+7.1
\n

* All scores averaged over 3 independent rollouts with 256K maximum sequence length.

\n

Controllable Simulation

\n

The more powerful capability is controllability: using natural-language instructions to shape the simulator’s behavior during training. We validate two modes.

\n

MCP: environment adaptation. We synthesize simulation system prompts from real MCP tool-use trajectories: each prompt specifies the tool schemas and server configuration, summarizes the hidden environment state (database contents, permission settings, service availability), and defines controllable simulation instructions that shape how the simulator responds at each turn. Control instructions inject targeted perturbations — intermittent API errors, paginated responses requiring follow-up calls, incomplete intermediate results that force multi-step retrieval, and partial failures in batch operations — to systematically expose agent weaknesses that real deployments rarely produce.

\n

The results reveal a sharp contrast: standard Sim RL without control instructions provides no meaningful gain (Tool Decathlon actually drops from 32.4 to 31.5), because the simulator lacks sufficient grounding to produce faithful responses. With controllable simulation, Tool Decathlon improves by +3.7 and MCPMark by +12.3. Controllability is not merely a factor in the magnitude of improvement — it is a prerequisite for Sim RL to work at all in this domain. The larger gain on MCPMark (+12.3 vs. +3.7) suggests that controllable simulation is especially effective for tasks requiring many sequential tool calls and careful handling of intermediate results.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Tool DecathlonMCPMark
Qwen3.5-35B-A3B-SFT32.421.5
+ Sim RL (uncontrolled)31.524.6
+ Sim RL (controlled)36.133.8
Δ+3.7+12.3
\n

Search: fictional-world construction. We construct 1,000 self-contained fictional environments, each anchored by a relational database (300–500 rows) of internally consistent fictional facts. A time-shifted environment might contain a 2029 smartphone market ranking with real brand names but non-existent model numbers. Since answers exist only within the fictional setting, the agent cannot bypass the search tool by answering from parametric memory; since all facts are invented, the agent cannot confuse simulated facts with real-world knowledge.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
F1 by ItemF1 by Row
Qwen3.5-35B-A3B-SFT34.0213.72
+ Sim RL (controlled)50.3124.21
Δ+16.29+10.49
Qwen3.5-397B-A17B-SFT70.1145.69
+ Sim RL (controlled)73.9851.74
Δ+3.87+6.05
\n

Sim RL vs. Real RL

\n

\"Sim

\n

Sim RL vs. Real RL on WideSearch: controllable Sim RL tracks or slightly exceeds Real RL trained with a live search engine.

\n

Performance. We directly compare controllable Sim RL against Real RL (trained with a live search engine) on WideSearch. Sim RL tracks or slightly exceeds Real RL: F1 by Item reaches 50.3% at step 60, compared to 45.6% for Real RL.

\n

\"Sim

\n

Tool usage divergence: Sim-RL-trained agents increase web_extractor calls while Real-RL-trained agents decrease them, reflecting how controllable simulation shapes distinct agent behaviors.

\n

Behavior. The more informative signal comes from agent behavior. Both regimes reduce web_search calls from ~5 to ~3.5 per trajectory, but web_extractor calls diverge sharply: Sim RL increases usage from 2.5 to 4.0, while Real RL decreases from 2.5 to 1.5. Because the simulated snippets deliberately withhold detailed content, the Sim-RL-trained agent learns that extracting full pages is necessary for assembling complete answers. Controllable simulation shapes agent behavior in targeted ways that real environments cannot.

\n

Paradigm II: Agent Foundation Model

\n

In Paradigm I, the agent and world model are separate models. Here we unify them: the same model that selects actions also predicts environment states. LWM training instills next-state prediction as an internalized reasoning capability. Key findings:

\n\n

We validate this by running LWM RL on Qwen3.5-35B-A3B-SFT — a single-turn task with no tool calls — then evaluating directly on multi-turn, tool-calling agentic tasks across seven benchmarks without additional fine-tuning, including three out-of-domain benchmarks absent from LWM training.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
In DomainOut of Domain
Base33.364.542.233.453.639.862.3
+ LWM RL39.667.947.446.264.949.471.3
Δ+6.3+3.4+5.2+12.8+11.3+9.7+9.0
\n

The out-of-domain results are particularly notable: the LWM training pipeline contains no Claw or function-calling data, yet gains of +11.3, +9.7, and +9.0 emerge on domains entirely absent from world-model training.

\n
\n

Build with Qwen-AgentWorld

\n

Deployment

\n

We have open-sourced Qwen-AgentWorld-35B-A3B (Hugging Face, ModelScope), a language world model built on a MoE architecture with 35B total parameters / 3B active parameters, supporting a 256K context window. It can be deployed and used in the following ways.

\n
# SGLang\npython -m sglang.launch_server \\\n    --model-path Qwen/Qwen-AgentWorld-35B-A3B \\\n    --port 8000 \\\n    --tensor-parallel-size 4 \\\n    --context-length 262144 \\\n    --reasoning-parser qwen3\n\n# vLLM\nvllm serve Qwen/Qwen-AgentWorld-35B-A3B \\\n    --port 8000 \\\n    --tensor-parallel-size 4 \\\n    --max-model-len 262144 \\\n    --reasoning-parser qwen3 \\\n    --trust-remote-code\n
\n
from transformers import AutoModelForCausalLM, AutoTokenizer\n\nmodel = AutoModelForCausalLM.from_pretrained(\n    "Qwen/Qwen-AgentWorld-35B-A3B",\n    torch_dtype="auto",\n    device_map="auto",\n)\ntokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen-AgentWorld-35B-A3B")\n
\n

Evaluation

\n

AgentWorldBench is available on Hugging Face and Modelscope as per-domain JSONL files, each containing interaction trajectories with ground-truth observations from real environments. Evaluation uses a three-step pipeline via eval/eval.py: (1) infer — run the world model to generate predicted observations, (2) judge — score each prediction against the ground truth across five dimensions (format, factuality, consistency, realism, quality) using an LLM judge, (3) aggregate — compute per-domain and overall scores. Both the world model and the judge use OpenAI-compatible APIs, supporting SGLang, vLLM, or proprietary endpoints. See the GitHub README for full setup, data format, and example commands.

\n

Summary

\n

Qwen-AgentWorld is a native language world model covering seven agent interaction domains within a single model at two scales (35B-A3B and 397B-A17B). A three-stage recipe, CPT injects environment knowledge, SFT activates next-state-prediction reasoning, RL sharpens simulation fidelity, progressively builds world-modeling capability from the ground up. We investigate two complementary paradigms through which world modeling enhances general agents. As a decoupled simulator, we validate the effectiveness of controllable simulation on Tool Decathlon, MCPMark, and WideSearch, surpassing both uncontrolled simulation and real-environment training. As a unified agent foundation model, LWM warm-up transfers to multi-turn agentic tasks across seven benchmarks, including three entirely out-of-domain, providing initial validation that language world models can serve as a foundation for building stronger agent models. Language world modeling opens a complementary axis for scaling general agents beyond what real-environment interaction alone can provide.

\n

Citation

\n
@article{zuo2026qwen,\n  title={Qwen-agentworld: language world models for general agents},\n  author={Zuo, Yuxin and Xiao, Zikai and Sheng, Li and Huang, Fei and Tu, Jianhong and Liu, Yuxuan and Tang, Tianyi and Hu, Xiaomeng and Su, Yang and Lan, Qingfeng and others},\n  journal={arXiv preprint arXiv:2606.24597},\n  year={2026}\n}\n
\n", "date_published": "2026-06-23T03:30:30+00:00", "external_url": "https://qwen.ai/blog?id=qwen-agentworld", "id": "urn:aggr:item:7a0d02bec9d952bc6d8d6a92217681619cca267c", "image": "https://img.alicdn.com/imgextra/i1/O1CN01HTUzD01pxACTcMZhl_!!6000000005426-2-tps-1590-954.png", "summary": "Today we release Qwen-AgentWorld, a native language world model that simulates agent environments across seven domains: Native world modeling: environment modeling is the training objective from continual pre-training onward (CPT → SFT →…", "tags": [ "ai", "open-source" ], "title": "Qwen-AgentWorld: Language World Models for General Agents", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-06-23-qwen-agentworld-language-world-models-for-general-agents/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

The Qwen family of foundation models already gives strong perception and reasoning about the physical world. But seeing is not acting: the gap between vision and language understanding and physical control remains the central bottleneck for embodied intelligence. The Qwen-Robot Suite bridges this gap with three foundation models — Qwen-RobotNav, Qwen-RobotManip, and Qwen-RobotWorld. Nav unifies five navigation task families through a controllable observation protocol. Manip turns heterogeneous robot data into a coherent canonical space, enabling cross-embodiment training at scale. World co-trains 20+ embodiments via a natural-language action interface under one world model. Together, they enable an agentic system where general intelligence translates directly into physical action.

\n

\"Qwen-Robot

\n

The Qwen family of multimodal foundation models has made remarkable progress in understanding the physical world. Qwen-VL can parse complex spatial relationships, identify objects in cluttered scenes, follow multi-step visual instructions, and reason about physical configurations, giving physical agents a preliminary cognitive foundation. A VLM can already plan in language: “go to the kitchen, find the red cup, pick it up, and place it on the shelf.”

\n

But understanding the physical world is not the same as acting in it. A VLM that can plan those steps cannot produce the motor commands that execute them. This is fundamentally an alignment challenge that language instructions and physical action signals live in different representation spaces, and bridging them requires more than perception alone. What makes this harder is that the embodied data needed to close this gap is fundamentally unlike internet text. It is heterogeneous by nature, expensive to collect, and narrow in diversity. A navigation trajectory, a tele-operated grasp, and a dashcam clip live in incompatible action spaces, observation formats, and embodiments. Naively pooling them produces conflict rather than synergy.

\n

The Qwen-Robot Suite bridges this gap with three foundation models — Qwen-RobotNav, Qwen-RobotManip, and Qwen-RobotWorld — each aligning language with a different domain of physical action. We pursue generalization across language instructions and adherence to physical laws, and have achieved significant progress on both fronts. In this post, we also explore the potential of these models as low-level tools for building general-purpose agentic systems.

\n\n

\"\"

\n

Each has its own technical report and deep-dive blog. This post tells the story of how they fit together.

\n

NAVIGATE

\n

Qwen-RobotNav

\n

The Gateway to Mobility — unifies 5 navigation domains under one model with controllable observation.

\n

Read tech blog →

\n

MANIPULATE

\n

Qwen-RobotManip

\n

The Foundation of Interaction — cross-embodiment alignment on >38K hours of open-source data.

\n

Read tech blog →

\n

IMAGINE

\n

Qwen-RobotWorld

\n

Infinite Worlds — language-driven world model co-training 20+ embodiments across domains.

\n

Read tech blog →

\n
\n

Qwen-RobotNav: The Gateway to Physical Mobility

\n

NAVIGATE

\n

Before an agent can manipulate anything, it has to get there. Mobile navigation spans tasks with fundamentally different memory requirements: instruction following demands long-horizon context while target tracking cares almost entirely about recent frames. No fixed observation strategy serves both.

\n

Qwen-RobotNav, built on Qwen3-VL, addresses this through a parameterised navigation interface with two complementary dimensions: task modes that select the navigation behaviour (instruction following, object search, target tracking, autonomous driving), and controllable observation parameters (token budget, temporal decay, per-camera weights, frame sample mode) that govern how visual history is encoded. Trained on 15.6 million samples with co-training on vision-language data to preserve grounded perception, Qwen-RobotNav unifies five task families under a single set of weights. The parameterised interface also makes Qwen-RobotNav a natural building block for agentic systems. An upper-level planner (Qwen3.7-Plus) decomposes long-horizon goals into sub-tasks and dynamically switches Qwen-RobotNav’s task mode and context strategy mid-episode, composing complex behaviours from repeated calls to the same model. This extends the system to long-horizon reasoning with persistent memory, enabling it to solve complex user intents that require multi-step navigation, evidence gathering, and grounded response generation.

\n

\"Qwen-RobotNav

\n

Highlights:

\n

5 Domains
\n8 SOTAs

\n

One Model Unifies VLN, ObjNav,
\nTracking, Driving & EQA

\n

Context
\n= Interface

\n

4-Axis Observation Protocol
\nZero Architecture Change

\n

Agentic

\n

Navigation as a Tool Call
\n+ Two-Level Memory
\nRaises the new bar on EQA

\n

Generalization

\n

In-the-Wild Single-Camera
\nDeployment in Unseen Environments

\n\n

\"Qwen-RobotNav

\n

Benchmarking Qwen-RobotNav

\n

Deployment

\n

Deployed zero-shot on a Unitree Go2 quadruped (NVIDIA Jetson Thor, 196ms latency) using only the built-in low-resolution camera. The robot executes step-by-step verbal instructions across multiple rooms in a previously unseen apartment.

\n

Instruction Following

\n

We evaluate a back-and-forth navigation task in an unseen exhibition hall: the robot first navigates 21.78 m from a living room to a hospital room following language instructions, then receives a reverse command and must precisely retrace the entire route. This is particularly challenging as it requires the model to maintain spatial awareness over long distances, ground diverse visual landmarks in both forward and reverse directions, and execute accurate bidirectional position control purely from language.

\n

Cross Embodiment

\n

A single set of weights serves both legged robot navigation and autonomous driving. On NAVSIM closed-loop driving, Qwen-RobotNav-4B achieves 91.4 PDMS.

\n

Read the Qwen-RobotNav blog →

\n
\n

Qwen-RobotManip: The Foundation of Physical Interaction

\n

MANIPULATE

\n

Physical agents need to interact with the real world — for example, completing manipulation tasks with robot arms. Yet an industrial arm on a production line and a service arm in a kitchen may perform visually similar grasping motions while having entirely different joint configurations and action spaces. The core challenge is making heterogeneous embodiments representationally compatible, so that scaling across robots and data sources produces synergy rather than conflict.

\n

\"Qwen-RobotManip

\n

Qwen-RobotManip, built on Qwen3.5-4B VL with a flow-matching DiT action head, introduces three mechanisms to solve this. A unified 80-dimensional state-action representation is shared across single-arm, dual-arm, dexterous-hand, and mobile embodiments. Camera-frame end-effector delta pose actions make visually similar motions numerically proximate across robots, abstracting away morphological differences. In-context policy adaptation reads execution history as an implicit embodiment signature for on-the-fly adaptation.

\n

Once the representation framework is unified, the data barrier drops. We train the VLA model on 11,320 hours of open-source robot data, 1,933 hours of open-source egocentric human video, and 24,808 hours of robot demonstrations across 15 embodiments synthesized from the human video via our Human-to-Robot synthesis pipeline — totaling >38,100 hours. Using only open-source data, the model already exhibits emergent generalization capabilities, including robustness to perturbations, zero-shot instruction following, reactive error recovery, and cross-embodiment transfer.

\n

Highlights:

\n

Alignment

\n

Representation · Motion · Behavior
\nThree-Dimensional Alignment

\n

Open-Source Data Only

\n

>38K Hours of Manipulation Data
\nAcross 15 Embodiments

\n

Dominant

\n

OOD Generalization
\nAcross All Benchmarks

\n

#1

\n

RoboChallenge Table30 v1 Generalist Track
\nSweeping Top 2, 20% Ahead of 3rd Place

\n\n

\"Downstream

\n

Key Finding — Alignment is the prerequisite for scale. Only models with unified cross-embodiment representations (UnifiedSpace + UnifiedEEF) exhibit clean log-linear data scaling. Without alignment, adding more data produces erratic or flat curves — scale cannot compensate for a broken formulation.

\n

Diverse Real-World Tasks

\n

A single generalist policy handles complex manipulation across diverse task categories, scenes, and objects.

\n

Instruction Following

\n

Following diverse unseen instructions across real-world settings (top row) and simulation (bottom row).

\n

Cross-Embodiment Transfer

\n

Tasks trained on other embodiments transfer zero-shot to new ones (top row); few-shot demonstrations enable rapid adaptation to entirely new tasks (bottom row).

\n

Read the Qwen-RobotManip blog →

\n
\n

Qwen-RobotWorld: Infinite Robotics Worlds

\n

IMAGINE

\n

Real-world experience is the scarcest resource in robotics. Qwen-RobotWorld addresses this by learning the world’s state transition function directly: given the current observation and a natural-language action, it predicts what the world will look like next. The key design choice is expressing all actions in natural language — this converts end-effector poses, steering commands, and navigation waypoints into a single interface, enabling 20+ embodiment types and 500+ action categories to be co-trained under the Embodied World Knowledge corpus (8.6M video-text pairs, 200M+ frames). A 60-layer dual-stream MMDiT couples Qwen2.5-VL’s semantic representations with video latents. Using a full multimodal LLM as the action encoder — rather than a lightweight text encoder — is load-bearing: it brings internalized world knowledge that arms are rigid bodies, fluids spread, and objects fall, implicitly constraining generation toward physically plausible futures. Each domain reinforces the others: manipulation teaches contact physics, driving teaches 3D geometry, navigation teaches room-scale spatial reasoning.

\n

\"Qwen-RobotWorld\"

\n

Highlights:

\n

Top-Tier

\n

Across
\n4 Benchmarks

\n

20+

\n

Robot Embodiments
\nUnified

\n

8.6M

\n

Cross-Scenario
\nTraining Pairs

\n

1300+

\n

Manipulation
\nSkills

\n\n

Any Instruction Following

\n

Changing a single keyword — object, destination, or action verb — produces a correspondingly different future. The world model truly understands language, not just pattern-matches.

\n

Different Object

\n

Pick up red strawberry

\n

⇄

\n

Pick up yellow potato

\n

Different Embodiment

\n

Assemble camera parts

\n

⇄

\n

Assemble camera parts

\n

Different Destination

\n

Place pen on wooden tray

\n

⇄

\n

Place pen on white paper

\n

Different Action

\n

Extend glue forward

\n

⇄

\n

Place glue into penholder

\n

Multi-View Consistent Generation

\n

Given a single instruction, Qwen-RobotWorld generates temporally and spatially consistent videos across multiple camera viewpoints — critical for sim-to-real transfer and multi-camera policy training.

\n

View More Multi-View Examples (16 more)

\n

Human → Robot Transfer

\n

Given a human demonstration, Qwen-RobotWorld generates realistic robot execution across diverse embodiments — no teleoperation required.

\n

ARX-L5

\n

Human Demo

\n

→

\n

Robot Execution

\n

xArm7

\n

Human Demo

\n

→

\n

Robot Execution

\n

Franka Panda

\n

Human Demo

\n

→

\n

Robot Execution

\n

Sawyer

\n

Human Demo

\n

→

\n

Robot Execution

\n

View More Embodiments (Kinova Gen3, Piper, KUKA iiwa, Kinova Jaco)

\n

Kinova Gen3

\n

Human Demo

\n

→

\n

Robot Execution

\n

Piper

\n

Human Demo

\n

→

\n

Robot Execution

\n

KUKA iiwa

\n

Human Demo

\n

→

\n

Robot Execution

\n

Kinova Jaco

\n

Human Demo

\n

→

\n

Robot Execution

\n

Autonomous Driving & Indoor Navigation

\n

Driving teaches large-scale 3D geometry and multi-agent dynamics; navigation teaches room-scale spatial reasoning. Each domain reinforces the others.

\n

Driving

\n

Indoor Navigation

\n

Read the Qwen-RobotWorld blog →

\n
\n

From Models to Agents: Closing the Loop

\n

Each model is independently useful — but because all three expose language-first interfaces, general-purpose Qwen models can compose with them as physical-world tools, connecting general intelligence to physical action. We have an in-hourse project Qwen-RobotClaw, a robotics agent harness that allows Qwen VLM agents to call Qwen-Robot Suite models as physical-world tools while properly managing the context and memory required by long-horizon tasks, pushing physical intelligence toward more general and more complex real-world applications. Here are early examples of what this makes possible.

\n

Open-Ended Task Execution

\n

Qwen-Omni observes the scene, randomly proposes manipulation tasks via speech, and judges execution in real time. Each video shows Qwen-RobotManip completing tasks on the fly with no pre-defined task list — demonstrating that a general-purpose multimodal model can serve as the task proposer and evaluator, while the suite model handles physical execution.

\n

Long-Horizon Manipulation

\n

We develop a VLM-driven agentic VLA system, in which the base Qwen-3.5 model serves as the high-level planner and Qwen-RobotManip handles low-level execution. Leveraging its capabilities in scene understanding, spatial reasoning, and task progress assessment, Qwen-3.5 decomposes a complex high-level instruction into a sequence of atomic subtasks, which are then executed by the VLA. This division of labor significantly improves robustness to OOD scenes and instructions.

\n

We illustrate this with a table-cleaning task on a cluttered tabletop with a basket. Given such a fully OOD scene and abstract instruction, directly using the VLA model exhibits clearly abnormal behavior (right). With Qwen-3.5 as the planner, the system decomposes the task into fine-grained atomic subtasks in real time (left), allowing the VLA to focus on one simple step at a time and demonstrating compositional generalization.

\n

We also observe that subtask decomposition helps the system recover from failure-and-retry loops. When the high-level VLM detects that execution has stalled, it replans by issuing a new subtask, enabling the system to resume progress and ultimately complete the task successfully.

\n

Agent + VLA (success)

\n

VLA alone (failure)

\n

Agentic Navigation & Embodied QA

\n

By combining the agent system with Qwen-RobotNav, we achieve a substantial improvement over previous state of the art on long-horizon 3D physical-world exploration tasks, including Embodied Question Answering benchmarks such as HM-EQA, MT-HM3D, and EXPRESS-Bench. We can also deploy the same open-world exploration capability in real environments, as shown in the demos below. We will release more technical details in future updates.

\n

\"Agentic

\n

In the first demo, the user asks the agent to find an open restroom in a real building. The agent scans the environment, follows corridor-level cues to search for restroom signage, discovers that the first restroom is unavailable due to a visible "Cleaning in Progress / 暂停使用" sign, and then replans to search for an alternative on the other side of the building. After verifying from visual evidence that the second restroom is open and accessible, it returns an evidence-grounded answer.

\n

In the second demo, which was previously released together with Qwen3.7-Max, the agent calls Qwen-RobotNav to autonomously explore an open campus environment, correct its deviations along the way, and finally recover a lost umbrella.

\n
\n

Chat2Robot

\n

We provide an experimental feature — Chat2Robot — where you can chat with a robot directly in your browser. Simply type a natural-language instruction, and watch the robot respond in real time. Give it a try and experience Qwen-Robot Suite in action!

\n
\n

Note: Chat2Robot currently supports Qwen-RobotManip only. The deployed policy is trained solely on the RoboTwin-Clean dataset, which contains only 50 tasks — it is not a perfect policy. Our goal here is to demonstrate a degree of zero-shot instruction following capability. This feature is still under active development and may not be fully polished — we welcome your feedback and suggestions!

\n
\n

\"Chat2Robot

\n

We thank D-Robotics (Digua) for supporting this feature.

\n
\n

What’s Next

\n

Physical world intelligence is still in its infancy. Contact-rich long-horizon tasks, lifelong learning, tighter integration between general-purpose planners and physical-world executors, and richer human-robot-environment interaction all remain open. But the path is becoming clear: start from strong multimodal understanding, bridge the vision-language representation space into each type of physical action, scale the training, and demand generalization.

\n

A physical agent that can go anywhere, do anything, and foresee what comes next.

\n

That is the destination — and the Qwen-Robot Suite is our first full step towards it.

\n
\n

Citation

\n
@article{qwenrobotnav,\n  title={Qwen-RobotNav: A Scalable Navigation Model Designed for an Agentic Navigation System},\n  author={Qwen Team},\n  year={2026}\n}\n\n@article{qwenrobotmanip,\n  title={Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models},\n  author={Qwen Team},\n  year={2026}\n}\n\n@article{qwenrobotworld,\n  title={Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation},\n  author={Qwen Team},\n  year={2026}\n}\n
\n", "date_published": "2026-06-16T02:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen-robotsuite", "id": "urn:aggr:item:14090f9dabb95d2c025fd3d180b449e274f256db", "image": "https://img.alicdn.com/imgextra/i3/O1CN01jzXzt71RYqOIWUmj4_!!6000000002124-2-tps-1590-954.png", "summary": "The Qwen family of foundation models already gives strong perception and reasoning about the physical world. But seeing is not acting: the gap between vision and language understanding and physical control remains the central bottleneck…", "tags": [ "ai", "release" ], "title": "Qwen-Robot Suite: A Foundation Model Suite for Physical World Intelligence", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-06-16-qwen-robot-suite-a-foundation-model-suite-for-physical/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"Qwen-RobotNav

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\"Qwen-RobotWorld\"

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Embodied intelligence requires agents to perceive, reason about, and act within physical environments. World models offer a scalable path forward — but current approaches face a fundamental tension. General video generation models learn rich visual priors but lack the ability to model embodied physics. Domain-specific embodied models are tailored to individual scenarios and cannot generalize across embodiments.

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Qwen-RobotWorld bridges this gap by treating natural language as a universal action interface. A single instruction like "pick up the red cup and place it on the shelf" implicitly encodes the complete action sequence, goal state, and physical constraints — no robot-specific control interface needed. This allows manipulation, autonomous driving, and indoor navigation to be trained jointly, with each domain's physical knowledge reinforcing the others.

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Language unifies the action space: world knowledge and embodied knowledge reinforce each other within a single model, enabling cross-scenario, cross-task physical generalization.

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Key Highlights

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Top-Tier Across 4 Benchmarks

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20+ Robot Embodiments Unified

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8.6M Cross-Scenario Training Pairs

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1300+ Manipulation Skills

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Model Architecture

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Dual-Stream Diffusion World Model

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\"Qwen-RobotWorld

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Qwen-RobotWorld adopts a dual-stream Multimodal Diffusion Transformer (MMDiT):

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The two streams interact via joint attention at every layer, enabling bidirectional cross-modal fusion throughout the denoising process.

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Using an MLLM as the action encoder — rather than lightweight encoders like T5 or CLIP — provides two key advantages: (1) deep language understanding accurately parses complex, compositional instructions into precise condition signals; (2) internalized world knowledge (e.g., that robot arms are rigid bodies with fixed joint constraints) implicitly constrains physically plausible transitions, preventing common failure modes like object deformation across frames.

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Scene2Robot: Human-to-Robot Transfer

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Scene2Robot enables cross-embodiment video editing: human demonstrations are retargeted to 14 robot morphologies via a multi-segment conditioning mechanism, where joint attention allows the generation to simultaneously attend to scene appearance and robot motion trajectory. This capability both serves as a data scaling engine during training and enables human-to-robot transfer at inference time.

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\"Scene2Robot

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Multi-View Geometrically Consistent Generation

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Single-camera observation inevitably occludes critical contact and spatial details. Qwen-RobotWorld generates 2–4 synchronized camera streams — main view, wrist-mounted views, and third-person views — with geometrically consistent object identity and motion across all viewpoints. During training, synchronized frames from multiple cameras are spatially concatenated into a single input; the model generates all views simultaneously, with asymmetric 3D RoPE providing spatial encoding and attention layers naturally establishing cross-view correspondence — without any architectural modification. This cross-view consistency further acts as a geometric regularizer, teaching the model object shape, depth, and spatial layout.

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Data: Embodied World Knowledge

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EWK Dataset

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\"Embodied

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The Embodied World Knowledge (EWK) dataset is organized along four complementary axes, each targeting a distinct source of physical variation:

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View Detailed Dataset Inventory

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
DatasetEmbodimentViewsContribution
Manipulation (~5.9M samples)
EgoHOD, EPIC-Kitchens, Egocentric-10kHuman handsEgocentricDexterity & coordination prior
Bridge V2, RH20T, DroidSingle-arm grippers3rd-person + wristInteraction primitives
Robomind, RoboCoinSingle/dual-arm, humanoidsEgo + 3rd-personCross-embodiment generalization
Agibot-World, GalaxeaSingle-arm (gripper + dexterous)Synced ego + wrist + 3rdTemporal & multi-view consistency
Qwen-Aloha (internal)Dual-arm grippersHead + dual wristMulti-view grasping prior
ActionNet, OpenLoongDexterous handsWrist + 3rd-personFine-grained dexterity
Autonomous Driving (~200K samples)
Waymo, NVIDIA PhysicalAI-AD, Bench2Drive, SekaiEgo vehicleSurround-viewLarge-scale ego-motion & multi-agent dynamics
Indoor Navigation (6K+ episodes)
VLNVerseMobile agentEgocentricRoom-scale spatial reasoning
Human-to-Robot Transfer
Scene2Robot (synthesized)14 robot morphologiesMulti-viewCross-embodiment video editing
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Action-Language Mapping

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The central challenge in building a universal world model is representational heterogeneity: manipulation uses joint angles, driving uses steering commands, navigation uses heading vectors — each requiring a separate model. Our action-language mapping framework resolves this by projecting all action signals onto a shared natural language space, so that videos from a Franka gripper, an autonomous vehicle, and a navigation agent all become instances of the same language-conditioned video generation task.

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A hierarchical five-layer annotation pipeline ensures caption quality and precision:

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1

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Task Goal High-level intent — what should change between states

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2

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Action Detail Spatio-temporal trajectories with explicit viewpoint declaration

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3

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Physical Feedback Observable consequences on the environment

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4

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Comprehensive Caption Full description for precise prediction

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5

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Concise Caption Essential elements for brief task-level commands

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During training, comprehensive and concise descriptions are sampled with equal probability, so the model handles both detailed trajectory specifications and brief task-level commands.

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Training

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Training follows a general-to-expert progressive curriculum:

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StagePhaseData MixObjective
PretrainingT2I / T2V / TI2V jointGeneral dataBuild foundational visual priors
Human interactionEgo4D, EPIC-Kitchen, etc.Grasping & tool-use priors
SFTPhase 1: Single-view manipulationEmbodied + general joint trainingCore manipulation physics
Phase 2: Multi-view expansionBroaden viewpoint coverage
Phase 3: Multi-view concatenationCross-view geometric consistency
Phase 4: Complex cross-domainLong-horizon & cross-scenario
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Pretraining on general data and human interaction videos (Ego4D, EPIC-Kitchen) builds broad visual priors — the T2I task specifically anchors object geometry that transfers to video generation through the shared backbone. SFT then progressively deepens embodied expertise across four phases while keeping general data in every batch, ensuring both capabilities advance together rather than trade off.

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Demos

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Fine-Grained Language Grounding

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Given identical initial frames, the model produces qualitatively distinct videos when a single keyword differs. It also handles complex, multi-step instructions requiring long-horizon reasoning.

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Contrastive Instruction Following:

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L: Pick up the red strawberry
\nR: Pick up the yellow potato

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L: Place pen on the wooden tray
\nR: Place pen on the white paper

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L: Hand glue to the person
\nR: Place glue into the penholder

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Complex Multi-Step Instructions:

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Sequentially pick up the red and yellow bell peppers, place them on the table from left to right

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Grab the yellow-and-blue stacked block, position it above the green-and-blue stacked block

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Cross-Domain Generalization

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(A) Cross-Embodiment:

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(B) Cross-Task × Cross-Environment:

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(C) Multi-View Consistency:

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(D) Zero-Shot Robustness:

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Ours

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LVP

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Cosmos2.5-14B

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Human-to-Robot Transfer

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The Scene2Robot mechanism preserves task intent from a human demonstration (left) while adapting motion to embodiment-specific kinematic constraints (right).

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Beyond Manipulation: Driving and Navigation

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The learned world model generalizes beyond robot manipulation to broader mobility scenarios.

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Autonomous Driving. Generated driving episodes from Bench2Drive, NVIDIA PhysicalAI-AD, Sekai, and Waymo demonstrate coherent scene dynamics and vehicle behaviors.

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Indoor Navigation. Egocentric navigation episodes from VLNVerse show the model’s ability to simulate first-person movement through complex indoor environments.

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From tabletop manipulation to autonomous driving and indoor navigation — Qwen-RobotWorld demonstrates that a unified world model can generalize beyond a single morphology or scenario family.

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Performance

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We evaluate against general video generation models (Sora2, Veo3, Wan2.6, Kling, LTX-2) and embodied world models (Cosmos, LVP, GigaWorld, Vidar, Wow) across four benchmarks.

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\"Benchmark

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EWMBench

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4.60

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Embodied motion fidelity

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DreamGen

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4.952

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Instruction following & physics alignment

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WorldModelBench

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8.99

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Physical reasoning & instruction following

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PBench

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0.804

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Physical behavior evaluation

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EWMBench

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
TypeModelSceneCHSDDynnDTWDiversityBLEUCLIPLogicsOverall
GeneralVeo30.8420.2130.1930.1610.0220.2140.8970.9473.49
Wan2.60.6710.2030.0900.1720.0500.1620.8741.0003.22
Kling0.8210.3270.1820.3420.0170.2590.9011.0003.85
LTX-20.7850.2080.1280.2440.0120.1430.8870.5002.91
Sora20.8530.2810.3490.2750.0310.2470.9100.9473.89
EmbodiedCosmos0.7960.2500.2050.2530.0800.1230.8460.7333.29
GigaWorld0.8710.3050.0850.2780.0280.2050.8870.9003.56
LVP0.8800.4250.0430.6230.0090.2180.9000.9524.05
Vidar0.7340.1880.1520.1770.0650.1610.8820.9413.30
Wow0.8870.2490.0530.2570.0270.1930.9000.9523.52
Ours0.9140.5660.3430.6710.0110.2080.8831.0004.60
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Strong motion fidelity (HSD 0.566), high scene consistency (0.914), and perfect logic constraint satisfaction.

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DreamGen Bench

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
ModelGR1-Env PAGR1-Env IFGR1-Object PAGR1-Object IFGR1-Behavior PAGR1-Behavior IFTotal
Cosmos-sft0.7090.6550.7750.7200.6490.6214.129
LVP0.8100.7720.7450.8290.7130.8894.758
Vidar0.4450.6470.4780.7260.3940.6513.341
GigaWorld0.6210.9330.5000.8520.4260.8844.216
Wow0.7930.8260.7550.8490.8090.6964.728
Ours0.8280.7930.8400.8780.7810.8324.952
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Strong object-level compositional generalization (GR1-Object IF: 0.878), with consistent physics alignment across all subsets.

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WorldModelBench

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
TypeModelInstr. (0-3)FrameTempNewtonMassFluidPenetr.Grav.Phys.Total
GeneralVeo32.520.980.951.000.890.990.911.004.809.25
Wan2.62.500.990.951.000.890.990.941.004.839.27
Sora22.210.960.931.000.910.990.951.004.848.93
Kling1.590.971.001.001.001.001.001.005.008.55
LTX-21.970.690.620.990.601.000.731.004.327.61
EmbodiedCosmos2.141.000.941.000.921.000.941.004.868.94
LVP2.010.890.911.000.930.990.951.004.878.67
GigaWorld2.130.590.461.000.480.990.690.984.137.31
Vidar1.620.540.451.000.561.000.851.004.407.01
Wow2.050.760.651.000.650.990.811.004.457.91
Ours2.330.870.851.001.001.000.941.004.948.99
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Perfect physics adherence (1.00) across Newton's laws, mass conservation, fluid dynamics, and gravity, with strong instruction following (2.33/3.0).

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PBench

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
TypeModelI2V-BgI2V-SAesImgBg-ConMotSub-ConO-ConQualityDomainOverall
GeneralVeo30.9750.9800.5260.6980.9380.9940.9270.1280.7710.8820.827
Wan2.60.8560.8430.5140.7190.9060.9780.8430.1360.7240.8320.778
Sora20.9810.9730.4870.6720.9610.9940.9540.1290.7690.8410.805
Kling0.9820.9790.5210.6990.9200.9900.9270.1240.7680.8740.821
LTX-20.9480.9550.5060.6220.9320.9860.9040.1180.7460.8450.796
EmbodiedLVP0.9790.9810.5150.6790.9540.9910.9620.1160.7720.8120.792
GigaWorld0.9570.9440.4950.6410.9250.9840.8920.1280.7460.8410.794
Wow0.9670.9570.5170.6890.9410.9800.9290.1110.7610.7860.774
Vidar0.9350.9220.5010.5730.9120.9820.8630.1200.7260.8100.768
Cosmos0.9740.9730.4700.6630.9400.9890.9310.1600.7630.8400.802
Ours0.9560.9430.4550.6490.9560.9900.9330.1240.7510.8570.804
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Strong domain understanding (0.857) and motion smoothness (0.990), reflecting consistent temporal coherence across physical scenarios.

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← Back to Qwen-Robot Suite

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\n

Citation

\n

If you find our work helpful, feel free to give us a cite.

\n
@article{qwenrobot-world,\n  title={Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation},\n  author={Qwen Team},\n  year={2026}\n}\n
\n", "date_published": "2026-06-16T00:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen-robotworld", "id": "urn:aggr:item:e794adae88220fcca61b7bf15eff88a39cb72953", "image": "https://img.alicdn.com/imgextra/i2/O1CN01tVjHlI24sKFloAjuy_!!6000000007446-2-tps-1590-954.png", "summary": "Embodied intelligence requires agents to perceive, reason about, and act within physical environments. World models offer a scalable path forward — but current approaches face a fundamental tension. General video generation models learn…", "tags": [ "ai", "release" ], "title": "Qwen-RobotWorld: Boundless Worlds for Embodied Agents", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-06-16-qwen-robotworld-boundless-worlds-for-embodied-agents/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

GitHub Paper

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\"Qwen-RobotNav

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Agentic navigation systems require a base navigation model with a configurable navigation context protocol: instruction following, object search, target tracking, and autonomous driving share the same perception-planning backbone yet demand fundamentally different context strategies for consuming the visual stream. Like the Model Context Protocol for LLM tool use, a navigation model needs a standardised interface to manage these diverse context requirements at inference time.

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\"Qwen-RobotNav

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We present Qwen-RobotNav, a scalable navigation model built on Qwen3-VL that addresses this through a parameterised interface with two complementary dimensions: task modes that select the navigation behaviour, and controllable observation parameters (token budget, temporal decay, per-camera weights) that govern how visual history is encoded. Trained on 15.6 million samples with training-time randomization over all parameters, Qwen-RobotNav generalizes to any inference-time configuration without architectural modification, unifying five task families under a single set of weights and serving as a natural building block for agentic systems.

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Highlights:

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5 Domains
\n8 SOTAs

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One Model Unifies VLN, ObjNav,
\nTracking, Driving & EQA

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Context
\n= Interface

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4-Axis Observation Protocol
\nZero Architecture Change

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Agentic

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Navigation as a Tool Call
\n+ Two-Level Memory
\nRaises the new bar on EQA

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Generalization

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In-the-Wild Single-Camera
\nDeployment in Unseen Environments

\n\n
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A Navigation Model with Controllable Context

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Mobile navigation spans tasks with wildly different needs. Instruction following demands a long memory of past observations to re-reference distant landmarks. Target tracking cares almost entirely about the most recent few frames. Object search shifts mid-episode: broad history during exploration, tight recency during approach.

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Existing unified navigation models embed a single assumption about what to remember. Qwen-RobotNav addresses this by treating context as a first-class, externally controllable degree of freedom. The model provides four control axes that an agent can tune per call:

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At training time, all these parameters are randomized per sample. The model never sees a fixed configuration, so it generalizes to any setting at inference time.

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\"Qwen-RobotNav

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Architecture. Qwen-RobotNav inherits the Qwen3-VL backbone and adds a lightweight 4-layer MLP action head that outputs 8 waypoints, each with position and heading. Camera identity and temporal order are communicated entirely through natural-language tags interleaved with visual tokens:

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Time step 0 Front View <image> Front Right View <image> ...\nTime step 1 Front View <image> ...\n
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Built for Agentic Navigation

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Qwen-RobotNav is designed as a reconfigurable navigation primitive within a two-tier system. An upper-level planner (Qwen3.7-Plus) decomposes long-horizon goals into sub-goals, while Qwen-RobotNav executes each navigation segment as a reactive waypoint predictor.

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\n

The planner can dynamically switch Qwen-RobotNav’s task mode and context strategy mid-episode. The two tiers communicate entirely through natural language, keeping the system modular and extensible.

\n
\n

\"Agentic

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Each navigation call specifies three things: a sub-goal instruction, a task mode (VLN / PointNav / ObjNav / Tracking), and an observation configuration. The same model weights serve all task phases; only the call arguments change.

\n

To support long-horizon reasoning, the system keeps a two-level memory. Each navigation segment produces a compact trajectory summary. A persistent evidence notebook accumulates durable conclusions across episodes (searched regions, candidate object locations, rejected hypotheses) so the planner always works with concise, relevant context.

\n
\n

Training at Scale

\n

Qwen-RobotNav is trained on 15.6 million samples across five task families, plus vision-language reasoning data that preserves the backbone’s perceptual grounding. Performance scales consistently from 2B to 8B parameters, with the most pronounced gains on long-horizon reasoning tasks.

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\"Training

\n

\"Scaling

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We also introduce an automated pipeline that converts text-to-video generations into navigation trajectories through prompt generation, video synthesis, VLM quality filtering, monocular depth estimation, and kinematic filtering, yielding 40K additional photorealistic samples without any 3D scene reconstruction.

\n
\n

Performance

\n

Instruction Following (VLN-CE)

\n

Development trend of VLN success rates on R2R and RxR validation-unseen splits. Traditional methods show steady early progress, while recent LLM/VLM-based approaches drive the latest performance gains, with Qwen-RobotNav achieving the highest success rates on both benchmarks.

\n

\"VLN-CE

\n

Qwen-RobotNav-8B achieves 72.1% SR on R2R and 76.5% SR on the longer-horizon RxR benchmark, surpassing prior best methods.

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MethodR2R SR↑R2R SPL↑RxR SR↑RxR SPL↑
NaVILA54.049.049.344.0
NavFoM61.755.364.456.2
ABot-N066.463.969.360.0
OmniNav69.566.173.662.0
Qwen-RobotNav-4B69.563.675.265.0
Qwen-RobotNav-8B72.166.676.565.7
\n

Object Searching

\n

On HM3Dv2 object-goal navigation, Qwen-RobotNav-4B achieves 75.6% SR using only RGB observations, surpassing all depth-based methods, and reaches only 1.72m from the goal on average.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
MethodSR↑SPL↑
VLFM52.530.4
CogNav72.526.2
Uni-NaVid73.737.1
Qwen-RobotNav-4B75.630.6
Qwen-RobotNav-8B71.233.0
\n

Active Visual Tracking

\n

On EVT-Bench, Qwen-RobotNav achieves the highest tracking rate at 90.0%, exceeding dedicated trackers and generalist models alike.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
MethodTR↑CR↓SR↑
TrackVLA++81.02.1086.0
NavFoM80.5—85.0
ABot-N087.68.5486.9
Qwen-RobotNav-4B90.06.4077.4
Qwen-RobotNav-8B89.75.7078.6
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Embodied Question Answering

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Equipped with the agentic system, Qwen-RobotNav sets new state-of-the-art results across three EQA benchmarks, surpassing prior methods by substantial margins.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
MethodHM-EQA Acc.↑MT-EQA Acc.↑EXPRESS LLM Score↑
Explore-EQA58.436.2—
Memory-EQA61.443.1—
FAST-EQA69.250.568.7
Qwen3.5-Plus + QwenNav-8B74.152.177.66
Qwen3.6-Plus + QwenNav-8B76.754.479.27
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Autonomous Driving (NAVSIM)

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On closed-loop driving evaluation, Qwen-RobotNav-4B achieves 91.4 PDMS, surpassing specialized driving models.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
MethodNC↑DAC↑TTC↑Comf.↑EP↑PDMS↑
NavFoM97.793.592.310079.684.3
AutoVLA98.495.698.099.981.989.1
ReCogDrive97.997.394.910087.390.8
ReflectDrive97.799.393.510086.991.1
Qwen-RobotNav-4B99.897.598.599.984.491.4
Qwen-RobotNav-8B99.896.998.299.984.290.9
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Qwen-RobotNav produces temporally consistent curved trajectories across both NAVSIM and the AlpaSim closed-loop simulator (zero-shot).

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\n

Real-World Deployment

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We deploy Qwen-RobotNav on a Unitree Go2 quadruped robot with on-device inference via NVIDIA Jetson Thor, achieving 196ms latency (5.1 Hz). The sole visual input is the Go2’s built-in low-resolution camera. All experiments are conducted zero-shot in previously unseen environments without any environment-specific fine-tuning.

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Deployment

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The robot executes navigation tasks in an apartment setting using step-by-step verbal instructions, traversing between the bedroom, living room, and bathroom while responding to fine-grained spatial directives.

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Instruction Following

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We evaluate a back-and-forth navigation task in an unseen exhibition hall: the robot first navigates 21.78 m from a living room to a hospital room following language instructions, then receives a reverse command and must precisely retrace the entire route. This is particularly challenging as it requires the model to maintain spatial awareness over long distances, ground diverse visual landmarks in both forward and reverse directions, and execute accurate bidirectional position control purely from language.

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Agentic Navigation

\n

In agentic mode, the system supports open-ended requests beyond simple route-following. Given the instruction "check whether a green umbrella was left at Cotti Coffee," the agent decomposes the task into sub-goals, navigates using corridor landmarks for localization, inspects the target scene, and produces an evidence-grounded answer without human intervention.

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Agentic navigation: autonomously checking for a green umbrella at Cotti Coffee.

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\n

What’s Next

\n

Qwen-RobotNav reframes multi-task navigation as a context modeling problem: diverse tasks share the same perception and planning backbone but require different strategies for consuming observations. Treating context as an externally controllable interface enables a single model to serve as a practical, deployable navigation primitive within agentic systems.

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← Back to Qwen-Robot Suite

\n
\n

Citation

\n
@article{qwenrobotnav2026,\n  title={Qwen-RobotNav: A Scalable Navigation Model Designed for an Agentic Navigation System},\n  author={Qwen Team},\n  year={2026}\n}\n
\n", "date_published": "2026-06-16T00:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen-robotnav", "id": "urn:aggr:item:f1225dae158ff9455c27f7750e8268db4fc816fa", "image": "https://img.alicdn.com/imgextra/i4/O1CN01ttKuLR1VWn4v7hWsE_!!6000000002661-2-tps-1590-954.png", "summary": "Agentic navigation systems require a base navigation model with a configurable navigation context protocol: instruction following, object search, target tracking, and autonomous driving share the same perception-planning backbone yet…", "tags": [ "ai", "release" ], "title": "Qwen-RobotNav: A Scalable Navigation Model Designed for an Agentic Navigation System", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-06-16-qwen-robotnav-a-scalable-navigation-model-designed-for-an/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

GitHub Paper

\n

\"Qwen-RobotNav

\n

Qwen-Omni × Qwen-RobotManip — Qwen-Omni observes the scene, randomly proposes manipulation tasks via speech, and judges execution in real time. Each video shows Qwen-RobotManip completing tasks on the fly with no pre-defined task list, demonstrating open-ended instruction following and generalization.

\n

Qwen-RobotManip is validated across various real-robot platforms and tasks, demonstrating strong generalization to novel scenes, unseen language instructions, and cross-embodiment transfer.

\n

View Real-World Evaluation Gallery

\n

\"Qwen-RobotManip

\n

Foundation models in language and multimodality have achieved remarkable generalization because heterogeneous data sources can be aligned under a unified formulation, and abundant low-cost internet data allows diverse training signals to reinforce one another at scale. But can this scaling recipe be applied to robotic manipulation?

\n

This is challenging. Unlike text or images, manipulation data is heterogeneous by nature, expensive to collect, and narrow in diversity. Aligning representations across different robot embodiments, sensors, and task domains while simultaneously scaling the data has remained an open problem.

\n

Qwen-RobotManip is a generalizable Vision-Language-Action (VLA) foundation model built upon Qwen-VL. It introduces a unified alignment framework across the representation, motion, and behavioral dimensions of manipulation, making large-scale multi-source training coherent rather than conflicting. Using only open-source robotic manipulation datasets and human demonstration videos without any proprietary data collection, Qwen-RobotManip constructs a ~38,100 hours pretraining corpus and already exhibits emergent generalization capabilities.

\n
\n

Without unified cross-embodiment alignment, scaling data produces conflicts; without data diversity, alignment alone cannot generalize. Alignment and scale are tightly coupled prerequisites for robotic foundation models.

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\n
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Key Highlights

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Alignment

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Representation · Motion · Behavior
\nThree-Dimensional Alignment

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Open-Source Data Only

\n

>38K Hours of Manipulation Data
\nAcross 15 Embodiments

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Dominant

\n

OOD Generalization
\nAcross All Benchmarks

\n

#1

\n

RoboChallenge Table30 v1 Generalist Track
\nSweeping Top 2, 20% Ahead of 3rd Place

\n\n
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Scaling Manipulation Data

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Human-to-Robot Data Synthesis

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\"Human-to-Robot

\n

Robot manipulation data is scarce and expensive to collect. We introduce a Human-to-Robot synthesis pipeline that converts egocentric human manipulation videos into robot demonstrations across 15 robot embodiments via human-to-robot retargeting, hand removal and inpainting, and depth-guided robot compositing.

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Data Sources

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The resulting pretraining corpus totals over 38,100 hours from three complementary sources:

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Data Curation

\n

\"Multi-Stage

\n

We design a multi-stage curation pipeline to ensure VLA training data quality and annotation correctness. Five state-action filtering stages remove noisy actions, fix temporal misalignment, and verify kinematic consistency. Three cross-modal checks then validate that language instructions match the video content, that visual observations agree with recorded robot states, and that video frames are free of corruption.

\n
\n

Qwen-RobotManip Model Design

\n

\"Qwen-RobotManip

\n

Qwen-RobotManip couples a Qwen3.5-4B vision-language backbone with a flow-matching Diffusion Transformer (DiT) action head. Three design choices enable coherent cross-embodiment training:

\n\n

Training. Pre-training uses dual-stream co-training with a VLA stream (robot manipulation data) and a VLM stream (vision-language understanding data) at a 9:1 ratio. Post-training adopts generalist SFT on all demonstration data collected for each benchmark. We propose co-training with VL data and VLA data during post-training, which further improves OOD instruction following and generalization.

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\n

Evaluation

\n

\"Evaluation

\n

Qwen-RobotManip is evaluated across 500+ simulation tasks and 80+ real-world tasks spanning various robot embodiments.

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Why OOD Evaluation Matters

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\"Standard

\n

A critical finding in our experiments: standard benchmarks systematically fail to capture the quality of pretraining. On in-distribution benchmarks like LIBERO and RoboTwin, models trained from scratch without any large-scale robot pretraining achieve performance comparable to previous SOTA pretrained models. Strong IID scores do not indicate genuine generalization; they can be achieved through pattern matching alone.

\n

The separation only becomes visible under out-of-distribution evaluation: novel scenes and task variations, following unseen instructions, and cross-embodiment transfer. This is why Qwen-RobotManip adopts OOD benchmarks as the north star for evaluating robotic foundation models.

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In-Distribution Results

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On standard benchmarks, Qwen-RobotManip matches or exceeds previous SOTA.

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ModelLIBERORT-EasyRT-Hard
π₀94.465.958.4
π_(0.5)97.682.776.8
StarVLA98.085.787.3
Abot-M098.686.185.1
Being-H0.799.290.289.6
Qwen-RobotManip-scratch98.288.788.4
Qwen-RobotManip99.193.492.5
Qwen-RobotManip-Context99.293.794.0
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Out-of-Distribution Generalization

\n

\"OOD

\n

Qwen-RobotManip substantially outperforms all previous models across three OOD generalization axes: task and scene variations, instruction following, and cross-embodiment transfer.

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Detailed Analysis per Benchmark

\n

LIBERO-Plus — OOD robustness evaluation under 7 perturbation dimensions (camera, robot, language, lighting, background, noise, layout):

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ModelCameraRobotLanguageLightBackgroundNoiseLayoutTotal
π₀13.86.058.885.081.479.068.953.6
π_(0.5)78.473.680.896.294.189.084.584.4
StarVLA52.549.888.595.795.773.076.974.1
Abot-M060.467.986.496.291.686.482.680.5
Being-H0.782.059.082.897.890.093.588.584.8
Qwen-RobotManip87.275.585.696.697.797.787.389.0
Qwen-RobotManip-Context89.983.986.598.699.997.987.591.4
\n

Qwen-RobotManip achieves 89.0% and Qwen-RobotManip-Context achieves 91.4% overall. The per-dimension breakdown shows that Camera and Robot perturbations benefit most from large-scale robot data pretraining, while Language and Light robustness is already provided by the VLM backbone.

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RoboTwin-Clean2Rand — Models are fine-tuned on the Clean dataset and tested under progressive environmental randomizations:

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ModelEasyBackgroundLightClutterHeightHard
StarVLA58.127.150.924.248.410.6
GR00T-N1.743.640.441.927.139.020.7
π_(0.5)73.167.069.257.967.647.9
Qwen-RobotManip73.274.668.461.371.062.6
Qwen-RobotManip-Context84.782.484.275.479.569.4
\n

Qwen-RobotManip achieves the highest Hard success rate (62.6%), retaining ~86% of its Easy performance compared to 66% for π_(0.5) and under 30% for models without pretraining. Qwen-RobotManip-Context further achieves 69.4% on Hard, demonstrating the effectiveness of in-context policy adaptation.

\n

RoboCasa365 — Evaluation across atomic and long-horizon manipulation in diverse kitchen environments:

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ModelAtomicComposite-SeenComposite-UnseenTotal
π₀36.35.20.715.0
π_(0.5)39.67.11.216.9
GR00T-N1.550.714.82.723.9
RLDX-163.027.55.433.2
Qwen-RobotManip68.620.114.935.9
Qwen-RobotManip-Context63.922.611.233.8
\n

On Composite-Unseen, which requires completing long-horizon tasks in OOD scenes, Qwen-RobotManip achieves 14.9%, nearly 3× the next-best model (5.4%).

\n

EBench — Mobile manipulation across tabletop, pick-and-place, and long-horizon tasks:

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ModelTableTopSimplePnPLongHorizonOverall
π₀15.73035.03917.04123.637
π₀.₅12.93245.05018.13927.141
X-VLA8.62450.0546.22523.736
InternVLA-A14.31143.04717.94623.936
Qwen-RobotManip50.07056.56029.95545.660
Qwen-RobotManip-Context49.35655.06626.65543.659
\n

Qwen-RobotManip achieves 45.6% overall SR and a composite score of 60, outperforming π_(0.5) (27.1% / 41) and all other baselines by a large margin across every split.

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RoboTwin-IF — Instruction following with held-out unseen instruction templates:

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ModelPick-DiversePlace-Rel.Ope.-Mic-Dr.Ope.-StaplerOpe.-TableAverage
StarVLA11130497429.4
GR00T-N1.720170143216.6
π_(0.5)442015926649.6
Qwen-RobotManip795742909372.2
Qwen-RobotManip-Context777133899072.0
\n

Qwen-RobotManip achieves 72.2% average, a +22.6 point lead over π_(0.5). The largest gains appear on tasks where the instruction must be parsed to select the correct action among multiple plausible alternatives, confirming genuine language-conditioned control.

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Zero-Shot Cross-Embodiment — Trained on AgileX ALOHA data only, evaluated on unseen robot embodiments on our RoboTwin-XE benchmark:

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ModelARXUR5FrankaTotal
π_(0.5) (joint)24.62.20.99.2
π_(0.5) (eef)11.510.01.17.5
Qwen-RobotManip (joint)37.64.11.814.5
Qwen-RobotManip (eef)42.922.85.923.9
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Camera-frame EEF representation dramatically improves zero-shot transfer by abstracting away morphological differences. Qwen-RobotManip (eef) reaches 23.9% overall, 3.2× π_(0.5) (eef) at 7.5%.

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Data Scaling: Alignment Enables Scale

\n

\"Downstream

\n

An important finding: only models with unified cross-embodiment representations exhibit clean log-linear data scaling behavior. Without the alignment framework (UnifiedSpace + UnifiedEEF), adding more data produces erratic or flat scaling curves. This confirms that alignment is the prerequisite for scale, not the other way around.

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\n

Real-World Experiments

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Generalization to Novel Scenes and Instructions

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\"Real-World

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In-domain evaluation across 7 tasks spanning basic pick-and-place, deformable object handling, and precision assembly:

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Taskπ_(0.5)StarVLAOurs
table-cleanup4/50/55/5
three-bowl-stacking5/54/55/5
melon-in-bowl2/50/55/5
towel-folding4/53/54/5
block-in-drawer0/50/55/5
yellow-disc-insertion0/50/52/5
three-block-stacking0/50/55/5
Average42.9%20.0%88.6%
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Out-of-domain evaluation with distribution shifts in visual scenes, objects, and instructions:

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TaskOOD Factorsπ_(0.5)StarVLAOurs
target-object-in-basketcluttered bg, unseen objects8/100/1010/10
left-right-bowl-stackingcluttered bg, left-right reference1/100/1010/10
tool-on-towelunseen small objects, distractors0/100/106/10
banana-on-toweldynamic lighting (disco light)6/100/109/10
Average37.5%0.0%87.5%
\n

Qwen-RobotManip achieves 88.6% in-domain and 87.5% OOD success, substantially outperforming π_(0.5) (42.9% / 37.5%) and StarVLA (20.0% / 0.0%).

\n

Data-Efficient Skill Transfer Across Embodiments

\n

\"ARX

\n

Few-shot adaptation. All methods are jointly finetuned on only 130 teleoperated demonstrations across 5 tasks. Qwen-RobotManip outperforms both baselines on 4 of 5 tasks:

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TaskSub-stepStarVLAπ_(0.5)Ours
Put FruitsPlace 1 / 2 / 33/1/09/5/29/5/3
Avg. success13.3%53.3%56.7%
Put BlocksOpen / Place1 / Place2 / Close1/1/0/04/2/2/25/4/3/3
Avg. success5.0%25.0%37.5%
Fold TowelFold 1 / Fold 20/03/13/3
Avg. success0.0%20.0%30.0%
Insert ScrewHandover / Insert0/02/02/0
Avg. success0.0%10.0%10.0%
Unscrew CapGrasp / Unscrew / Place4/0/09/2/19/4/3
Avg. success13.3%40.0%53.3%
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Cross-embodiment skill transfer. A single policy jointly finetuned on 6K CobotMagic and 130 ARX demonstrations is evaluated on 4 novel tasks on ARX, for which ARX has zero training demonstrations:

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ModelStack PlatesStack BlocksFruits in PlateTrash in BucketAvg.
w/o UnifiedSpace0/100/103/100/107.5%
w/o UnifiedEEF0/100/105/100/1012.5%
Qwen-RobotManip3/105/107/107/1055.0%
\n

The full unified framework achieves 55.0%, over 4× the best ablation variant, demonstrating that the unified representation enables skill-level transfer across kinematically different embodiments.

\n

Complex Multi-Step Tasks and Emergent Recovery

\n

In the RoboChallenge Table30 v1 Generalist Track, which spans 30 tasks across 4 robot platforms, Qwen-RobotManip ranks 1st with 45% success rate and 59.83 process score, outperforming the runner-up by 20%. On 8 bimanual coordination tasks, it achieves 40% success versus π_(0.5)’s 21.2%.

\n

\"Challenging

\n

\"Average

\n

Bimanual coordination. Among the 30 benchmark tasks, 8 require tight bimanual coordination on the ALOHA platform, where the two arms must jointly stabilize, transport, and manipulate objects. Qwen-RobotManip achieves 40% average success rate, far exceeding π_(0.5) (21.2%), DM0 (16.2%), GR00T-MULTI (7.5%), and π₀ (7.5%). Notably, Qwen-RobotManip is the only model to succeed on “pour fries into plate” (30% vs. 0% for all baselines), a task demanding sequential bimanual steps — stabilizing the fries box with the left arm, opening it with the right arm, picking it up, and pouring the contents onto the plate. We attribute this strong bimanual performance to two factors: (1) our pretraining corpus contains a substantial proportion of bimanual demonstration data, enabling the model to learn coordinated dual-arm control primitives; and (2) the Human-to-Robot synthesis pipeline further expands the effective bimanual pretraining data by synthesizing bimanual robot demonstrations from egocentric human videos.

\n

\"Bimanual

\n

Robust pick-and-place across embodiments. We identify 12 tasks across all four platforms that center on pick-and-place primitives, ranging from single-object grasping to multi-step sequential manipulation involving 4–5 objects. Qwen-RobotManip achieves 63.3% average success rate on these tasks, surpassing the next-best baseline DM0 (48.3%) by 15.0 percentage points. We attribute this capability to two factors: (1) the large-scale cross-embodiment pretraining data encodes abundant pick-and-place patterns, and (2) the unified action space enables knowledge sharing of fundamental spatial skills across different robot morphologies.

\n

\"Emergent

\n

Reactive error recovery. When objects slip during grasping, the model autonomously retries until successful. This behavior emerges from pretraining at scale, not from explicit programming.

\n
\n

Towards Scalable Robotic Foundation Models

\n

Qwen-RobotManip demonstrates that the scaling recipe behind language and multimodal foundation models can be extended to robotic manipulation, but only when alignment and scale work in tandem. The unified cross-embodiment representation is what makes large-scale multi-source training productive rather than conflicting, and the Human-to-Robot synthesis pipeline provides the data diversity that alignment alone cannot supply.

\n

Embodied intelligence is still at an early stage. Contact-rich, long-horizon real-world tasks, failure recovery, continual learning, and complex human-robot-environment interactions remain challenging. Yet Qwen-RobotManip points to a clear path forward:

\n

Alignment unlocks scale, and scale unlocks generalization.

\n

← Back to Qwen-Robot Suite

\n
\n

Citation

\n
@article{qwenrobotmanip,\n  title={Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models},\n  author={Qwen Team},\n  year={2026}\n}\n
\n", "date_published": "2026-06-16T00:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen-robotmanip", "id": "urn:aggr:item:1d7d4e460a51507ed5dd93b7ec9f751e5838d59c", "image": "https://img.alicdn.com/imgextra/i4/O1CN01tCtYQc1DfLOL4aayA_!!6000000000243-2-tps-1590-954.png", "summary": "Qwen-Omni × Qwen-RobotManip — Qwen-Omni observes the scene, randomly proposes manipulation tasks via speech, and judges execution in real time. Each video shows Qwen-RobotManip completing tasks on the fly with no pre-defined task list,…", "tags": [ "ai", "release" ], "title": "Qwen-RobotManip: Alignment Unlocks Scale for Robotic Manipulation Foundation Models", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-06-16-qwen-robotmanip-alignment-unlocks-scale-for-robotic/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"Qwen3.7

\n

Today we introduce Qwen3.7-Plus — a multimodal agent model that unifies vision and language into a single, versatile agent foundation. Building on Qwen3.7’s strong text backbone, Qwen3.7-Plus delivers a comprehensive upgrade in vision-language capabilities while retaining full agentic strength in coding, tool use, and productivity workflows.

\n

What sets Qwen3.7-Plus apart is its ability to operate as a multimodal interactive hybrid agent. It perceives real-world scenes, reads screens and operates GUIs, writes code from visual references, navigates mobile apps end-to-end, and answers visual questions grounded in web knowledge — seamlessly blending GUI and CLI interactions within a single agent loop. As a versatile coding agent and productivity assistant, it handles the full spectrum from frontend prototyping to complex software engineering and multi-step workflow automation with full-modality input. It generalizes across agent scaffolds, performing consistently whether deployed through Claude Code, OpenClaw, Qwen Code, or other frameworks.

\n\n

Performance

\n

\"\"

\n

Text Benchmarks

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Opus-4.6 MaxK2.6 ThinkingGLM-5.1 ThinkingDeepSeek-V4-Pro MaxQwen3.6-PlusQwen3.7-Plus
Coding Agent
Terminal Bench 2.0-Terminus65.466.763.567.961.670.3
SWE-Verified80.880.2--80.678.877.7
SWE-Pro57.359.558.859.056.657.6
SWE-Multilingual77.576.7--76.273.875.8
NL2repo47.642.841.035.534.441.1
SciCode51.952.245.1--41.451.3
QwenWebDev1617--1564157015001536
QwenSVG154113251605150614321588
General Agent
Qwenclaw65.554.758.759.257.261.8
CoWorkBench68.258.266.066.364.565.1
ClawEval70.461.562.758.457.162.7
Skillsbench--56.253.152.345.754.9
BFCL-V476.771.370.970.668.972.9
MCP-Mark56.755.957.557.148.258.7
MCP-Atlas75.866.671.873.674.173.2
Vitabench--39.145.151.942.845.6
Deep-Planning58.942.334.144.640.962.3
SpreadSheetBench-v189.384.585.284.980.286.3
Kernel Bench L32.63/98%1.41/80%2.00/78%1.07/54%1.03/48%2.06/98%
QwenWorldBench56.150.950.252.347.662.1
STEM & Reasoning
GPQA Diamond91.390.586.290.190.490.3
HLE40.036.434.737.728.834.7
LiveCodeBench88.889.6--93.587.189.6
HMMT 2026 Feb96.292.789.495.287.892.9
IMOAnswerBench75.386.083.889.883.886.0
CritPT12.68.04.612.92.96.0
Apex34.524.011.538.38.822.7
General Capability
MMLU-Pro89.787.186.387.588.588.5
MMLU-Redux95.295.394.394.894.594.5
SuperGPQA72.571.368.069.971.671.4
IFEval91.994.594.591.994.394.6
IFBench62.576.076.077.074.279.1
MRCR-v2 128k84.063.162.074.485.991.7
Multilingualism
WMT24++82.781.681.882.284.384.6
MAXIFE81.387.787.788.988.288.8
MMMLU90.687.587.287.989.589.0
MMLU-ProX86.183.783.983.984.785.4
NOVA-6359.156.754.652.857.958.8
INCLUDE87.484.284.386.185.183.0
Global PIQA91.289.289.590.589.890.3
PolyMATH80.282.767.672.077.484.0
\n

* Terminal-Bench 2.0: Harbor/Terminus-2 harness; 5h timeout, 12 CPU/24 GB RAM; temp=1.0, top_p=0.95, top_k=20, max_tokens=80K, 256K ctx; avg of 5 runs. All experiments prepend a <think> token at each turn, allowing the model to decide whether to engage extended thinking.
\n* SWE-Bench Series: Internal agent scaffold (bash + file-edit tools); temp=1.0, top_p=0.95, 200K context window.
\n* SWE-bench Pro: Problematic tasks corrected and all baselines evaluated on the refined benchmark.
\n* QwenClawBench: a real-user-distribution Claw agent benchmark; open-source: https://github.com/SKYLENAGE-AI/QwenClawBench.
\n* CoWorkBench: an internal cowork benchmark; long-horizon tasks across computer science, finance, law, medical, and other productivity domains.
\n* SkillsBench: Evaluated via OpenCode on 78 tasks (excluding 9 external API-dependent tasks); avg of 5 runs.
\n* MCP-Mark: GitHub MCP v0.30.3; Playwright responses truncated at 32K tokens.
\n* MCP-Atlas: Public set score; gemini-2.5-pro judger.
\n* VITA-Bench: Avg subdomain scores; using claude-4.5-sonnet as judger, as the older official judgers are no longer available.
\n* Kernel Bench L3: Metrics reported: median of per-problem speedup over PyTorch eager reference / fraction of problems faster than torch.compile, across 50 problems. Each test sample runs in an isolated Docker container with one H100 80GB GPU, with internet access restricted to the CUTLASS codebase and official CUDA documentation, limited to 500 tool calls with early stopping after 100 non-improving turns. GPT-5.4 (xhigh) is applied to detect potential hacking behaviors. CUPTI is used for kernel-level timing.
\n* Reasoning scenarios: Recommended system prompt: "Reasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer."
\n* WMT24++: Harder WMT24 subset; avg scores on 55 langs via XCOMET-XXL.
\n* MAXIFE: Accuracy on EN + multilingual prompts (23 settings total).
\n* MMLU-ProX: Avg accuracy across 29 languages.
\n* Empty cells (--) indicate scores not yet available.

\n

Qwen3.7-Plus delivers competitive text performance that approaches Max-tier models across the board. In coding agents, it performs strongly on Terminal Bench 2.0, SWE-bench series, and SciCode, handling both real-world software engineering and scientific programming tasks effectively. In general-purpose agents, it demonstrates robust tool-use and planning capabilities across MCP-Mark, Deep-Planning, and Kernel Bench L3, showing particular strength in complex multi-step planning and GPU kernel optimization. Its reasoning performance on GPQA Diamond, HMMT, and IMOAnswerBench places it among the strongest Plus-tier models on hard STEM benchmarks. In instruction following and multilingual tasks, it delivers consistent quality across IFBench, WMT24++, and PolyMATH, with strong coverage across diverse languages.

\n

Multimodal Benchmarks

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
GPT-5.4 (xhigh)Opus-4.6 MaxGemini-3.1 ProQwen3.6-PlusQwen3.7-Plus
Multimodal Reasoning
MMMU-Pro81.273.981.878.879.0
MathVision91.065.587.488.090.3
BabyVision53.112.655.937.470.4 / 64.7
CharXiv(RQ)84.566.084.481.585.9 / 84.4
HiPhO65.040.885.480.484.1
ERQA67.840.868.065.769.8
VisFactor40.824.439.836.042.8
MedXpertQA-MM77.364.480.768.771.0
Visual Agent & Coding
ScreenSpot Pro67.449.568.168.279.0
OSWorld-Verified75.072.7--62.573.3
AndroidWorld--62.070.767.281.0
QwenVision2Code1884.01518.01632.01522.01772.0
ClawEval-MM54.454.745.749.155.7
Multimodal Search & Knowledge QA
SimpleVQA69.479.676.969.481.7
WorldVQA45.965.456.133.661.1
MMSearchPlus19.738.942.019.641.4
BC-VL48.151.549.926.151.1
MMBC18.846.328.218.346.3
General Visual Understanding
RealWorldQA83.873.983.585.486.9
CountQA58.432.572.871.777.0
OmniDocBench1.585.586.690.091.291.4
OCR-Bench-V2(EN)59.154.364.667.070.7
OCR-Bench-V2(ZH)57.754.958.263.667.1
ODinW13------51.851.1
Autonomous Driving
LingoQA78.277.666.876.083.4
Ego3D-Bench↓6.98.110.46.15.9
SURDS64.658.364.073.277.2
VLADBench77.148.073.175.677.2
Video Understanding
VideoMME (w/ sub.)89.586.188.487.888.0
VideoMMMU82.485.285.384.085.4
MLVU (M-Avg)86.181.784.786.787.4
TVBench82.569.873.076.078.2
LVBench77.463.075.174.876.2
\n

* Multimodal Search & Knowledge QA: All models evaluated with search augmentation enabled.
\n* BabyVision and CharXiv(RQ): Scores are reported as "with CI / without CI".
\n* VideoMME (w/ sub.): Scores are reported with subtitles.
\n* BC-VL and MMBC: Scores are reported with the recommended presence penalty 1.5 in BC tasks.
\n* ScreenSpot Pro and OSWorld-Verified: Scores are reported with "enable_thinking=False".
\n* Empty cells (--) indicate the scores are not yet available.

\n

Qwen3.7-Plus’s multimodal improvements are not limited to isolated gains in visual understanding. Instead, they reflect a systematic enhancement of the core capabilities required by multimodal agents: understanding complex visual inputs, reasoning over visual information, using tools to solve problems, and ultimately executing tasks in code or GUI environments.

\n

In Multimodal Reasoning, Qwen3.7-Plus delivers strong performance on challenging visual reasoning benchmarks such as BabyVision, MathVision, HiPhO, ERQA, and VisFactor. These results demonstrate the model’s ability to integrate fine-grained visual perception, spatial relationships, physical commonsense, and multi-step logical reasoning. In particular, its significant improvement on BabyVision over Qwen3.6-Plus suggests stronger generalization on tasks that are closer to early human visual cognition and spatial reasoning.

\n

In Visual Agent & Coding, Qwen3.7-Plus shows substantial gains on ScreenSpot Pro, OSWorld-Verified, and AndroidWorld. This indicates that the model can not only recognize screen content, but also localize key UI elements, understand task intent, and complete multi-step interactions. On QwenVision2Code, the model also demonstrates strong vision-to-code generation capabilities, turning images, videos, and design references into executable code. These capabilities form the foundation for multimodal agents to move from “understanding interfaces” to “operating interfaces” and even “building interfaces.”

\n

In Multimodal Search & Knowledge QA, Qwen3.7-Plus achieves clear improvements on SimpleVQA, WorldVQA, MMSearchPlus, BC-VL, and MMBC. The model can combine visual inputs with external knowledge retrieval to answer questions that cannot be solved from image content alone. This makes it better suited for real-world tasks, where users do not simply ask “what is in the image,” but expect the model to combine visual evidence, commonsense, and up-to-date knowledge to provide reliable answers.

\n

In General Visual Understanding, Qwen3.7-Plus maintains strong performance across real-world scenes, document parsing, chart understanding, OCR, counting, and spatial localization. It performs strongly on tasks such as RealWorldQA, CountQA, OmniDocBench, CharXiv, and OCR-Bench-V2. These capabilities are essential for robustly handling real business inputs, including screenshots, receipts, tables, reports, posters, product images, and complex UI pages.

\n

Beyond images, Qwen3.7-Plus further strengthens video understanding and driving-scene understanding. On video benchmarks such as VideoMMMU, MLVU, TVBench, and LVBench, it can reason over events, actions, temporal dynamics, and semantic relationships in both short and long videos. On driving-related evaluations such as LingoQA, Ego3D-Bench, SURDS, and VLADBench, it also demonstrates strong understanding of dynamic scenes, traffic participants, and spatial relationships. These capabilities lay an important foundation for real-world multimodal agents, autonomous driving understanding, and embodied AI scenarios.

\n

Build with Qwen3.7-Plus

\n

Qwen3.7-Plus is now available through Alibaba Cloud Model Studio.

\n

API Usage

\n

As a multimodal model, Qwen3.7-Plus accepts both text and image/video inputs. It also supports the preserve_thinking feature: preserving thinking content from all preceding turns in messages, which is recommended for agentic tasks.

\n

Alibaba Cloud Model Studio

\n

Alibaba Cloud Model Studio supports industry-standard protocols, including chat completions and responses APIs compatible with OpenAI’s specification.

\n
"""\nEnvironment variables:\n  DASHSCOPE_API_KEY: Your API Key from https://modelstudio.console.alibabacloud.com\n  DASHSCOPE_BASE_URL: (optional) Base URL for compatible-mode API.\n    - Beijing: https://dashscope.aliyuncs.com/compatible-mode/v1\n    - Singapore: https://dashscope-intl.aliyuncs.com/compatible-mode/v1\n    - US (Virginia): https://dashscope-us.aliyuncs.com/compatible-mode/v1\n"""\nfrom openai import OpenAI\nimport os\n\napi_key = os.environ.get("DASHSCOPE_API_KEY")\nif not api_key:\n    raise ValueError(\n        "DASHSCOPE_API_KEY is required. "\n        "Set it via: export DASHSCOPE_API_KEY='your-api-key'"\n    )\n\nclient = OpenAI(\n    api_key=api_key,\n    base_url=os.environ.get(\n        "DASHSCOPE_BASE_URL",\n        "https://dashscope-intl.aliyuncs.com/compatible-mode/v1",\n    ),\n)\n\nmessages = [{"role": "user", "content": "Write a Python function to merge two sorted linked lists."}]\n\ncompletion = client.chat.completions.create(\n    model="qwen3.7-plus",\n    messages=messages,\n    extra_body={\n        "enable_thinking": True,\n        # "preserve_thinking": True,\n    },\n    stream=True\n)\n\nreasoning_content = ""\nanswer_content = ""\nis_answering = False\nprint("\\n" + "=" * 20 + "Reasoning" + "=" * 20 + "\\n")\n\nfor chunk in completion:\n    if not chunk.choices:\n        print("\\nUsage:")\n        print(chunk.usage)\n        continue\n\n    delta = chunk.choices[0].delta\n\n    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:\n        if not is_answering:\n            print(delta.reasoning_content, end="", flush=True)\n        reasoning_content += delta.reasoning_content\n\n    if hasattr(delta, "content") and delta.content:\n        if not is_answering:\n            print("\\n" + "=" * 20 + "Answer" + "=" * 20 + "\\n")\n            is_answering = True\n        print(delta.content, end="", flush=True)\n        answer_content += delta.content\n
\n

For more information, please visit the API doc.

\n

Multimodal Interactive Hybrid Agent

\n

Qwen3.7-Plus features multimodal hybrid-agent capabilities designed for closed-loop execution of real-world tasks. It can not only understand visual interfaces, perceive on-screen content, and perform both GUI interactions and CLI operations, but also leverage environmental feedback for code generation, application manipulation, testing, validation, and iterative optimization. By integrating the full workflow of “see, think, write, act, and verify” into a unified agent loop, it enables end-to-end automation of complex software tasks from initial understanding to final delivery.

\n

We built the Hybrid-Agent intelligent agent system based on Qwen3.7, deeply integrating the code generation capabilities of large language models with GUI automation execution, achieving full-chain APP development from requirement analysis to version iteration. The Agent operated continuously and stably for over 11 hours, fully automating the complete R&D cycle of an English vocabulary learning APP. It generated more than 10,000+ lines of code, triggered over 1,000+ Agent calls, and covered core stages across the entire software development lifecycle: requirement document generation, automated coding, installation and deployment, test case creation, GUI-based automated testing, multi-scenario parallelized testing, automatic product documentation updates, and autonomous version evolution.

\n

For professional desktop application scenarios, the Hybrid-Agent system deeply integrates the model’s GUI perception and code generation capabilities to enable one-click autonomous replication of professional desktop applications. The Agent autonomously completed a high-fidelity recreation of the native macOS Stocks app, covering the full pipeline from requirement understanding to delivery validation: autonomously interacting with the native app to comprehend UI layout and feature details, generating SwiftUI source code from interaction records, integrating with the LongBridge real-world market API for live data, automatically compiling and launching the recreated app, and finally conducting 10 functional verification tests autonomously – including real-time quote loading, stock selection and switching, multi-period view toggling, search filtering, and detailed stats panel display – all passed. The delivered application faithfully reproduces the native Stocks app’s dark theme, split-view layout, real-time market data, and full interactivity.

\n

Visual Agent

\n

Qwen3.7-Plus can serve as a powerful visual agent, combining visual understanding with tool use to solve complex visual tasks. Through integration with a code interpreter, it can analyze images to spot differences, complete missing puzzle pieces, solve sliding-block puzzles, navigate mazes, and assemble jigsaw puzzles—all by autonomously generating and executing code. With search augmentation, it can also leverage web knowledge to reason over real-world visual questions and provide multimodal answers across single-image, multi-image, and video inputs.

\n

Below, we showcase several examples that demonstrate the multimodal agent capabilities of Qwen3.7-Plus.

\n

Multimodal Reasoning

\n

For multimodal reasoning, we introduce code execution to further enhance the model’s problem-solving ability. The model first understands the structure and constraints in the visual input, then transforms the visual task into a computable representation, and finally writes and executes code to solve, search, or verify the answer.

\n

In tasks such as spot-the-difference, missing-block completion, sliding-block puzzles, mazes, and jigsaw puzzles, the model needs to go beyond recognizing visual content. It must also perform spatial modeling, path search, state simulation, and result verification. These examples highlight Qwen3.7-Plus’s ability to move from visual perception to programmatic problem solving.

\n

Find the differences Next

\n

Qwen3.7

\n

Fill with patches Next

\n

Qwen3.7

\n

Klotski Next

\n

Qwen3.7

\n

Maze simulator Next

\n

Qwen3.7

\n

Jigsaw Next

\n

Qwen3.7

\n

Multimodal Search

\n

In search-augmented visual question answering, Qwen3.7-Plus can combine image, video, or multi-image inputs with web search to answer real-world knowledge questions. The model first extracts key entities, scenes, text, and contextual clues from the visual input, then retrieves external knowledge through search, and finally synthesizes visual evidence with retrieved information to produce the answer.

\n

This enables the model to handle a wide range of open-world questions, such as identifying locations, understanding the background of events, analyzing products or objects, and answering visual questions that depend on up-to-date knowledge.

\n

Realworld VQA Next

\n

Qwen3.7

\n

Realworld VQA Next

\n

Qwen3.7

\n

Multi-hop Multimodal Browsing Agent Next

\n

Qwen3.7

\n

video search simplevqa Next

\n

Qwen3.7

\n

Visual Coding

\n

Qwen3.7-Plus demonstrates strong vision-to-code generation capabilities. It can transform images, videos, UI screenshots, and design references into executable code, covering a broad range of scenarios from SVG reconstruction to full webpage generation.

\n

Image/Video to SVG

\n

In image/video-to-SVG tasks, the model needs to understand geometric structures, colors, layouts, hierarchical relationships, and dynamic changes in visual content, and then express these elements precisely in code. This requires not only visual understanding, but also structured representation and code generation.

\n

For icons, illustrations, animations, graphic design, and information visualization, this capability can significantly reduce the cost of turning visual references into editable code assets.

\n

vision to svg Next

\n

User

\n

\"image\" Please generate svg code according to the image.

\n

Qwen3.7

\n

vision to svg Next

\n

User

\n

\"image\" Please generate svg code according to the image.

\n

Qwen3.7

\n

vision to svg Next

\n

User

\n

Please generate svg code according to the video.

\n

User

\n

Qwen3.7

\n

vision to svg Next

\n

User

\n

Please generate svg code according to the video.

\n

User

\n

Qwen3.7

\n

vision to svg Next

\n

User

\n

\"image\" Please generate svg code according to the image.

\n

Qwen3.7

\n

Vision-Driven Web Design

\n

In vision-driven web design, Qwen3.7-Plus can generate complete interactive webpages based on visual references, video materials, or design intent. The model can also use generation tools to produce assets for webpage design.

\n

It not only reproduces the visual style of a reference page, but also organizes layout, writes frontend code, handles interaction logic, and integrates multimodal assets into the final page. This demonstrates the potential of Qwen3.7-Plus as a visual coding assistant: moving from “given a reference image” to “generate a runnable web prototype.”

\n

Web Design with Video-Generation Next

\n

Qwen3.7

\n

Web Design with Video-Generation Next

\n

Qwen3.7

\n

Web Design with Video-Generation Next

\n

Qwen3.7

\n

Browser Agent

\n

Built on Qwen3.7-Plus, the browser Agent is demonstrated and recorded through Qwen for Chrome, a browser extension embedded in Chrome. Users can interact with Qwen directly from the browser sidebar and, with authorization, switch it into Agent mode. In this mode, Qwen can perceive the current webpage, understand the user’s task, plan the next steps, and operate as a Browser Agent to perform clicks, typing, navigation, configuration, and verification directly in the real browser environment.

\n

With this setup, the Qwen3.7 browser Agent integrates page understanding, task planning, and GUI automation to operate inside real web-based work environments. Given a non-technical user’s request to purchase the cheapest ECS server, the Agent can navigate the cloud console, compare instance options, select a low-cost configuration, set up images, storage, security groups, and order details, while dynamically adjusting its strategy when prices change, inventory is limited, or purchase constraints arise. In the follow-up task, the Agent further handles instance scaling and maintenance, completing shutdown, configuration updates, disk expansion, service recovery, and final verification. This scenario covers the real cloud workflow from server purchase to upgrade, turning a complex console-based process into a continuous, efficient, and deliverable browser automation task.

\n

Real-world Perception & Reasoning

\n

Qwen3.7-Plus also shows strong performance in real-world perception and multimodal reasoning. Real-world scenes are often much more complex than standard visual question answering. They may involve occlusion, cluttered backgrounds, small objects, relationships among multiple entities, cross-image comparison, and implicit physical commonsense.

\n

To answer these questions reliably, the model must first identify visual details robustly, then combine them with spatial relationships, commonsense knowledge, and logical reasoning.

\n

realworld counting Next

\n

Qwen3.7

\n

multi-image reasoning Next

\n

Qwen3.7

\n

puzzle Next

\n

Qwen3.7

\n

Grounding Next

\n

Qwen3.7

\n

Coding Assistants

\n

Qwen3.7-Plus integrates seamlessly with popular agent frameworks and coding assistants:

\n

Claude Code

\n

Qwen APIs support the Anthropic API protocol, enabling direct use with Claude Code:

\n
npm install -g @anthropic-ai/claude-code\n\nexport ANTHROPIC_MODEL="qwen3.7-plus"\nexport ANTHROPIC_SMALL_FAST_MODEL="qwen3.7-plus"\nexport ANTHROPIC_BASE_URL=https://dashscope-intl.aliyuncs.com/apps/anthropic\nexport ANTHROPIC_AUTH_TOKEN=<your_api_key>\n\nclaude\n
\n

OpenClaw

\n

Connect to OpenClaw via Model Studio:

\n
curl -fsSL https://molt.bot/install.sh | bash\nexport DASHSCOPE_API_KEY=<your_api_key>\nopenclaw dashboard\n
\n

Configure ~/.openclaw/openclaw.json:

\n
{\n  "models": {\n    "mode": "merge",\n    "providers": {\n      "modelstudio": {\n        "baseUrl": "https://dashscope-intl.aliyuncs.com/compatible-mode/v1",\n        "apiKey": "DASHSCOPE_API_KEY",\n        "api": "openai-completions",\n        "models": [\n          {\n            "id": "qwen3.7-plus",\n            "name": "qwen3.7-plus",\n            "reasoning": true,\n            "input": ["text"],\n            "contextWindow": 1000000,\n            "maxTokens": 65536\n          }\n        ]\n      }\n    }\n  },\n  "agents": {\n    "defaults": {\n      "model": {\n        "primary": "modelstudio/qwen3.7-plus"\n      }\n    }\n  }\n}\n
\n

Qwen Code

\n

Qwen Code is deeply optimized for the Qwen series:

\n
npm install -g @qwen-code/qwen-code@latest\nqwen\n
\n

Summary

\n

Qwen3.7-Plus is our most capable multimodal agent model, unifying vision understanding and language reasoning into a versatile agent foundation. It operates as a multimodal interactive hybrid agent — perceiving real-world scenes, operating graphical interfaces, writing code from visual references, and completing end-to-end tasks across both GUI and CLI environments. As a versatile coding agent and productivity assistant, it handles the full range of tasks from frontend prototyping to complex software engineering and multi-step workflow automation. It generalizes across agent scaffolds, performing consistently whether deployed through Claude Code, OpenClaw, Qwen Code, or other frameworks. We welcome community feedback and look forward to seeing what you build.

\n

Citation

\n
@misc{qwen37plus,\n    title = {{Qwen3.7-Plus}: Multimodal Agent Intelligence},\n    url = {https://qwen.ai/blog?id=qwen3.7-plus},\n    author = {{Qwen Team}},\n    month = {May},\n    year = {2026}\n}\n
\n", "date_published": "2026-06-01T02:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen3.7-plus", "id": "urn:aggr:item:92bf2b521918e1ff345fbeb11dc8fa9bda8da34a", "image": "https://img.alicdn.com/imgextra/i3/O1CN01dlIhjJ1Zi1FO5WGsc_!!6000000003227-2-tps-1590-954.png", "summary": "Today we introduce Qwen3.7-Plus — a multimodal agent model that unifies vision and language into a single, versatile agent foundation. Building on Qwen3.7’s strong text backbone, Qwen3.7-Plus delivers a comprehensive upgrade in…", "tags": [ "ai", "release" ], "title": "Qwen3.7-Plus: Multimodal Agent Intelligence", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-06-01-qwen3-7-plus-multimodal-agent-intelligence/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"Qwen3.6

\n

GitHub Paper Demo

\n

Over the past few years, multimodal large language models have become increasingly capable of understanding images, videos, and real-world scenes. They can recognize objects, reason about spatial relationships, answer visual questions, and solve complex multimodal reasoning tasks.

\n

But for embodied intelligence, understanding the world is only the first step. A truly embodied agent also needs to understand task goals, take actions in the physical world, and generalize across different robot embodiments, environments, and tasks.

\n

This is the motivation behind Qwen-VLA.

\n

Qwen-VLA is a general-purpose Vision-Language-Action model. Built upon the Qwen multimodal backbone, it extends visual perception, language understanding, and spatial reasoning into continuous action generation and trajectory prediction. In other words, it allows the model to not only see and think, but also begin to act.

\n
\n

One Model for Multiple Embodied Tasks

\n

Traditional embodied AI systems are often highly specialized: one model for tabletop manipulation, another for navigation, and yet another for a specific robot platform. This approach can work well for individual tasks, but it does not scale easily to broader tasks, diverse environments, or different robot embodiments.

\n

Qwen-VLA explores a more unified direction:

\n
\n

Can a single generalist policy model support robotic manipulation, vision-language navigation, and cross-embodiment control at the same time?

\n
\n

In Qwen-VLA, robotic manipulation and vision-language navigation are formulated under the same framework: given visual observations, language instructions, and embodiment-specific conditions, the model predicts the next action or trajectory. The Qwen multimodal backbone understands the visual and language inputs, while an action decoder generates continuous actions.

\n
\n

Training: From Language Priors to Closed-Loop Control

\n

\"Qwen-VLA

\n

The core of Qwen-VLA is not simply attaching an action head to a multimodal model. More importantly, it builds a joint training system that covers diverse tasks, environments, and robot embodiments. The full training pipeline progresses through four stages, from language priors to closed-loop control.

\n

Data

\n

The pretraining data spans five major sources:

\n\n

Four-Stage Training

\n

The key idea: first learn to generate action structures from language, then learn to adapt those actions to the visual environment.

\n

\"Qwen-VLA

\n\n
\n

Performance

\n

A Single Generalist Model Can Match or Even Surpass Specialist Models

\n

The experimental results show the potential of Qwen-VLA as a generalist policy model. A single model can cover multiple manipulation benchmarks, including LIBERO, Simpler, RoboCasa, and RoboTwin, while approaching or surpassing specialized policy models on several tasks.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
BenchmarkBest Specialist ModelQwen-VLA
LIBEROABot-M0 98.6%97.9%
RoboCasa-GR1ABot-M0 58.3%56.7%
Simpler-WidowXStarVLA-OFT 64.6%73.7%
RoboTwin-Easy / HardABot-M0 86.0% / 85.0%86.1% / 87.2%
\n

On robotic manipulation benchmarks, Qwen-VLA-Instruct achieves 97.9% on LIBERO, 73.7% on Simpler-WidowX, and 86.1% / 87.2% on RoboTwin-Easy / Hard. Many of the compared methods are specialist models fine-tuned for individual benchmarks, while Qwen-VLA is a unified generalist model trained under a single framework.

\n

On vision-language navigation (VLN-CE), Qwen-VLA-Instruct achieves 69.0% Oracle Success Rate and 57.5% Success Rate on R2R Val-Unseen, and 59.6% SR and 47.8% SPL on the more challenging RxR Val-Unseen, surpassing all open-source baselines.

\n

In real-world ALOHA dual-arm experiments, Qwen-VLA pretrained model achieves 83.6% average in-domain success and 76.9% average OOD success, substantially outperforming training from scratch (48.5% / 36.2%) and π_(0.5) (71.6% / 41.5%).

\n
\n

Real-World Out-of-Distribution Generalization

\n

We also care about how Qwen-VLA generalizes on real robots.

\n

In real-world ALOHA dual-arm robot experiments, Qwen-VLA demonstrates generalization to unseen colors, objects, backgrounds, positions, and language instructions. Compared with policies trained from scratch, models pretrained with Qwen-VLA show clear improvements under real-world out-of-distribution settings.

\n

This part is best shown through videos. The following demonstrations are tested with the Qwen-VLA-Base model. When asked to “pick up the green ball” or “pick up the blue ball,” the model can correctly act based on color-specific instructions. When presented with unseen objects such as toys, vegetables, or sunglasses, it can still follow language commands to grasp or move them. When the background, lighting, and tabletop layout change, the model remains relatively stable. For compositional tasks such as “tidy up the table,” it can identify multiple targets and execute multi-step operations.

\n

Compared with tables alone, these videos better illustrate the core value of Qwen-VLA:

\n
\n

The model is not merely memorizing action templates in a fixed environment. It is learning to understand goals and act under real-world variations.

\n
\n
\n

Zero-Shot Generalization in Dynamic Scenes

\n

Beyond static tabletop manipulation, Qwen-VLA also shows zero-shot generalization in dynamic manipulation tasks.

\n

On the DOMINO dynamic manipulation benchmark, Qwen-VLA-Instruct is not specifically fine-tuned for the benchmark, yet it still achieves a 26.6% success rate and a 39.5 manipulation score, outperforming a range of standard VLA baselines and even some specialist models for dynamic manipulation.

\n

This suggests that the model is not only learning grasping templates in static scenes, but also acquiring a more transferable action prior from spatial understanding to motion control. Given visual observations, language goals, and its action generation capability, the model can directly produce coherent action sequences and complete tasks within dynamic interaction windows.

\n
\n

From Multimodal Understanding to Embodied Intelligence

\n

Qwen-VLA is a natural extension of Qwen’s multimodal capabilities toward embodied intelligence.

\n

In the past, multimodal models mainly focused on understanding the world. With Qwen-VLA, we further explore how models can generate actions in the physical world based on vision and language.

\n

Qwen-VLA unifies robotic manipulation, vision-language navigation, and cross-embodiment control. It connects Qwen’s visual understanding and spatial reasoning capabilities to continuous action generation. Through joint pretraining on real robot data, human egocentric data, synthetic simulation data, and general vision-language data, it learns more general embodied experience. It also demonstrates the potential of generalist policy models across manipulation benchmarks, real-world out-of-distribution generalization, and zero-shot dynamic manipulation.

\n

Embodied intelligence is still at an early stage. Long-horizon real-world tasks, failure recovery, continual learning, and more complex human-robot-environment interactions remain challenging. But Qwen-VLA points to a clear next step:

\n
\n

Models should not only understand the world — they should also learn to act in it.

\n
\n

Citation

\n
@article{qwenvla,\n  title={Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments},\n  author={Qwen Team},\n  year={2026},\n  eprint={2605.30280},\n  archivePrefix={arXiv},\n  primaryClass={cs.RO},\n  url={https://arxiv.org/abs/2605.30280}, \n}\n
\n", "date_published": "2026-05-29T09:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwenvla", "id": "urn:aggr:item:3a3eb0b0a6e23f426d60ff74561e688f5215564b", "image": "https://img.alicdn.com/imgextra/i3/O1CN013MqZ6I1aTeRGXyxAB_!!6000000003331-2-tps-1590-954.png", "summary": "Over the past few years, multimodal large language models have become increasingly capable of understanding images, videos, and real-world scenes. They can recognize objects, reason about spatial relationships, answer visual questions, and…", "tags": [ "ai", "release" ], "title": "Qwen-VLA: From Understanding the World to Acting in It", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-05-29-qwen-vla-from-understanding-the-world-to-acting-in-it/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"Qwen3.7

\n

Today we introduce Qwen3.7-Max, our latest proprietary model designed for the agent era. Qwen3.7-Max is built to be a versatile agent foundation — equally capable of writing and debugging code, automating office workflows, and sustaining autonomous execution across hundreds or thousands of steps.

\n

What sets Qwen3.7-Max apart is the breadth and depth of its agent capabilities. It excels as a coding agent, from frontend prototyping to complex multi-file engineering. It serves as a reliable office and productivity assistant through MCP integrations and multi-agent orchestration. It sustains coherent reasoning across extremely long horizons — as demonstrated by a 35-hour, fully autonomous kernel optimization run comprising over 1,000 tool calls. It generalizes across agent scaffolds, performing consistently whether deployed through Claude Code, OpenClaw, Qwen Code, or other frameworks.

\n\n

Performance

\n

\"\"

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Opus-4.6 MaxK2.6 ThinkingGLM-5.1 ThinkingDS-V4-Pro MaxQwen3.6-PlusQwen3.7-Max
Coding Agent
Terminal Bench 2.0-Terminus65.466.763.567.961.669.7
SWE-Verified80.880.2--80.678.880.4
SWE-Pro57.359.558.859.056.660.6
SWE-Multilingual77.576.7--76.273.878.3
NL2repo47.642.841.035.534.447.2
SciCode51.952.245.1--41.453.5
QwenWebDev1617--1564157015001568
QwenSVG154113251605150614321608
General Agent
Qwenclaw65.554.758.759.257.264.3
CoWorkBench68.258.266.066.364.567.2
ClawEval70.461.562.758.457.165.2
Skillsbench--56.253.152.345.759.2
BFCL-V476.771.370.970.668.975.0
MCP-Mark56.755.957.557.148.260.8
MCP-Atlas75.866.671.873.674.176.4
Vitabench--39.145.151.942.847.9
SpreadSheetBench-v189.384.585.284.980.287.0
Kernel Bench L32.63/98%1.41/80%2.00/78%1.07/54%1.03/48%1.98/96%
HLE w/ tools53.054.052.348.250.253.5
QwenWorldBench56.150.950.252.347.657.3
STEM & Reasoning
GPQA Diamond91.390.586.290.190.492.4
HLE40.036.434.737.728.841.4
LiveCodeBench88.889.6--93.587.191.6
HMMT 2026 Feb96.292.789.495.287.897.1
IMOAnswerBench75.386.083.889.883.890.0
CritPT12.68.04.612.92.911.4
Apex34.524.011.538.38.844.5
General Capability
MMLU-Pro89.787.186.387.588.589.6
MMLU-Redux95.295.394.394.894.595.0
SuperGPQA72.571.368.069.971.673.6
IFEval91.994.594.591.994.394.3
IFBench62.576.076.077.074.279.1
MRCR-v2 128k84.063.162.074.485.990.4
Multilingualism
WMT24++82.781.681.882.284.385.8
MAXIFE81.387.787.788.988.289.2
MMMLU90.687.587.287.989.590.3
MMLU-ProX86.183.783.983.984.787.0
NOVA-6359.156.754.652.857.959.0
INCLUDE87.484.284.386.185.186.2
Global PIQA91.289.289.590.589.891.4
PolyMATH80.282.767.672.077.486.5
\n

* Terminal-Bench 2.0: Harbor/Terminus-2 harness; 5h timeout, 12 CPU/24 GB RAM; temp=1.0, top_p=0.95, top_k=20, max_tokens=80K, 256K ctx; avg of 5 runs. All experiments prepend a token at each turn, allowing the model to decide whether to engage extended thinking.
\n* SWE-Bench Series: Internal agent scaffold (bash + file-edit tools); temp=1.0, top_p=0.95, 200K context window.
\n* SWE-bench Pro: Problematic tasks corrected and all baselines evaluated on the refined benchmark.
\n* NL2Repo: Evaluated via Claude-code. We disable Bash commands that attempt to access the specific repository, such as pip download, pip install, and git clone.
\n* QwenWebDev: Internal front-end code generation benchmark; bilingual (EN/CN), 7 categories; auto-render + multimodal judge; BT/Elo rating.
\n* QwenClawBench: a real-user-distribution Claw agent benchmark; open-source: https://github.com/SKYLENAGE-AI/QwenClawBench.
\n* CoWorkBench: an internal cowork benchmark; long-horizon tasks across computer science, finance, law, medical, and other productivity domains.
\n* SkillsBench: Evaluated via OpenCode on 78 tasks (excluding 9 external API-dependent tasks); avg of 5 runs.
\n* MCP-Mark: GitHub MCP v0.30.3; Playwright responses truncated at 32K tokens.
\n* MCP-Atlas: Public set score; gemini-2.5-pro judger.
\n* VITA-Bench: Avg subdomain scores; using claude-4.5-sonnet as judger, as the older official judgers are no longer available.
\n* Kernel Bench L3: Metrics reported: median of per-problem speedup over PyTorch eager reference / fraction of problems faster than torch.compile, across 50 problems. Each test sample runs in an isolated Docker container with one H100 80GB GPU, with internet access restricted to the CUTLASS codebase and official CUDA documentation, limited to 500 tool calls with early stopping after 100 non-improving turns. GPT-5.4 (xhigh) is applied to detect potential hacking behaviors. CUPTI is used for kernel-level timing.
\n* QwenWorldBench: Internal benchmark for evaluating LLMs as world models for simulating agentic environments; 7 domains (Terminal, SWE, MCP, Search, OS, Android, Web); open-ended 5-dim rubric judge grounded in real-environment feedback.
\n* Reasoning scenarios: Recommended system prompt: "Reasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer."
\n* MRCR-v2: 128K context subset containing 8 needles utilized; evaluation protocol adopted from https://github.com/google-deepmind/eval_hub/tree/master/eval_hub/mrcr_v2.
\n* WMT24++: Harder WMT24 subset; avg scores on 55 langs via XCOMET-XXL.
\n* MAXIFE: Accuracy on EN + multilingual prompts (23 settings total).
\n* MMLU-ProX: Avg accuracy across 29 languages.
\n* Empty cells (--) indicate scores not yet available.

\n

In coding agents, Qwen3.7-Max performs strongly on SWE-Pro (60.6), SWE-Multilingual (78.3), SciCode (53.5), and QwenSVG (1608). On Terminal Bench 2.0-Terminus (69.7), it outperforms DS-V4-Pro Max (67.9). On SWE-Verified (80.4), it is on par with Opus-4.6 Max (80.8) and DS-V4-Pro Max (80.6).

\n

In general-purpose agents, improvements are even more pronounced. Qwen3.7-Max performs exceptionally well on MCP-Mark (60.8 vs. GLM-5.1’s 57.5), MCP-Atlas (76.4 vs. Opus-4.6’s 75.8), and Skillsbench (59.2 vs. K2.6’s 56.2), and demonstrates strong GPU kernel optimization capabilities on Kernel Bench L3 (1.98x median speedup, 96% win rate). It also scores highly on BFCL-V4 (75.0), Qwenclaw (64.3), and ClawEval (65.2), closely approaching Opus-4.6 Max. On the office automation benchmark SpreadSheetBench-v1, it achieves a top-tier score of 87.

\n

In reasoning, Qwen3.7-Max achieves leading results on GPQA Diamond (92.4 vs. Opus-4.6’s 91.3), HLE (41.4 vs. Opus-4.6’s 40), HMMT 2026 Feb (97.1 vs. Opus-4.6’s 96.2), IMOAnswerBench (90 vs. DS-V4-Pro’s 89.8), and Apex (44.5 vs. DS-V4-Pro’s 38.3), demonstrating exceptional strength on the hardest reasoning benchmarks.

\n

In general capabilities and multilingualism, Qwen3.7-Max stands out on IFBench (79.1 vs. DS-V4-Pro’s 77.0), demonstrating precise instruction following. It achieves leading scores on WMT24++ (85.8) and MAXIFE (89.2), confirming top-tier multilingual understanding and translation quality. It also delivers strong results on SuperGPQA (73.6) and QwenWorldBench (57.3).

\n

Notably, these scores are drawn from a wide variety of agent scaffolds. Rather than optimizing for any single framework, Qwen3.7-Max delivers consistently across Claude Code, OpenClaw, Qwen Code, and custom tool-use frameworks, making it a reliable drop-in backbone for any agent system.

\n

Cowork Productivity Assistant

\n

Qwen3.7-Max serves as your advanced coworker for real-world productivity. Its powerful agent capabilities fundamentally streamline professional workflows — synthesizing complex information, performing in-depth data analysis and modeling, and generating publication-ready documents and visualizations — to reliably handle high-complexity enterprise workloads.

\n

Qwen3.7-Max features native compatibility with mainstream agent harnesses. For long-horizon tasks, it supports autonomous planning and continuous execution across multi-hour sessions. Through thousands of tool calls and dozens of refinement iterations, it steadily improves output quality. Complex projects that typically require one to two weeks of specialized team effort can now be completed end-to-end within hours, delivering measurable productivity gains.

\n

Agent Scaling

\n

Building on the environment scaling approach introduced in Qwen3.5, we have continued to aggressively expand both the quality and diversity of agentic training environments in Qwen3.7. Just as language models generalize from diverse pretraining text, we find that agentic capabilities generalize from diverse training environments.

\n

As shown in the figure below, this environment scaling produces a clear and consistent improvement trajectory, with Qwen3.7-Max achieving a top-3 average ranking that approaches Claude-4.6-Opus-Max. Crucially, all benchmarks in our evaluation feature entirely unseen, out-of-domain environments that were never present in training.

\n

We also observe a striking predictability in the scaling behavior: performance gains across any subset of benchmarks are highly consistent and can reliably predict the relative gains on the remaining benchmarks or the overall average, suggesting that environment scaling drives genuine capability generalization rather than benchmark-specific improvement. Further analysis of the scaling dynamics and methodology will be detailed in our upcoming technical report.

\n

\"\"

\n

Cross-Harness Generalization

\n

Our Rollout environment infrastructure decouples each training instance into three orthogonal components — Task, Harness, and Verifier — that can be freely recombined. We support a wide range of harnesses and their evolving versions, and ground our environments in real-world settings rather than synthetic proxies. This decoupled design enables combinatorial scaling: the same task is paired with diverse harnesses (across types and versions) and verifiers at minimal marginal cost. More critically, it enables cross-harness and cross-verifier RL training, where the model encounters identical tasks under varying harness configurations, forcing it to learn generalizable problem-solving strategies rather than harness-specific shortcuts. Across QwenClawBench and CoWorkBench, Qwen3.7-Max delivers strong, consistent performance regardless of the harness used at evaluation time, confirming that the model has learned to solve tasks — not to exploit particular harnesses.

\n

\"\"

\n

Self-Evolving in the Wild

\n

Extend Attention is a production-grade, variable-length multi-head attention operator in SGLang. In our test scenario, it computes attention scores between newly generated tokens and a prefix KV-cache of up to 32K entries with MTP — a memory-bound, latency-critical kernel in LLM serving. The reference implementation is SGLang’s official Triton implementation.

\n

We tasked Qwen3.7-Max with optimizing this kernel on an ECS instance equipped with T-Head ZW-M890 PPUs — a hardware platform never seen during training. The model had no prior profiling data, no hardware documentation, and no example kernels for this architecture. It started from an empty workspace containing only a task description, the existing SGLang implementation, and an evaluation script.

\n

Over the course of ~35 hours of continuous autonomous execution, the model performed 432 kernel evaluations across 1,158 tool calls. It wrote, compiled, profiled, and iteratively improved the Extend Attention Kernel entirely on its own — diagnosing compilation failures, fixing correctness bugs, identifying performance bottlenecks through runtime profiling, and redesigning the kernel architecture multiple times.

\n

The final result: 10.0x geometric mean speedup over the Triton reference, measured across multiple workloads. The optimization trajectory shows sustained, non-trivial progress far beyond the first few hours: the model was still finding meaningful improvements after 30+ hours, demonstrating that long-horizon autonomous optimization is not just feasible but productive.

\n

docs.qwenlm.ai

\n

Key structural transitions in the optimization trajectory

\n
    \n
  1. \n

    Split-KV parallelism (0.33x → 2.58x, ~2h): The initial kernel launched only 8 blocks (4 tokens × 2 KV heads × 1 batch) on 36 SMs, leaving most SMs idle. The model redesigned the kernel with Split-KV partitioning — dividing the prefix KV-cache across multiple thread blocks per query — and introduced a separate reduction kernel using online softmax rescaling to merge partial results.

    \n
    2. \n
  2. \n

    Launch and allocation overhead removal (2.58x → 5.37x, ~2.5h): The model systematically removed host-device synchronization overhead: replacing per-call cudaMalloc/cudaFree with pre-allocated torch::empty tensors, eliminating synchronous cudaMemcpy calls for prefix length queries by using tensor metadata instead, and unrolling the inner loop 2x to amortize loop control overhead and increase instruction-level parallelism.

    \n
    3. \n
  3. \n

    Workload-adaptive split tuning (5.37x → 6.85x, ~3h): The model evolved from a fixed split divisor to workload-size-dependent heuristics — applying more aggressive splitting for smaller inputs and tuning per-workload split counts to maximize SM wave occupancy on the 36-SM architecture.

    \n
    4. \n
  4. \n

    Reduction and batching optimizations (6.85x → 8.50x, 3h–25h): Eliminating shared memory barriers by switching to register-based K/V loading for higher SM occupancy, persistent static tensors for partial results to avoid per-call allocation, more aggressive split heuristics for small inputs, and a batched softmax update (4 expf calls instead of 6) to reduce per-token overhead. Q pre-scaling by sm_scale eliminated a per-iteration floating-point multiply after warp reduction.

    \n
    5. \n
  5. \n

    MTP γ=4 specialized kernel (8.50x → 10.0x, 32h–35h): The most significant architectural redesign — restructuring the kernel to process all 4 query tokens simultaneously per block, sharing K/V loads across queries to amortize memory access cost. Combined with __ldg read-only cache intrinsics for V buffer loads, multi-query batched attention output reduction, register pressure tuning, and re-tuned split heuristics, this delivered the final ~1.2x improvement in the last hours.

    \n
    6. \n
\n

We also ran the same task with several other models under identical conditions. GLM 5.1 reached 7.3x; Kimi K2.6 reached 5.0x; DeepSeek V4 Pro reached 3.3x; Qwen3.6-Plus reached 1.1x. Models that stopped early did so because the agent issued no tool calls for five consecutive rounds — the model concluded it could no longer make progress and voluntarily ended the session.

\n

In addition to achieving strong kernel generation results on PPUs, Qwen3.7-Max also generates high-quality, production-grade kernels across a variety of NVIDIA GPUs. For example, on KernelBench L3, Qwen3.7-Max is able to produce accelerated kernels for 96% of the scenarios, compared to 98% for Opus-4.6, 78% for GLM 5.1, 80% for Kimi K2.6, 54% for DeepSeek V4 Pro, and 48% for Qwen3.6-Plus.

\n

This result highlights two properties of Qwen3.7-Max as a foundation model powering long-horizon autonomous agents: sustained long-horizon reasoning — the model maintains coherent optimization strategy across over a thousand tool calls without losing context or regressing — and strong in-context generalization — it produces competitive kernels for an architecture it has never encountered, relying on runtime feedback rather than memorized hardware knowledge.

\n

Reward Hacking Monitoring for Long-Horizon Training

\n

We integrated Qwen3.7-Max into the Reinforcement Learning (RL) monitoring for Software Engineering (SWE) tasks, successfully building a framework for reward hacking self-monitoring and rule self-evolution. During RL experiments exceeding 80 hours, the model autonomously retrieved and replayed training trajectories, executing over 10,000 calls. The system systematically identified candidate hacking patterns (such as attempts to bypass constraints to access ground-truth answers on GitHub) while performing rule verification, counter-example mining, and iterative optimization.

\n

As a result, Qwen3.7-Max achieved multiple rounds of rule self-evolution, adding 13 new heuristic rules and accurately flagging 1,618 hacking cases. This not only ensured the stability of RL rewards but also facilitated the continuous self-improvement of the model as a sophisticated software engineering agent.

\n

\"\"

\n

Long-Horizon Planning and Execution in Startup Management

\n

Within the framework of Dynamic Cumulative Survival Games, we have scaled the temporal complexity of training tasks to specifically reinforce long-horizon planning and execution capabilities. This advancement enhances the agent’s policy consistency throughout sequential decision-making trajectories exceeding a thousand steps, enabling it to continuously construct hypotheses, dynamically adjust strategies based on environmental feedback, and accumulate long-term experience and memory. Consequently, the agent maintains a stable execution cadence over vast time horizons, remaining resilient to the common pitfalls of context rot and instruction drift.

\n

In YC-Bench — a benchmark simulating the full year-long lifecycle of a startup — the agent must navigate hundreds of decision-making rounds ranging from personnel management and contract screening to malicious client identification, all while maintaining a profit margin against rising labor costs. Qwen3.7-Max achieved a total revenue of 2.08M USD, which is double the performance of Qwen3.6-Plus (1.05M USD) and 5.9 times that of Qwen3.5-Plus (352K USD), successfully completing 237 tasks. Beyond the metrics, the model demonstrated a profound capacity for strategic evolution across context windows: it actively explored potential clients, identified and blacklisted malicious traps, prioritized reliable revenue streams, and autonomously recovered from mid-term crises to eventually converge into a stable, high-efficiency execution loop.

\n

docs.qwenlm.ai

\n

Build with Qwen3.7

\n

Qwen3.7-Max is now available through Alibaba Cloud Model Studio. You can integrate it with popular agent frameworks and coding assistants.

\n

API Usage

\n

Qwen3.7-Max supports the preserve_thinking feature: preserving thinking content from all preceding turns in messages, which is recommended for agentic tasks.

\n

Alibaba Cloud Model Studio

\n

Alibaba Cloud Model Studio supports industry-standard protocols, including chat completions and responses APIs compatible with OpenAI’s specification, as well as an API interface compatible with Anthropic.

\n
"""\nEnvironment variables:\n  DASHSCOPE_API_KEY: Your API Key from https://modelstudio.console.alibabacloud.com\n  DASHSCOPE_BASE_URL: (optional) Base URL for compatible-mode API.\n    - Beijing: https://dashscope.aliyuncs.com/compatible-mode/v1\n    - Singapore: https://dashscope-intl.aliyuncs.com/compatible-mode/v1\n    - US (Virginia): https://dashscope-us.aliyuncs.com/compatible-mode/v1\n"""\nfrom openai import OpenAI\nimport os\n\napi_key = os.environ.get("DASHSCOPE_API_KEY")\nif not api_key:\n    raise ValueError(\n        "DASHSCOPE_API_KEY is required. "\n        "Set it via: export DASHSCOPE_API_KEY='your-api-key'"\n    )\n\nclient = OpenAI(\n    api_key=api_key,\n    base_url=os.environ.get(\n        "DASHSCOPE_BASE_URL",\n        "https://dashscope-intl.aliyuncs.com/compatible-mode/v1",\n    ),\n)\n\nmessages = [{"role": "user", "content": "Write a Python function to merge two sorted linked lists."}]\n\ncompletion = client.chat.completions.create(\n    model="qwen3.7-max",\n    messages=messages,\n    extra_body={\n        "enable_thinking": True,\n        # "preserve_thinking": True,\n    },\n    stream=True\n)\n\nreasoning_content = ""\nanswer_content = ""\nis_answering = False\nprint("\\n" + "=" * 20 + "Reasoning" + "=" * 20 + "\\n")\n\nfor chunk in completion:\n    if not chunk.choices:\n        print("\\nUsage:")\n        print(chunk.usage)\n        continue\n\n    delta = chunk.choices[0].delta\n\n    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:\n        if not is_answering:\n            print(delta.reasoning_content, end="", flush=True)\n        reasoning_content += delta.reasoning_content\n\n    if hasattr(delta, "content") and delta.content:\n        if not is_answering:\n            print("\\n" + "=" * 20 + "Answer" + "=" * 20 + "\\n")\n            is_answering = True\n        print(delta.content, end="", flush=True)\n        answer_content += delta.content\n
\n

For more information, please visit the API doc.

\n

Frontend Coding

\n

Qwen3.7-Max can generate rich interactive web applications from a single prompt — including Three.js 3D scenes, Canvas animations, full page layouts, and dynamic SVG.

\n

Gesture Controlled Particles System Next

\n

User

\n

用Three.js创建一个实时交互的3D粒子系统网页。要求：1.通过摄像头检测手掌张合控制粒子群的收缩与扩散，当手掌张开时例子扩散，当手掌握紧时例子收缩为一个球；2.当手势为1时，粒子组成文字（hello, world），当手势为2时组成文字 （I’am Qwen）；3.粒子需实时响应手势变化；4.文字应有3D旋转效果；5. 用html实现

\n

Qwen3.7-Max

\n

Fashion Magazine Page with Video-Generation Next

\n

User

\n

Build a “MONOLITH” luxury fashion magazine cover as an interactive web page. You have access to a video generation script at scripts/dashscope-video-gen.sh that can generate AI videos.

\n

STEP 1 — Generate 3 custom videos by running these commands: bash scripts/dashscope-video-gen.sh “Extreme close-up cinematic portrait of a high fashion model, dramatic side lighting, black and white with crimson red lipstick, wind blowing hair slowly, Vogue cover aesthetic, shallow depth of field” assets/demo-videos/monolith-hero.mp4 9:16 5

\n

bash scripts/dashscope-video-gen.sh “Cinematic slow-motion haute couture fashion runway walk, dramatic chiaroscuro spotlight, silhouette emerging from darkness, editorial magazine film look with grain” assets/demo-videos/monolith-knockout.mp4 9:16 5

\n

bash scripts/dashscope-video-gen.sh “Overhead cinematic shot of luxury fashion magazine open pages being slowly turned, golden jewelry and perfume bottle on black marble, dramatic spotlight, editorial still life” assets/demo-videos/monolith-editorial.mp4 16:9 5

\n

STEP 2 — Build the web page using the generated videos: HERO: Full-viewport portrait video (monolith-hero.mp4 as muted autoplay looping background) with duotone overlay. Knockout headline “MONOLITH” revealing monolith-knockout.mp4 playing through transparent text. EDITORIAL: Below-fold split layout with monolith-editorial.mp4 in a magazine-page container with page-curl hover effect, and pull-quotes with architectural line accents. DESIGN: Monochrome + crimson accent, grain overlay, minimal masthead, scroll-triggered animations, sticky top bar with issue number and date.

\n

Do not ask follow-up questions. Do not wait for user input. Pick reasonable defaults and complete the artifact. Write all code to a single index.html file. Run the video generation commands first, wait for them to complete, then build the HTML.

\n

Qwen3.7-Max

\n

Instancing Dynamic Next

\n

User

\n

I want to build a Three.js WebGL page that shows instancing dynamic. Can you make it a single standalone HTML file that works when I just open it in a browser?

\n

Qwen3.7-Max

\n

3D Racing Game Next

\n

User

\n

创建一个3D赛车游戏，补充自动玩游戏的auto模式, 优化赛车游戏的轨道，障碍物，另外请补充金币等机制。

\n

Qwen3.7-Max

\n

Dragon Boat Next

\n

User

\n

生成一段 SVG 代码，用于表现一幅充满动感的龙舟竞渡矢量插画。画面的主体是一艘细长的龙舟，船上坐满了划手，他们身穿鲜亮的黄绿色上衣和白色帽子，整齐划一地挥动船桨。船桨切入浑浊的棕色河水中，激起白色水花。船尾站着一名指挥者，穿着色彩鲜艳的短裤；船头飘扬着一面蓝色旗帜，上面有黄色数字“15”。背景中，一座巨大的灰色混凝土桥横跨河面，远处在淡蓝色天空下可见朦胧的城市建筑和绿色树木。整体风格采用现代扁平化设计，色彩鲜明、轮廓清晰。请添加流畅的动画，以表现以下动态效果：划手整齐同步的划桨动作、龙舟轻微的上下起伏、旗帜随风飘动，以及水面的涟漪，从而营造出紧张激烈、充满能量的竞赛氛围。

\n

Qwen3.7-Max

\n

Office Assistant

\n

Qwen3.7-Max can act as an intelligent office assistant through tool integration. In this example, it reads a university thesis formatting specification and automatically reformats a messy draft — fixing page layout, heading styles, fonts, margins, table of contents, and reference formatting — all through autonomous office-cli tool calls. (The sample thesis is AI-generated for demonstration purposes.)

\n

Thesis Formatting with Office Tools Next

\n

To facilitate front-end display, the original Word document is specifically displayed here as a PDF.

\n

User

\n

请完成一个论文格式修复任务。 ## 输入文件 - 格式规范说明文件: 研究生学位论文格式规范.docx - 格式混乱版论文（待修复）: 论文_格式混乱版.docx ## 输出文件 - 论文_格式修复版.docx

\n

Workspace

\n

研究生学位论文格式规范.docx

\n

论文_格式混乱版.docx

\n

Qwen3.7-Max

\n

论文_格式修复版.docx

\n

LLM-Powered Phyicial-World Navigation Agent

\n

One more thing, Qwen3.7-Max now can operate a robot dog through tool-use calls — performing physical understanding, planning, memory and decision-making in physical environments, powered by our robotics agent harness Qwen-RobotClaw, navigation foundation model Qwen-RobotNav, and several vision tools built with Qwen-plus model. In the demo below, the left panel shows a 20 mins agent’s tool-call interaction flow in the physical-world; the center shows the quadruped robot’s first-person view along its trajectory, and the right shows the agent’s long-term memory.

\n

Coding Assistants

\n

Qwen3.7-Max integrates seamlessly with popular agent frameworks and coding assistants:

\n

Claude Code

\n

Qwen APIs support the Anthropic API protocol, enabling direct use with Claude Code:

\n
npm install -g @anthropic-ai/claude-code\n\nexport ANTHROPIC_MODEL="qwen3.7-max"\nexport ANTHROPIC_SMALL_FAST_MODEL="qwen3.7-max"\nexport ANTHROPIC_BASE_URL=https://dashscope-intl.aliyuncs.com/apps/anthropic\nexport ANTHROPIC_AUTH_TOKEN=<your_api_key>\n\nclaude\n
\n

OpenClaw

\n

Connect to OpenClaw via Model Studio:

\n
curl -fsSL https://molt.bot/install.sh | bash\nexport DASHSCOPE_API_KEY=<your_api_key>\nopenclaw dashboard\n
\n

Configure ~/.openclaw/openclaw.json:

\n
{\n  "models": {\n    "mode": "merge",\n    "providers": {\n      "modelstudio": {\n        "baseUrl": "https://dashscope-intl.aliyuncs.com/compatible-mode/v1",\n        "apiKey": "DASHSCOPE_API_KEY",\n        "api": "openai-completions",\n        "models": [\n          {\n            "id": "qwen3.7-max",\n            "name": "qwen3.7-max",\n            "reasoning": true,\n            "input": ["text"],\n            "contextWindow": 1000000,\n            "maxTokens": 65536\n          }\n        ]\n      }\n    }\n  },\n  "agents": {\n    "defaults": {\n      "model": {\n        "primary": "modelstudio/qwen3.7-max"\n      }\n    }\n  }\n}\n
\n

Qwen Code

\n

Qwen Code is deeply optimized for the Qwen series:

\n
npm install -g @qwen-code/qwen-code@latest\nqwen\n
\n

Summary

\n

Qwen3.7-Max is our most versatile and capable model for agent-driven workflows. From coding and office automation to long-horizon autonomous tasks, it combines frontier-level reasoning with robust cross-scaffold generalization and the ability to sustain productive execution over extended periods — providing a powerful foundation for building the next generation of AI agents. We welcome community feedback and look forward to seeing what you build.

\n

Citation

\n
@misc{qwen37,\n    title = {{Qwen3.7}: The Agent Frontier},\n    url = {https://qwen.ai/blog?id=qwen3.7},\n    author = {{Qwen Team}},\n    month = {May},\n    year = {2026}\n}\n
\n", "date_published": "2026-05-20T02:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen3.7", "id": "urn:aggr:item:66b8699906f26abf58a4e0c3de00ce3c5b36f0ea", "image": "https://img.alicdn.com/imgextra/i2/O1CN01za5jOD2214yBUb8F5_!!6000000007059-2-tps-1590-954.png", "summary": "Today we introduce Qwen3.7-Max, our latest proprietary model designed for the agent era. Qwen3.7-Max is built to be a versatile agent foundation — equally capable of writing and debugging code, automating office workflows, and sustaining…", "tags": [ "ai", "release" ], "title": "Qwen3.7: The Agent Frontier", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-05-20-qwen3-7-the-agent-frontier/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"Qwen3.5-LiveTranslate

\n

DashScope Demo

\n

Qwen3.5-LiveTranslate-Flash is the latest simultaneous interpretation model in the Qwen family, built on top of Qwen3.5-Omni. It delivers real-time, multimodal translation that not only hears and translates speech, but also sees and understands visual context to produce more accurate translations. Compared with its predecessor Qwen3-LiveTranslate, Qwen3.5-LiveTranslate-Flash brings major upgrades across language coverage, latency, voice cloning, and terminology handling, making it well-suited for international meetings, livestream localization, online classrooms, and business negotiations.

\n

Key Highlights

\n\n

Performance

\n

We evaluate Qwen3.5-LiveTranslate-Flash in both offline and real-time (streaming) settings.

\n

Offline Translation

\n

On public multilingual speech translation benchmarks (FLEURS, CoVoST2), Qwen3.5-LiveTranslate-Flash achieves higher translation accuracy than mainstream commerical large speech models, significantly surpasses its predecessor Qwen3-LiveTranslate-Flash, and delivers breakthroughs in both language coverage and translation quality.

\n

Overview English → X Next

\n

\"image\"

\n

Overview X → English Next

\n

\"image\"

\n

FLEURS English → X Next

\n

\"image\"

\n

FLEURS X → English Next

\n

\"image\"

\n

CoVoST2 English → X Next

\n

\"image\"

\n

CoVoST2 X → English Next

\n

\"image\"

\n

Real-Time Translation

\n

With the Readable Unit streaming strategy, Qwen3.5-LiveTranslate-Flash reduces first-token latency by 3.45 s and per-token latency by 1.88 s compared to Qwen3-LiveTranslate-Flash, achieving an average speech-to-speech per-token latency of 2.8 s, with virtually no loss in translation quality.

\n

Overview Next

\n

\"image\"

\n

Model Architecture

\n

Qwen3.5-LiveTranslate is a translation large model built on the Qwen3.5-Omni Thinker-Talker architecture. The Thinker receives interleaved visual and audio inputs and generates text translations, while the Talker takes the translated text and source audio to produce speech with crosslingual voice cloning. For real-time simultaneous interpretation, we adopt a chunk-wise streaming input mechanism and introduce Readable Unit tags to control speech synthesis granularity, effectively reducing interpretation latency. Meanwhile, dynamic crosslingual voice cloning enables the model to preserve the speaker’s original vocal characteristics during real-time translation.

\n

\"Qwen3.5-LiveTranslate

\n

Qwen3.5-LiveTranslate model architecture overview

\n

More Supported Languages

\n

Compared to Qwen3-LiveTranslate, Qwen3.5-LiveTranslate significantly expands language coverage. The support of input audio and output text grows from 18 to 60 languages, and output audio support from 10 to 29 languages, enabling a far wider range of cross-lingual translation combinations across global scenarios.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Qwen3-LiveTranslateQwen3.5-LiveTranslate
Input ModalityAudio / VideoAudio / Video
Inference ModeOffline / StreamingOffline / Streaming
Voice Cloning✗✓ (3 modes: pre-registered / clone-once / real-time)
HotwordsUp to 1,000Up to 1,000
Input Audio Languages & Output Text Languages18 languages Chinese, English, Russian, French, German, Portuguese, Spanish, Italian, Indonesian, Korean, Japanese, Vietnamese, Thai, Arabic, Cantonese, Hindi, Greek, Turkish60 languages Afrikaans, Arabic, Asturian, Azerbaijani, Basque, Belarusian, Bengali, Bosnian, Bulgarian, Cantonese, Catalan, Cebuano, Chinese, Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Interlingua, Italian, Japanese, Javanese, Kannada, Kazakh, Korean, Kyrgyz, Lingala, Latvian, Lithuanian, Macedonian, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Norwegian Bokmål, Nynorsk, Odia, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tajik, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uyghur, Vietnamese
Output Audio Languages10 languages Chinese, English, French, German, Russian, Italian, Spanish, Portuguese, Japanese, Korean29 languages Chinese, English, German, Italian, Portuguese, Spanish, Japanese, Korean, French, Russian, Thai, Indonesian, Arabic, Vietnamese, Turkish, Finnish, Polish, Hindi, Dutch, Czech, Urdu, Filipino, Swedish, Danish, Hebrew, Icelandic, Malay, Norwegian, Persian
\n

🎬 See It in Action

\n

International Meeting

\n

A multilingual business meeting where participants speak in different languages and switch between them mid-sentence. Qwen3.5-LiveTranslate handles code-switching, diverse accents, and domain-specific terminology in real time — delivering fluent, natural translations without missing a beat.

\n

Traveling Abroad

\n

A real-world travel scenario powered by Qwen AI Glasses: a Chinese tourist orders food at a local restaurant in Thailand. The model performs live Thai-to-Chinese translation on-device, combining visual context from the menu with spoken dialogue to produce accurate, context-aware translations — making cross-language communication effortless on the go.

\n

Livestream Scenarios

\n

E-commerce livestream translation scenario. Qwen3.5-LiveTranslate accurately translates product specifications and numerical information, ensuring precise cross-language delivery of product parameters.

\n

Classical Chinese Translation

\n

A scene from Romance of the Three Kingdoms narrated in classical Chinese (文言文). Qwen3.5-LiveTranslate accurately interprets and translates archaic Chinese prose into modern English, demonstrating its ability to handle literary and historical language beyond everyday speech.

\n

Visual Disambiguation

\n

Qwen3.5-LiveTranslate leverages visual context to resolve translation ambiguities. When a word or phrase has multiple possible meanings, the model uses what it sees — on-screen text, objects, or scene context — to select the correct interpretation, producing translations that are both accurate and contextually grounded.

\n

Using Qwen3.5-LiveTranslate via DashScope API

\n
import os\nimport time\nimport base64\nimport asyncio\nimport json\nimport websockets\nimport pyaudio\nimport queue\nimport threading\nimport traceback\n\n\nclass LiveTranslateClient:\n    """Client for the DashScope live-translation service: captures mic audio, sends it to the server, and plays back the translated speech."""\n\n    def __init__(self, api_key: str, target_language: str = "en", *, audio_enabled: bool = True):\n        if not api_key:\n            raise ValueError("API key cannot be empty.")\n\n        self.api_key = api_key\n        self.target_language = target_language\n        self.audio_enabled = audio_enabled\n        self.ws = None\n        self.api_url = "wss://dashscope.aliyuncs.com/api-ws/v1/realtime?model=qwen3.5-livetranslate-flash-realtime"\n\n        # Audio input parameters (microphone capture)\n        self.input_rate = 16000\n        self.input_chunk = 1600\n        self.input_format = pyaudio.paInt16\n        self.input_channels = 1\n\n        # Audio output parameters (local playback)\n        self.output_rate = 24000\n        self.output_chunk = 2400\n        self.output_format = pyaudio.paInt16\n        self.output_channels = 1\n\n        # Runtime state and playback resources\n        self.is_connected = False\n        self.audio_player_thread = None\n        self.audio_playback_queue = queue.Queue()\n        self.pyaudio_instance = pyaudio.PyAudio()\n\n    async def connect(self):\n        """Open a WebSocket connection to the translation service."""\n        headers = {"Authorization": f"Bearer {self.api_key}"}\n        try:\n            self.ws = await websockets.connect(self.api_url, additional_headers=headers)\n            self.is_connected = True\n            print(f"Successfully connected to server: {self.api_url}")\n            await self.configure_session()\n        except Exception as e:\n            print(f"Connection failed: {e}")\n            self.is_connected = False\n            raise\n\n    async def configure_session(self):\n        """Configure the translation session: target language, audio formats, and optional features."""\n        config = {\n            "event_id": f"event_{int(time.time() * 1000)}",\n            "type": "session.update",\n            "session": {\n                # `modalities` decides what the server returns:\n                #   ["text", "audio"] — both translated text and synthesized speech (recommended)\n                #   ["text"]          — translated text only\n                "modalities": ["text", "audio"] if self.audio_enabled else ["text"],\n                "input_audio_format": "pcm",\n                "output_audio_format": "pcm",\n                # `input_audio_transcription`: enable source-language ASR.\n                # Setting `model` to 'qwen3-asr-flash-realtime' also streams back the source transcript.\n                # "input_audio_transcription": {\n                #     "model": "qwen3-asr-flash-realtime",\n                #     "language": "zh"  # source language; defaults to 'en'\n                # },\n                "translation": {\n                    "language": self.target_language,\n                    # `corpus`: register hotwords to boost accuracy on proper nouns and domain-specific terms.\n                    # "corpus": {\n                    #     "phrases": {\n                    #         "人工智能": "Artificial Intelligence",\n                    #         "机器学习": "Machine Learning"\n                    #     }\n                    # }\n                }\n            }\n        }\n        print(f"Sending session config: {json.dumps(config, indent=2, ensure_ascii=False)}")\n        await self.ws.send(json.dumps(config))\n\n    async def send_audio_chunk(self, audio_data: bytes):\n        """Base64-encode an audio chunk and send it to the server."""\n        if not self.is_connected:\n            return\n\n        event = {\n            "event_id": f"event_{int(time.time() * 1000)}",\n            "type": "input_audio_buffer.append",\n            "audio": base64.b64encode(audio_data).decode()\n        }\n        await self.ws.send(json.dumps(event))\n\n    async def send_image_frame(self, image_bytes: bytes, *, event_id: str | None = None):\n        """Send an image frame to the server as visual context for translation."""\n        if not self.is_connected:\n            return\n        if not image_bytes:\n            raise ValueError("image_bytes cannot be empty")\n\n        image_b64 = base64.b64encode(image_bytes).decode()\n        event = {\n            "event_id": event_id or f"event_{int(time.time() * 1000)}",\n            "type": "input_image_buffer.append",\n            "image": image_b64,\n        }\n        await self.ws.send(json.dumps(event))\n\n    def _audio_player_task(self):\n        """Background thread task: drain PCM chunks from the playback queue and write them to the speaker output stream."""\n        stream = self.pyaudio_instance.open(\n            format=self.output_format,\n            channels=self.output_channels,\n            rate=self.output_rate,\n            output=True,\n            frames_per_buffer=self.output_chunk,\n        )\n        try:\n            while self.is_connected or not self.audio_playback_queue.empty():\n                try:\n                    audio_chunk = self.audio_playback_queue.get(timeout=0.1)\n                    if audio_chunk is None:  # sentinel: stop the playback loop\n                        break\n                    stream.write(audio_chunk)\n                    self.audio_playback_queue.task_done()\n                except queue.Empty:\n                    continue\n        finally:\n            stream.stop_stream()\n            stream.close()\n\n    def start_audio_player(self):\n        """Spin up the background audio playback thread (no-op when audio output is disabled)."""\n        if not self.audio_enabled:\n            return\n        if self.audio_player_thread is None or not self.audio_player_thread.is_alive():\n            self.audio_player_thread = threading.Thread(target=self._audio_player_task, daemon=True)\n            self.audio_player_thread.start()\n\n    async def handle_server_messages(self, on_text_received):\n        """Continuously receive and dispatch event messages pushed by the server."""\n        try:\n            async for message in self.ws:\n                event = json.loads(message)\n                event_type = event.get("type")\n                if event_type == "response.audio.delta" and self.audio_enabled:\n                    audio_b64 = event.get("delta", "")\n                    if audio_b64:\n                        audio_data = base64.b64decode(audio_b64)\n                        self.audio_playback_queue.put(audio_data)\n\n                elif event_type == "response.done":\n                    print("\\n[INFO] Response complete.")\n                    usage = event.get("response", {}).get("usage", {})\n                    if usage:\n                        print(f"[INFO] Token usage: {json.dumps(usage, indent=2, ensure_ascii=False)}")\n                # Receive source-language ASR results (requires input_audio_transcription.model to be enabled)\n                # elif event_type == "conversation.item.input_audio_transcription.text":\n                #     stash = event.get("stash", "")  # streaming partial result, not yet finalized\n                #     print(f"[Recognizing] {stash}")\n                # elif event_type == "conversation.item.input_audio_transcription.completed":\n                #     transcript = event.get("transcript", "")  # final transcript for an utterance\n                #     print(f"[Source] {transcript}")\n                # In voice + text mode, the translation text arrives alongside synthesized audio under the `transcript` field\n                elif event_type == "response.audio_transcript.done":\n                    print("\\n[INFO] Translation complete.")\n                    text = event.get("transcript", "")\n                    if text:\n                        print(f"[INFO] Translation: {text}")\n                # In text-only mode, the translation arrives via response.text.done under the `text` field\n                elif event_type == "response.text.done":\n                    print("\\n[INFO] Translation complete.")\n                    text = event.get("text", "")\n                    if text:\n                        print(f"[INFO] Translation: {text}")\n\n        except websockets.exceptions.ConnectionClosed as e:\n            print(f"[WARNING] Connection closed: {e}")\n            self.is_connected = False\n        except Exception as e:\n            print(f"[ERROR] Unknown error during message handling: {e}")\n            traceback.print_exc()\n            self.is_connected = False\n\n    async def start_microphone_streaming(self):\n        """Continuously capture microphone audio and stream it to the server in real time."""\n        stream = self.pyaudio_instance.open(\n            format=self.input_format,\n            channels=self.input_channels,\n            rate=self.input_rate,\n            input=True,\n            frames_per_buffer=self.input_chunk\n        )\n        print("Microphone started, please begin speaking...")\n        try:\n            while self.is_connected:\n                audio_chunk = await asyncio.get_event_loop().run_in_executor(\n                    None, stream.read, self.input_chunk\n                )\n                await self.send_audio_chunk(audio_chunk)\n        finally:\n            stream.stop_stream()\n            stream.close()\n\n    async def close(self):\n        """Gracefully close the WebSocket connection and release audio resources."""\n        self.is_connected = False\n        if self.ws:\n            await self.ws.close()\n            print("WebSocket connection closed.")\n\n        if self.audio_player_thread:\n            self.audio_playback_queue.put(None)  # signal the playback thread to exit\n            self.audio_player_thread.join(timeout=1)\n            print("Audio playback thread stopped.")\n\n        self.pyaudio_instance.terminate()\n        print("PyAudio instance released.")\n\n\ndef print_banner():\n    print("=" * 60)\n    print("  Powered by Qwen qwen3.5-livetranslate-flash-realtime")\n    print("=" * 60 + "\\n")\n\n\ndef get_user_config():\n    """Collect runtime parameters from the user via CLI: output mode and target language."""\n    print("Select mode:")\n    print("1. Voice + Text [default] | 2. Text only")\n    mode_choice = input("Enter option (press Enter for Voice + Text): ").strip()\n    audio_enabled = (mode_choice != "2")\n\n    if audio_enabled:\n        lang_map = {\n            "1": "en", "2": "zh", "3": "ru", "4": "fr", "5": "de", "6": "pt",\n            "7": "es", "8": "it", "9": "ko", "10": "ja", "11": "yue"\n        }\n        print("Select target translation language (Voice + Text mode):")\n        print("1. English | 2. Chinese | 3. Russian | 4. French | 5. German | 6. Portuguese | 7. Spanish | 8. Italian | 9. Korean | 10. Japanese | 11. Cantonese")\n    else:\n        lang_map = {\n            "1": "en", "2": "zh", "3": "ru", "4": "fr", "5": "de", "6": "pt", "7": "es", "8": "it",\n            "9": "id", "10": "ko", "11": "ja", "12": "vi", "13": "th", "14": "ar",\n            "15": "yue", "16": "hi", "17": "el", "18": "tr"\n        }\n        print("Select target translation language (Text only mode):")\n        print("1. English | 2. Chinese | 3. Russian | 4. French | 5. German | 6. Portuguese | 7. Spanish | 8. Italian | 9. Indonesian | 10. Korean | 11. Japanese | 12. Vietnamese | 13. Thai | 14. Arabic | 15. Cantonese | 16. Hindi | 17. Greek | 18. Turkish")\n\n    choice = input("Enter option (default is the first one): ").strip()\n    target_language = lang_map.get(choice, next(iter(lang_map.values())))\n\n    return target_language, audio_enabled\n\n\nasync def main():\n    """Program entry point: connect, configure the session, and drive the live-translation loop."""\n    print_banner()\n\n    api_key = os.environ.get("DASHSCOPE_API_KEY")\n    if not api_key:\n        print("[ERROR] Please set the environment variable DASHSCOPE_API_KEY")\n        print("  Example: export DASHSCOPE_API_KEY='your_api_key_here'")\n        return\n\n    target_language, audio_enabled = get_user_config()\n    print("\\nConfiguration complete:")\n    print(f"  - Target language: {target_language}")\n    if not audio_enabled:\n        print("  - Output mode: Text only")\n\n    client = LiveTranslateClient(api_key=api_key, target_language=target_language, audio_enabled=audio_enabled)\n\n    # Callback fired as translated text arrives — stream it to stdout, character by character\n    def on_translation_text(text):\n        print(text, end="", flush=True)\n\n    try:\n        print("Connecting to the translation service...")\n        await client.connect()\n\n        # Launch the audio playback thread (only does real work when audio output is enabled)\n        client.start_audio_player()\n\n        print("\\n" + "-" * 60)\n        print("Connected! Please speak into the microphone.")\n        print("The program will translate your speech in real time and play the results. Press Ctrl+C to exit.")\n        print("-" * 60 + "\\n")\n\n        # Run two coroutines concurrently: server-message handling + microphone audio upload\n        message_handler = asyncio.create_task(client.handle_server_messages(on_translation_text))\n        tasks = [message_handler]\n        # Microphone capture is the translation input source — required regardless of output mode\n        microphone_streamer = asyncio.create_task(client.start_microphone_streaming())\n        tasks.append(microphone_streamer)\n\n        await asyncio.gather(*tasks)\n\n    except KeyboardInterrupt:\n        print("\\n\\nUser interrupted, exiting...")\n    except Exception as e:\n        print(f"\\nFatal error occurred: {e}")\n    finally:\n        print("\\nCleaning up resources...")\n        await client.close()\n        print("Program exited.")\n\n\nif __name__ == "__main__":\n    asyncio.run(main())\n
\n

Future Directions

\n

We will continue exploring the capability boundaries of multimodal translation and focus on the following directions:

\n\n

Citation

\n

Feel free to cite the following article if you find Qwen3.5-LiveTranslate helpful:

\n
@misc{qwen35livetranslateblog,\n    title = {Qwen3.5-LiveTranslate: From Sound to Sight, From Word to Right},\n    url = {https://qwen.ai/blog?id=qwen3.5-livetranslate},\n    author = {Qwen Team},\n    month = {May},\n    year = {2026}\n}\n
\n", "date_published": "2026-05-19T09:40:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen3.5-livetranslate", "id": "urn:aggr:item:b54e1ddcd2fb1814ed65a03dd456f106a023721e", "image": "https://img.alicdn.com/imgextra/i1/O1CN01VmUTIT1HdI4ZLu25U_!!6000000000780-2-tps-1590-954.png", "summary": "DashScope Demo Qwen3.5-LiveTranslate-Flash is the latest simultaneous interpretation model in the Qwen family, built on top of Qwen3.5-Omni. It delivers real-time, multimodal translation that not only hears and translates speech, but also…", "tags": [ "ai", "release" ], "title": "Qwen3.5-LiveTranslate: From Sound to Sight, From Word to Right", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-05-19-qwen3-5-livetranslate-from-sound-to-sight-from-word-to-right/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"Qwen-Scope

\n

HUGGING FACE MODELSCOPE TECHNICAL REPORT

\n

Interpretability research has emerged as a critical area for understanding LLM behaviors, informing performance optimization, and enabling more controllable model outputs. Today, we are excited to introduce Qwen-Scope, an interpretability toolkit trained on the Qwen3 and Qwen3.5 series models. Specifically, we inserted and trained Sparse Autoencoders (SAEs) within Qwen’s hidden layers. By imposing sparsity constraints, SAEs decompose the model’s dense hidden representations into sparse, disentangled, and interpretable features. Qwen-Scope not only sheds light on the internal mechanisms underlying Qwen’s behavior, but also holds potential for model optimization. Application scenarios include controllable inference, data classification and synthesis, model training and optimization, and evaluation sample distribution analysis.

\n\n

Overlook

\n

The weights released in this Qwen-Scope open-source project involve 7 LLMs, covering both dense and MoE models from the Qwen3 and Qwen3.5 series, with a total of 14 sets of SAEs. To ensure broad feature coverage, strong semantic meaningfulness, and stable training, we sampled 0.5B tokens from the pretraining data of the corresponding models to train our SAEs.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
NameBackbone typeSAE widthExpansion factorL0
Dense
SAE-Res-Qwen3-1.7B-Base-W32K-L0_50base32K1650
SAE-Res-Qwen3-1.7B-Base-W32K-L0_100base32K16100
SAE-Res-Qwen3-8B-Base-W64K-L0_50base64K1650
SAE-Res-Qwen3-8B-Base-W64K-L0_100base64K16100
SAE-Res-Qwen3.5-2B-Base-W32K-L0_50base32K1650
SAE-Res-Qwen3.5-2B-Base-W32K-L0_100base32K16100
SAE-Res-Qwen3.5-9B-Base-W64K-L0_50base64K1650
SAE-Res-Qwen3.5-9B-Base-W64K-L0_100base64K16100
SAE-Res-Qwen3.5-27B-W80K-L0_50instruct80K1650
SAE-Res-Qwen3.5-27B-W80K-L0_100instruct80K16100
MoE
SAE-Res-Qwen3-30B-A3B-Base-W32K-L0_50base32K1650
SAE-Res-Qwen3-30B-A3B-Base-W128K-L0_100base128K64100
SAE-Res-Qwen3.5-35B-A3B-Base-W32K-L0_50base32K1650
SAE-Res-Qwen3.5-35B-A3B-Base-W128K-L0_100base128K64100
\n

Applications

\n

Qwen-Scope facilitates the analysis and development of Qwen series models. This section showcases its utility across four dimensions: inference, evaluation, data, and training. Please refer to our technical report for more details.

\n

Inference: Analysis of Model Behavior and Controllable Outputs

\n

By controlling the activation of features, we can achieve targeted control over inference results, such as directed modifications in language, entities, or style, without explicitly providing natural language instructions.

\n

\"Qwen-Scope

\n

Data: Classification and Synthesis

\n

Qwen-Scope performs multi-dimensional analysis and summarization of model representations, enabling its use as a data labeling and classification tool that offers insights into data processing strategies and characterizes data properties. Taking toxic text as an example, we can use seed data to select highly relevant features for sample classification based on the activation states across all features. This process requires no additional training procedures, greatly saving annotation time. Moreover, high classification accuracy can be achieved with only a small amount of data, significantly reducing dependence on large volumes of bootstrapping data.

\n

\"Qwen-Scope

\n

In data synthesis scenarios, Qwen-Scope can also help identify toxic text features in existing data that have been rarely or never activated, and directionally synthesize supplementary samples. Compared to traditional data synthesis approaches, this method offers stronger controllability and targeting capability, enabling more efficient coverage of long-tail capabilities and improving the training data efficiency ratio by approximately 15 times.

\n

\"Qwen-Scope

\n

Training: Targeted Fine-Tuning

\n

The features identified by Qwen-Scope can also be leveraged during the training phase. For instance, when we observe unexpected language mixing in model outputs (e.g., unexpected Chinese words appearing in English responses), we can pinpoint the anomalous activation patterns associated with this issue. During supervised fine-tuning, we then design a loss function specifically targeting these abnormal activations to guide the model in reducing the frequency of such undesirable cases.

\n

\"Qwen-Scope

\n

Another example is the problem of endless repetitive generation, which occurs infrequently and is therefore rarely sampled during reinforcement learning. To address this, we can amplify or highlight the corresponding anomalous activation features, thereby increasing the likelihood of sampling these bad cases. This enables the model to more effectively optimize against repetition issues during the reinforcement learning stage.

\n

\"Qwen-Scope

\n

Evaluation: Addressing Redundancy and Gaps in Test Samples

\n

Evaluation is one of the core aspects of large language model development. As the number of capabilities and dimensions to be evaluated continues to grow, along with increasingly large sample sizes, a key question arises: which evaluation datasets contain redundancies and which domains have insufficient coverage. Through Qwen-Scope, we can analyze the feature coverage of test sets to assess the degree of evaluation redundancy across different benchmark datasets. As shown in the figure below, we found that some commonly used evaluation datasets exhibit overlapping coverage in their activated features, causing certain benchmarks to be affected by repetitive evaluation and thus having relatively lower practical significance. We hope that this type of analytical approach can help users conveniently select test samples and evaluation datasets with higher coverage and lower evaluation costs.

\n

\"Qwen-Scope

\n

Summary

\n

Qwen-Scope is not only a tool for analyzing model behavior, but also enables deep introspection into the internal workings of models, transforming complex parameter computations into human-understandable concepts and patterns. It does more than just “interpret” models—it can actively “improve” them. Empirical results demonstrate that Qwen-Scope provides valuable insights and guidance for model optimization across various stages, including inference, evaluation, data processing, and training. Interpretability is thus not merely a post-hoc analytical tool, but can serve as one of the core engines driving model evolution. We welcome feedback from the community and eagerly look forward to seeing your creativity in action—showcasing even more innovative and interesting use cases!

\n

Demo

\n

You can try Qwen-Scope on Huggingface or modelscope.

\n

Citation

\n

If you think Qwen-Scope offers you any help, feel free to cite.

\n
@misc{qwen_scope,\n      title={{Qwen-Scope}: Turning Sparse Features into Development Tools for Large Language Models},\n      author={Boyi Deng and Xu Wang and Yaoning Wang and Yu Wan and Yubo Ma and Baosong Yang and Haoran Wei and Jialong Tang and Huan Lin and Ruize Gao and Tianhao Li and Qian Cao and Xuancheng Ren and Xiaodong Deng and An Yang and Fei Huang and Dayiheng Liu and Jingren Zhou},\n      year={2026},\n      eprint={2605.11887},\n      archivePrefix={arXiv},\n      primaryClass={cs.CL},\n      url={https://arxiv.org/abs/2605.11887},\n}\n
\n", "date_published": "2026-04-30T04:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen-scope", "id": "urn:aggr:item:7dd56ab13623fe66bf2d5c8b2e8f5555a7cd349f", "image": "https://img.alicdn.com/imgextra/i1/O1CN01Zn57Fv1HbuseMj1D4_!!6000000000777-2-tps-1590-954.png", "summary": "Interpretability research has emerged as a critical area for understanding LLM behaviors, informing performance optimization, and enabling more controllable model outputs. Today, we are excited to introduce Qwen-Scope, an interpretability…", "tags": [ "ai", "open-source" ], "title": "Qwen-Scope: Decoding Intelligence, Unleashing Potential", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-04-30-qwen-scope-decoding-intelligence-unleashing-potential/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"\"

\n

Introduction

\n

Following the release of Qwen3-Next, Gated Delta Network (GDN) has become the workhorse attention layer across the Qwen family — from Qwen3-Next-80B-A3B all the way to the subsequent Qwen3.5 / Qwen3.6 series. As models scale to 397A17B / 122A10B / 35B / 27B and context windows stretch beyond 256K, the overhead of the GDN block in end-to-end training and inference has become non-negligible.

\n

Today we officially open-source FlashQLA: a high-performance linear attention kernel library built on TileLang. FlashQLA applies reasonable operator fusion and performance optimization to the forward and backward passes of GDN Chunked Prefill, achieving 2-3× forward speedup and 2× backward speedup over the FLA Triton kernel across multiple scenarios on NVIDIA Hopper. The efficiency gains are particularly pronounced in pretraining scenarios and edge-side agentic inference.

\n

Key highlights of this release:

\n
    \n
  1. \n

    Gate-driven automatic intra-card context parallelism. By exploiting the exponential decay property of the GDN gate, FlashQLA automatically enables intra-card CP under TP, long-sequence, and small-head-count settings, improving GPU SM utilization.

    \n
    2. \n
  2. \n

    Hardware-friendly algebraic reformulation. We reformulate the forward and backward flows of GDN Chunked Prefill to a certain extent, effectively reducing Tensor Core, CUDA Core, and SFU overhead without sacrificing numerical precision.

    \n
    3. \n
  3. \n

    TileLang fused warp-specialized kernels. Rather than following the step-by-step decomposition into independent kernels, nor fusing the entire computation flow into a single kernel, we take CP and backward requirements into account, use TileLang to build several key fused kernels, and manually implement warpgroup specialization to overlap data movement, Tensor Core computation, and CUDA Core computation.

    \n
    4. \n
\n

FlashQLA code and benchmarks are open-sourced at github.com/QwenLM/FlashQLA.

\n

Key Problems in FLA GDN Chunked Prefill

\n

Let us first review the forward computation flow of GDN Chunked Prefill, taking chunk index i as an example:

\n
    \n
  1. Aᵢ ← (I+StrictLower( diag(βᵢ)(Γᵢ ⊙ KᵢKᵢ^ᵀ) ))⁻¹
    2. \n
  2. {Wᵢ ← Aᵢdiag(βᵢ)diag(γᵢ)Kᵢ; Uᵢ ← Aᵢdiag(βᵢ)Vᵢ
    3. \n
  3. {Vᵢ’ ← Uᵢ−WᵢSᵢ; Sᵢ₊₁ ← γ_(i,C−1)Sᵢ + Kᵢ^ᵀdiag((γ_(i,C−1))/γᵢ)Vᵢ’
    4. \n
  4. Oᵢ ← diag(γ)QᵢSᵢ + (Lower(Γᵢ) ⊙ QᵢKᵢ^ᵀ)Vᵢ′
    5. \n
\n

Ignoring gate preprocessing and CP, each step of this flow corresponds to one kernel in FLA. This flow has two main efficiency problems:

\n
    \n
  1. Most of the above are memory-bound kernels. The flow repeatedly reads K, V and other data, while W, U, S as intermediate variables must be written to HBM and then read by the next kernel, incurring significant memory access overhead.
    2. \n
  2. The recurrent nature of the SSM state means that the corresponding third step chunk_gated_delta_rule_fwd_kernel can only launch batch_size * num_heads thread blocks simultaneously, resulting in low GPU utilization in small-model, small-batch, or TP scenarios.
    3. \n
\n

The solutions to these two problems are contradictory. For the first problem, the most intuitive solution is to write a fully-fused kernel, where all data is accessed only once and all intermediate variables are kept on-chip. When batch_size * num_heads is large enough, this is certainly optimal. However, such a solution obviously runs into the second problem: for edge-side inference with small models and batch_size=1, or for large-model online deployments with TP where long-sequence inputs from coding agents etc. cannot launch a large enough batch for chunked prefill, the speedup of a fully-fused kernel over the original FLA implementation is limited.

\n

The earliest solution to the second problem comes from how DeltaNet does context parallelism, which splits a long sequence into multiple sub-sequences, uses S₀=0 to parallelize the recurrence, and then computes an additional M matrix to correct the recurrent results. This scheme was later optimized to insert a step before the recurrence kernel to compute the S₀ of each sub-sequence, and has now been merged into the FLA repository. For CP rank j, the specific preprocessing flow is:

\n
    \n
  1. {S^∗_(j,i+1) ← γ_(j,i,C−1)S^∗_(j,i) + K_(j,i)^ᵀ diag((γ_(j,i,C−1))/γ_(j,i)) V’_(j,i); M_(j,i+1) ← ( γ_(j,i,C−1) I −K_(j,i)^ᵀ diag((γ_(j,i,C−1))/γ_(j,i)) W_(j,i) ) M_(j,i)
    2. \n
  2. S_(j,0) ← S^∗_(j,0) + M_(j,0)S_(j−1,0)
    3. \n
\n

However, this CP scheme also has its drawbacks: first, it introduces significant extra computation, with the time complexity of recurrently computing the M matrix even exceeding that of the S matrix; second, it does not work well with fully-fused kernels, because matrix inversion and other steps must be performed before the S₀ of each sub-sequence can be computed.

\n

A Balanced Solution: Fusing Kernels While Enabling Intra-Card CP

\n

Based on the two problems above, a compromise solution can be derived: split the GDN Chunked Prefill forward computation into two fused kernels, inserting CP-related preprocessing steps between them. After some transformations and simplifications, the following computation flow is obtained:

\n
    \n
  1. Aᵢ ← (I+StrictLower( diag(βᵢ)KᵢKᵢ^ᵀ))⁻¹
    2. \n
  2. CP Preprocess\n\n
    3. \n
  3. {Vᵢ^Δ ← Vᵢ − diag(γᵢ)KᵢSᵢ; Vᵢ’ ← (Γᵢ ⊙ Aᵢ)diag(βᵢ)Vᵢ^Δ; Sᵢ₊₁ ← γ_(i,C−1)Sᵢ + Kᵢ^ᵀdiag((γ_(i,C−1))/γᵢ)Vᵢ’; Oᵢ ← diag(γᵢ)QᵢSᵢ + (Lower(Γᵢ) ⊙ QᵢKᵢ^ᵀ)Vᵢ’
    4. \n
\n

We also designed a simple mathematical model to automatically determine the degree of parallelism. Let N be the number of chunks in a sequence and L be the number of chunks per CP rank. It is easy to see that the runtime of steps 2.1 and 3 is proportional to L, while the runtime of step 2.2 is proportional to N/L; therefore we can choose L=λ √N to minimize total time, where λ is a coefficient composed of batch_size, num_heads, and other hyperparameters.

\n

In production, intra-card CP is not always needed. Following the original FLA implementation, step 3 can also increase parallelism by 2-4× via splitting v_head_dim, at the cost of redundant memory access to Q and K. Based on measured data, we enable CP only when batch_size * num_heads <= 40 or batch_size * num_heads <= 56 && seq_len >= 8192.

\n

Further Optimization via Gate Decay

\n

Revisiting the GDN recurrence:

\n

Sᵢ₊₁ = αᵢSᵢ(I−βᵢkᵢkᵢ^ᵀ)+βᵢvᵢkᵢ^ᵀ

\n

For αᵢ∈(0,1), the influence of each Sᵢ on subsequent states decays exponentially, giving it a sliding-window property. For a sufficiently long window size of W, starting computation from S_(i−W)=0 can obtain the accurate Sᵢ, without the need to start from S₀. We refer to this process as warmup. On real data, we find that αᵢ is not constantly 1 on 60–80% of linear attention heads, and 6–8 chunks of warmup are sufficient to drive the Sᵢ error below the noise floor.

\n

Therefore, for linear attention heads with the sliding-window property, we can design a lighter CP preprocessing flow that discards the computation of the correction term M and directly obtains an equally accurate sub-sequence S₀ through warmup:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
C0C1C2C3C4C5C6C7C8C9C10C11C12
R1OOOOO
R2XXOOOO
R3XXOOOO
\n

X denotes warming up with a zero initial state until the gate has decayed sufficiently, then writing out the S₀ of that CP rank; O denotes subsequent normal recurrent computation. The warmup length for each rank is determined by an independent kernel that collects gate statistics, and the cost of this step is negligible.

\n

TileLang Warp-Specialized Kernel

\n

We implement FlashQLA in TileLang using a warpgroup-specialization pattern: one producer warpgroup and three consumer warpgroups reside in the same SM, exchange data through shared memory, and synchronize via mbarriers.

\n

Forward

\n

In the forward pass, the three consumer warp groups compute V’, S, and O respectively, overlapping computation and memory traffic through a ping-pong structure.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
WG3WG2WG1WG0
BAR 0LDQLDγSTOγ, γ_(C−1)γ⁻¹TCP = Q K^ᵀ
BAR 1LDKLDβSTSᵢTCU = K SᵢΓ = L(γ I γ⁻¹) A_γ = Γ ⊙ A P_γ = sΓ ⊙ PSᵢ₊₁ = γ_(C−1) Sᵢ
BAR 2LDVW = β (V − γ U)TCO = Q Sᵢ
BAR 3LDATCV^Δ = A_γ WO = sγ O
BAR 4V’ = γ_(C−1)γ⁻¹ V^ΔTCO = O + P_γ V^Δ
BAR 5TCSᵢ₊₁ = Sᵢ₊₁ + K^ᵀ V′
\n

Notes:

\n\n

CP Preprocessing

\n

As mentioned earlier, the CP preprocessing splits into two cases: the original approach (computing both M and S) and the sliding-window approach (computing only S). We designed a single fused kernel that handles both:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
WG3WG2WG1WG0
BAR 0LDKLDγγ_(C−1)γ⁻¹TCX = A^ᵀ K
BAR 1LDVLDβSTSᵢTCU = K Sᵢ Y = −γ_(C−1)γ⁻¹ V + γ_(C−1) UX = −β XSᵢ₊₁ = γ_(C−1) Sᵢ
BAR 2LDAγ^π = γ^π γ_(C−1)γ^π = γ^π γ_(C−1)TCSᵢ₊₁ = Sᵢ₊₁ + X^ᵀ Y
BAR 3TCZ^L = K M^L TCM^L = M^L + X^ᵀ Z^LTCZ^R = K M^R TCM^R = M^R + X^ᵀ Z^R
\n

Notes:

\n\n

Backward

\n

In the backward pass, we reuse the CP preprocessing kernel from the previous section to recompute the S matrix, then fuse bwd_dv, bwd_dhu, bwd_dqkwg, bwd_wy into a single kernel with corresponding algebraic optimizations. Because of on-chip resource constraints, the backward kernel does not use multi-stage pipelining; instead it relies on the long compute chain to hide memory traffic. The full schedule is available in the FlashQLA repo.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
WG3WG2WG1WG0
BAR 00ST$dK$TC P=QK^ᵀγ, γ_(C−1)γ⁻¹
BAR 01TC dV’=KdSᵢ₊₁ dV’=γ_(C−1)γ⁻¹dV′Γ=γ I γ⁻¹ P_γ=sL(Γ)⊙ PdSᵢ = γ_(C−1) dSᵢ₊₁
BAR 02TC dV’=dV’+P_γ^ᵀ dOA_β = A β A_γ = Γ ⊙ A_β
BAR 03TCU=KSᵢ
BAR 04TC dV=A_γ^ᵀ dV’W=V−γ Udγ_(C−1)=∑ Sᵢ ⊙ dSᵢ₊₁
BAR 05ST$dV$LDVdV_γ = −γ dV dγ = ∑ᵢ dV_γ ⊙ UTC dA_γ = dV’Wᵀ TC V’=A_γ W
BAR 06TC dP_γ = dO V’^ᵀ
BAR 07LDKTC dK=V’dSᵢ₊₁^ᵀdA_β = Γ ⊙ dA_γ dγ = dγ + ∑ᵢ dP_γ ⊙ L(P) dγ = dγ − ∑ⱼ dP_γ ⊙ L(P) dP = sL(Γ)⊙ dP_γ
BAR 08dK=γ_(C−1)γ⁻¹dK dγ_(C−1)=∑ K ⊙ dK dγ = −∑ᵢ K ⊙ dKTC dQ=dOSᵢᵀ
BAR 09LDQTC dK=dK+dV_γ Sᵢ^ᵀdQ=sγ dQ dγ = ∑ Q ⊙ dQ
BAR 10LDSTC dQ=dQ+dPK
BAR 11ST$dQ$dγ = dγ + ∑ᵢ dA_β ⊙ A β dγ = dγ − ∑ⱼ dA_β ⊙ A β dβ = ∑ⱼ dA_β ⊙ A dA=dA_β βTC dSᵢ = dSᵢ + K^ᵀ dV_γ
BAR 12TC dK=dK+dP^ᵀ Q
BAR 13TC dA = −A^ᵀ dA A^ᵀ TC A_T = KK^ᵀdO_γ=sγ dO
BAR 14LD$dO$ LDAdβ = dβ + ∑ᵢ dA ⊙ A_T dA_T = β dA dA_S = dA_T + dA_T^ᵀTC dS₀ = dS₀ + Q^ᵀ dO_γ
BAR 15TC dK=dK+dA_S K
\n

Benchmark

\n

We benchmarked FlashQLA against the FLA Triton and FlashInfer baseline (FLA 0.5.0, Triton 3.5.1, FlashInfer 0.6.9, TileLang 0.1.8) on the head configurations used by the Qwen3.5 / Qwen3.6 family — hᵥ ∈ 64, 48, 32, 24, 16, 8, corresponding to TP1 through TP8.

\n

\"FlashQLA

\n

Specifically, the forward (FWD) benchmarks measure single-kernel latency for different models and TP settings under varying batch lengths, while the backward (BWD) benchmarks examine the relationship between total token count within a batch and latency during a single update step.

\n

Selected H200 single-layer forward results:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Model / TPSeqlenh_(qk)hᵥFlashQLAFlashInferFLAvs FLAvs FI
397B/122B TP81x32768280.310ms1.653ms0.913ms2.95×5.33×
397B/122B TP81x16384280.184ms0.833ms0.465ms2.53×4.53×
397B/122B TP824576+8192280.302ms1.242ms0.767ms2.54×4.11×
397B/122B TP41x327684160.486ms1.654ms1.250ms2.57×3.40×
397B/122B TP41x163844160.292ms0.832ms0.623ms2.13×2.85×
27B TP21x327688240.659ms1.616ms1.564ms2.37×2.45×
2B/0.8B TP11x3276816160.493ms1.640ms1.285ms2.60×3.33×
Sym h321x3276832320.877ms1.554ms1.952ms2.23×1.77×
\n

The speedup grows with TP degree because FlashQLA improves SM utilization via intra-card AutoCP in the exact regimes — TP sharding and small head number — where the baseline leaves SMs idle.

\n

Usage

\n

FlashQLA exposes both a high-level API matching FLA’s signature and low-level fwd/bwd entry points:

\n
import torch\nfrom qla import chunk_gated_delta_rule\n\no, final_state = chunk_gated_delta_rule(\n    q=q,                            # [B, T, H_q, K]\n    k=k,                            # [B, T, H_q, K]\n    v=v,                            # [B, T, H_v, V]\n    g=g,                            # [B, T, H_v]\n    beta=beta,                      # [B, T, H_v]\n    scale=scale,\n    initial_state=initial_state,    # optional, [B, H_v, K, V]\n    output_final_state=True,\n    cu_seqlens=cu_seqlens,          # optional, varlen support\n)\n
\n

Requirements: SM90, CUDA 12.8+, PyTorch 2.8+. Install:

\n
git clone https://github.com/QwenLM/FlashQLA.git\ncd FlashQLA && pip install -v .\n
\n

Acknowledgments

\n

FlashQLA is inspired by Flash Linear Attention, FlashInfer and TileLang. We thank these communities for the reference implementations.

\n

Citation

\n

If FlashQLA is useful for your research, please cite:

\n
@misc{flashqla2026,\n    title  = {FlashQLA: Flash Qwen Linear Attention},\n    author = {Zhang, Chengruidong and Lin, Xi and Jiang, Huiqiang and Wang, Zekun and\n              Li, Xiao and Cao, Yizhong and Zhuang, Bohan and Men, Rui and Zhang, Jianwei and\n              Zheng, Bo and Lin, Junyang and Liu, Dayiheng and Zhou, Jingren},\n    year   = {2026},\n    publisher = {GitHub},\n    howpublished = {\\url{https://github.com/QwenLM/FlashQLA}}\n}\n
\n", "date_published": "2026-04-28T02:00:00+00:00", "external_url": "https://qwen.ai/blog?id=flashqla", "id": "urn:aggr:item:ec3446ada7c39c8eaeca9a50472afbae8596466a", "image": "https://img.alicdn.com/imgextra/i2/O1CN01IUOCqg1RG48rlBwP7_!!6000000002083-2-tps-1590-954.png", "summary": "Introduction Following the release of Qwen3-Next, Gated Delta Network (GDN) has become the workhorse attention layer across the Qwen family — from Qwen3-Next-80B-A3B all the way to the subsequent Qwen3.5 / Qwen3.6 series. As models scale…", "tags": [ "ai", "open-source" ], "title": "FlashQLA: CP-/Bwd-Friendly Fused Linear Attention Kernels for GDN", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-04-28-flashqla-cp-bwd-friendly-fused-linear-attention-kernels-for/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"Qwen3.6-27B

\n

QWEN STUDIO HUGGING FACE MODELSCOPE DISCORD

\n

Following the launch of Qwen3.6-Plus and Qwen3.6-35B-A3B, we are excited to open-source Qwen3.6-27B — a dense 27-billion-parameter multimodal model at the scale the community has been asking for most. Still supporting both multimodal thinking and non-thinking modes, Qwen3.6-27B delivers flagship-level agentic coding performance, surpassing the previous-generation open-source flagship Qwen3.5-397B-A17B (397B total / 17B active MoE) across all major coding benchmarks. As a dense architecture, it is straightforward to deploy without MoE routing complexity, making it an ideal choice for developers who need top-tier coding capabilities at a practical, widely-deployable scale. Qwen3.6-27B is now live on Qwen Studio, available through our API, and released as open weights for the community.

\n\n

\"\"

\n

Performance

\n

Below we present comprehensive evaluations of Qwen3.6-27B against both dense and MoE baselines, including our previous-generation open-source flagship Qwen3.5-397B-A17B. Qwen3.6-27B delivers remarkable improvements across agentic coding benchmarks, surpassing models with up to 15x its total parameter count.

\n

Language

\n

Qwen3.6-27B achieves a breakthrough in agentic coding for dense models. With only 27B parameters, it outperforms the Qwen3.5-397B-A17B (397B total / 17B active) on every major coding benchmark — including SWE-bench Verified (77.2 vs. 76.2), SWE-bench Pro (53.5 vs. 50.9), Terminal-Bench 2.0 (59.3 vs. 52.5), and SkillsBench (48.2 vs. 30.0). It also surpasses all peer-scale dense models by a wide margin. On reasoning tasks, Qwen3.6-27B achieves 87.8 on GPQA Diamond, competitive with models several times its size.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Qwen3.5-27BQwen3.5-397B-A17BGemma4-31BClaude 4.5 OpusQwen3.6-35B-A3BQwen3.6-27B
Coding Agent
SWE-bench Verified75.076.252.080.973.477.2
SWE-bench Pro51.250.935.757.149.553.5
SWE-bench Multilingual69.369.351.777.567.271.3
Terminal-Bench 2.041.652.542.959.351.559.3
SkillsBench Avg527.230.023.645.328.748.2
QwenWebBench106811861197153613971487
NL2Repo27.332.215.543.229.436.2
Claw-Eval Avg64.370.748.576.668.772.4
Claw-Eval Pass^346.248.125.059.650.060.6
QwenClawBench52.251.841.752.352.653.4
Knowledge
MMLU-Pro86.187.885.289.585.286.2
MMLU-Redux93.294.993.795.693.393.5
SuperGPQA65.670.465.770.664.766.0
C-Eval90.593.082.692.290.091.4
STEM & Reasoning
GPQA Diamond85.588.484.387.086.087.8
HLE24.328.719.530.821.424.0
LiveCodeBench v680.783.680.084.880.483.9
HMMT Feb 2592.094.888.792.990.793.8
HMMT Nov 2589.892.787.593.389.190.7
HMMT Feb 2684.387.977.285.383.684.3
IMOAnswerBench79.980.974.584.078.980.8
AIME2692.693.389.295.192.794.1
\n

* SWE-Bench Series: Internal agent scaffold (bash + file-edit tools); temp=1.0, top_p=0.95, 200K context window. We correct some problematic tasks in the public set of SWE-bench Pro and evaluate all baselines on the refined benchmark.
\n* Terminal-Bench 2.0: Harbor/Terminus-2 harness; 3h timeout, 32 CPU/48 GB RAM; temp=1.0, top_p=0.95, top_k=20, max_tokens=80K, 256K ctx; avg of 5 runs.
\n* SkillsBench: Evaluated via OpenCode on 78 tasks (self-contained subset, excluding API-dependent tasks); avg of 5 runs.
\n* NL2Repo: Others are evaluated via Claude Code (temp=1.0, top_p=0.95, max_turns=900).
\n* QwenClawBench: A real-user-distribution Claw agent benchmark; temp=0.6, 256K ctx.
\n* QwenWebBench: An internal front-end code generation benchmark; bilingual (EN/CN), 7 categories (Web Design, Web Apps, Games, SVG, Data Visualization, Animation, and 3D); auto-render + multimodal judge (code/visual correctness); BT/Elo rating system.
\n* AIME 26: We use the full AIME 2026 (I & II), where the scores may differ from Qwen 3.5 notes.

\n

Vision Language

\n

Qwen3.6-27B is natively multimodal, supporting both vision-language thinking and non-thinking modes in a single unified checkpoint — the same as Qwen3.6-35B-A3B. It handles images and video alongside text, enabling multimodal reasoning, document understanding, and visual question answering.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Qwen3.5-27BQwen3.5-397B-A17BGemma4-31BClaude 4.5 OpusQwen3.6-35B-A3BQwen3.6-27B
STEM & Puzzle
MMMU82.385.080.480.781.782.9
MMMU-Pro75.079.076.970.675.375.8
MathVista mini87.8--79.3--86.487.4
DynaMath87.786.379.579.782.885.6
VlmsAreBlind96.9--87.2--96.697.0
General VQA
RealWorldQA83.783.972.377.085.384.1
MMStar81.083.877.373.280.781.4
MMBenchEN-DEV-v1.192.6--90.9--92.892.3
SimpleVQA56.067.152.965.758.956.1
Document Understanding
CharXiv RQ79.580.867.968.578.078.4
CC-OCR81.082.075.776.981.981.2
OCRBench89.4--86.1--90.089.4
Spatial Intelligence
ERQA60.567.557.546.861.862.5
CountBench97.897.296.190.696.197.8
RefCOCO avg90.992.3----92.092.5
EmbSpatialBench84.5------84.384.6
RefSpatialBench67.7--4.7--64.370.0
Video Understanding
VideoMME(w sub.)87.087.5--77.786.687.7
VideoMMMU82.384.781.684.483.784.4
MLVU85.986.7--81.786.286.6
MVBench74.677.6--67.274.675.5
Visual Agent
V*93.795.8--67.090.194.7
AndroidWorld64.2--------70.3
\n

* Empty cells (--) indicate scores not yet available or not applicable.

\n

Build with Qwen3.6-27B

\n

Qwen3.6-27B is coming soon to Alibaba Cloud Model Studio. Please stand by until we are fully ready.

\n

Qwen3.6-27B is available as open weights on Hugging Face and ModelScope for self-hosting, and through the Alibaba Cloud Model Studio API. You can also try it instantly on Qwen Studio.

\n

The model can be seamlessly integrated with popular third-party coding assistants, including OpenClaw, Claude Code, and Qwen Code, to streamline development workflows and enable efficient, context-aware coding experiences.

\n

API Usage

\n

This release supports the preserve_thinking feature: preserving thinking content from all preceding turns in messages, which is recommended for agentic tasks.

\n

Alibaba Cloud Model Studio

\n

Alibaba Cloud Model Studio supports industry-standard protocols, including chat completions and responses APIs compatible with OpenAI’s specification, as well as an API interface compatible with Anthropic.

\n

Example code for chat completions API is provided below:

\n
"""\nEnvironment variables (per official docs):\n  DASHSCOPE_API_KEY: Your API Key from https://modelstudio.console.alibabacloud.com\n  DASHSCOPE_BASE_URL: (optional) Base URL for compatible-mode API.\n    - Beijing: https://dashscope.aliyuncs.com/compatible-mode/v1\n    - Singapore: https://dashscope-intl.aliyuncs.com/compatible-mode/v1\n    - US (Virginia): https://dashscope-us.aliyuncs.com/compatible-mode/v1\n  DASHSCOPE_MODEL: (optional) Model name; override for different models.\n"""\nfrom openai import OpenAI\nimport os\n\napi_key = os.environ.get("DASHSCOPE_API_KEY")\nif not api_key:\n    raise ValueError(\n        "DASHSCOPE_API_KEY is required. "\n        "Set it via: export DASHSCOPE_API_KEY='your-api-key'"\n    )\n\nclient = OpenAI(\n    api_key=api_key,\n    base_url=os.environ.get(\n        "DASHSCOPE_BASE_URL",\n        "https://dashscope-intl.aliyuncs.com/compatible-mode/v1",\n    ),\n)\n\nmessages = [{"role": "user", "content": "Introduce vibe coding."}]\n\nmodel = os.environ.get(\n    "DASHSCOPE_MODEL",\n    "qwen3.6-27b",\n)\ncompletion = client.chat.completions.create(\n    model=model,\n    messages=messages,\n    extra_body={\n        "enable_thinking": True,\n        # "preserve_thinking": True,\n    },\n    stream=True\n)\n\nreasoning_content = ""  # Full reasoning trace\nanswer_content = ""  # Full response\nis_answering = False  # Whether we have entered the answer phase\nprint("\\n" + "=" * 20 + "Reasoning" + "=" * 20 + "\\n")\n\nfor chunk in completion:\n    if not chunk.choices:\n        print("\\nUsage:")\n        print(chunk.usage)\n        continue\n\n    delta = chunk.choices[0].delta\n\n    # Collect reasoning content only\n    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:\n        if not is_answering:\n            print(delta.reasoning_content, end="", flush=True)\n        reasoning_content += delta.reasoning_content\n\n    # Received content, start answer phase\n    if hasattr(delta, "content") and delta.content:\n        if not is_answering:\n            print("\\n" + "=" * 20 + "Answer" + "=" * 20 + "\\n")\n            is_answering = True\n        print(delta.content, end="", flush=True)\n        answer_content += delta.content\n
\n

For more information, please visit the API doc.

\n

Coding & Agents

\n

Qwen3.6-27B features excellent agentic coding capabilities and can be seamlessly integrated into popular third-party coding assistants, including OpenClaw, Claude Code, and Qwen Code.

\n

OpenClaw

\n

Qwen3.6-27B is compatible with OpenClaw (formerly Moltbot / Clawdbot), a self-hosted open-source AI coding agent. Connect it to Model Studio to get a full agentic coding experience in the terminal. Get started with the following script:

\n
# Node.js 22+\ncurl -fsSL https://molt.bot/install.sh | bash   # macOS / Linux\n\n# Set your API key\nexport DASHSCOPE_API_KEY=<your_api_key>\n\n# Launch OpenClaw\nopenclaw dashboard # web browser\n# openclaw tui # Open a new terminal and start the TUI\n
\n

On first use, edit ~/.openclaw/openclaw.json to point OpenClaw at Model Studio. Find or create the following fields and merge them — do not overwrite the entire file to preserve your existing settings:

\n
{\n  "models": {\n    "mode": "merge",\n    "providers": {\n      "modelstudio": {\n        "baseUrl": "https://dashscope-intl.aliyuncs.com/compatible-mode/v1",\n        "apiKey": "DASHSCOPE_API_KEY",\n        "api": "openai-completions",\n        "models": [\n          {\n            "id": "qwen3.6-27b",\n            "name": "qwen3.6-27b",\n            "reasoning": true,\n            "input": ["text", "image"],\n            "contextWindow": 131072,\n            "maxTokens": 16384\n          }\n        ]\n      }\n    }\n  },\n  "agents": {\n    "defaults": {\n      "model": {\n        "primary": "modelstudio/qwen3.6-27b"\n      },\n      "models": {\n        "modelstudio/qwen3.6-27b": {}\n      }\n    }\n  }\n}\n
\n

Qwen Code

\n

Qwen3.6-27B is compatible with Qwen Code, an open-source AI agent designed for the terminal and deeply optimized for the Qwen Series. Get started with the following script:

\n
# Node.js 20+\nnpm install -g @qwen-code/qwen-code@latest\n\n# Start Qwen Code (interactive)\nqwen\n\n# Then, in the session:\n/help\n/auth\n
\n

On first use, you’ll be prompted to sign in. You can run /auth anytime to switch authentication methods.

\n

Claude Code

\n

Qwen APIs also support the Anthropic API protocol, meaning you can use it with tools like Claude Code for elevated coding experience:

\n
# Install Claude Code\nnpm install -g @anthropic-ai/claude-code\n\n# Configure environment\nexport ANTHROPIC_MODEL="qwen3.6-27b"\nexport ANTHROPIC_SMALL_FAST_MODEL="qwen3.6-27b"\nexport ANTHROPIC_BASE_URL=https://dashscope-intl.aliyuncs.com/apps/anthropic\nexport ANTHROPIC_AUTH_TOKEN=<your_api_key>\n\n# Launch the CLI\nclaude\n
\n

Summary

\n

Qwen3.6-27B demonstrates that a well-trained dense model can surpass much larger predecessors on the tasks that matter most for developers. At 27 billion parameters — the most widely deployed open-source scale — it outperforms the 397B-parameter Qwen3.5-397B-A17B on every major agentic coding benchmark, while remaining straightforward to deploy and serve. With Qwen3.6-27B joining the roster, the Qwen3.6 open-source family now offers a comprehensive range of models, underscoring a generation where agentic coding achieved breakthroughs across every scale — from the 3B-active Qwen3.6-35B-A3B to the API-accessible Qwen3.6-Plus and Qwen3.6-Max-Preview. We are grateful for the community’s feedback and look forward to seeing what you build with these models. Stay tuned for more from the Qwen team!

\n

Citation

\n

Feel free to cite the following article if you find Qwen3.6-27B helpful:

\n
@misc{qwen36_27b,\n    title = {{Qwen3.6-27B}: Flagship-Level Coding in a 27B Dense Model},\n    url = {https://qwen.ai/blog?id=qwen3.6-27b},\n    author = {{Qwen Team}},\n    month = {April},\n    year = {2026}\n}\n
\n", "date_published": "2026-04-22T02:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen3.6-27b", "id": "urn:aggr:item:b903e8c984c5a892e87550badb859726465651f4", "image": "https://img.alicdn.com/imgextra/i2/O1CN01pnefgf1o0FcxKA3uu_!!6000000005162-2-tps-1590-954.png", "summary": "QWEN STUDIO HUGGING FACE MODELSCOPE DISCORD Following the launch of Qwen3.6-Plus and Qwen3.6-35B-A3B, we are excited to open-source Qwen3.6-27B — a dense 27-billion-parameter multimodal model at the scale the community has been asking for…", "tags": [ "ai", "open-source" ], "title": "Qwen3.6-27B: Flagship-Level Coding in a 27B Dense Model", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-04-22-qwen3-6-27b-flagship-level-coding-in-a-27b-dense-model/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"Qwen3.6-Max-Preview

\n

QWEN STUDIO DISCORD

\n

Following the release of Qwen3.6-Plus, we are sharing an early preview of our next proprietary model: Qwen3.6-Max-Preview. Compared to Qwen3.6-Plus, this preview release brings stronger world knowledge and instruction following, along with significant agentic coding improvements across a wide range of benchmarks. As a preview, the model is still under active development — we are continuing to iterate and expect further gains in subsequent versions.

\n\n

Performance

\n

Below we present evaluations of Qwen3.6-Max-Preview against leading frontier models. Compared to Qwen3.6-Plus, the preview release delivers significant improvements in agentic coding (e.g., SkillsBench +9.9, SciCode +6.3, NL2Repo +5.0, Terminal-Bench 2.0 +3.8), stronger world knowledge (SuperGPQA +2.3, QwenChineseBench +5.3), and better instruction following (ToolcallFormatIFBench +2.8).

\n

\"\"

\n

Build with Qwen3.6-Max-Preview

\n

Qwen3.6-Max-Preview is coming soon to Alibaba Cloud Model Studio. Please stand by until we are fully ready.

\n

Qwen3.6-Max-Preview is available through the Alibaba Cloud Model Studio API as qwen3.6-max-preview. You can also try it instantly on Qwen Studio.

\n

API Usage

\n

This release supports the preserve_thinking feature: preserving thinking content from all preceding turns in messages, which is recommended for agentic tasks.

\n

Alibaba Cloud Model Studio

\n

Alibaba Cloud Model Studio supports industry-standard protocols, including chat completions and responses APIs compatible with OpenAI’s specification, as well as an API interface compatible with Anthropic.

\n

Example code for chat completions API is provided below:

\n
"""\nEnvironment variables (per official docs):\n  DASHSCOPE_API_KEY: Your API Key from https://modelstudio.console.alibabacloud.com\n  DASHSCOPE_BASE_URL: (optional) Base URL for compatible-mode API.\n    - Beijing: https://dashscope.aliyuncs.com/compatible-mode/v1\n    - Singapore: https://dashscope-intl.aliyuncs.com/compatible-mode/v1\n    - US (Virginia): https://dashscope-us.aliyuncs.com/compatible-mode/v1\n  DASHSCOPE_MODEL: (optional) Model name; override for different models.\n"""\nfrom openai import OpenAI\nimport os\n\napi_key = os.environ.get("DASHSCOPE_API_KEY")\nif not api_key:\n    raise ValueError(\n        "DASHSCOPE_API_KEY is required. "\n        "Set it via: export DASHSCOPE_API_KEY='your-api-key'"\n    )\n\nclient = OpenAI(\n    api_key=api_key,\n    base_url=os.environ.get(\n        "DASHSCOPE_BASE_URL",\n        "https://dashscope-intl.aliyuncs.com/compatible-mode/v1",\n    ),\n)\n\nmessages = [{"role": "user", "content": "Introduce vibe coding."}]\n\nmodel = os.environ.get(\n    "DASHSCOPE_MODEL",\n    "qwen3.6-max-preview",\n)\ncompletion = client.chat.completions.create(\n    model=model,\n    messages=messages,\n    extra_body={\n        "enable_thinking": True,\n        # "preserve_thinking": True,\n    },\n    stream=True\n)\n\nreasoning_content = ""  # Full reasoning trace\nanswer_content = ""  # Full response\nis_answering = False  # Whether we have entered the answer phase\nprint("\\n" + "=" * 20 + "Reasoning" + "=" * 20 + "\\n")\n\nfor chunk in completion:\n    if not chunk.choices:\n        print("\\nUsage:")\n        print(chunk.usage)\n        continue\n\n    delta = chunk.choices[0].delta\n\n    # Collect reasoning content only\n    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:\n        if not is_answering:\n            print(delta.reasoning_content, end="", flush=True)\n        reasoning_content += delta.reasoning_content\n\n    # Received content, start answer phase\n    if hasattr(delta, "content") and delta.content:\n        if not is_answering:\n            print("\\n" + "=" * 20 + "Answer" + "=" * 20 + "\\n")\n            is_answering = True\n        print(delta.content, end="", flush=True)\n        answer_content += delta.content\n
\n

For more information, please visit the API doc.

\n

Summary

\n

Qwen3.6-Max-Preview is an early preview of our next proprietary model, delivering meaningful improvements over Qwen3.6-Plus in agentic coding, world knowledge, and instruction following. It achieves the top score on six major coding benchmarks — SWE-bench Pro, Terminal-Bench 2.0, SkillsBench, QwenClawBench, QwenWebBench, and SciCode — with substantial gains over its predecessor. It also demonstrates stronger knowledge (SuperGPQA, QwenChineseBench) and better instruction following (ToolcallFormatIFBench).

\n

As a preview release, Qwen3.6-Max-Preview is still under active development. We are continuing to iterate on the model and expect further improvements in subsequent versions. We welcome community feedback and look forward to seeing what you build. Stay tuned!

\n

Citation

\n

Feel free to cite the following article if you find Qwen3.6-Max-Preview helpful:

\n
@misc{qwen36_max_preview,\n    title = {{Qwen3.6-Max-Preview}: Smarter, Sharper, Still Evolving},\n    url = {https://qwen.ai/blog?id=qwen3.6-max-preview},\n    author = {{Qwen Team}},\n    month = {April},\n    year = {2026}\n}\n
\n", "date_published": "2026-04-18T02:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen3.6-max-preview", "id": "urn:aggr:item:f75d7f9d65bbcd25865ac563cc8962c62da10e30", "image": "https://img.alicdn.com/imgextra/i4/O1CN01G7Tcjx1TKJ2SRtYll_!!6000000002363-2-tps-1590-954.png", "summary": "QWEN STUDIO DISCORD Following the release of Qwen3.6-Plus, we are sharing an early preview of our next proprietary model: Qwen3.6-Max-Preview. Compared to Qwen3.6-Plus, this preview release brings stronger world knowledge and instruction…", "tags": [ "ai", "release" ], "title": "Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolving", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-04-18-qwen3-6-max-preview-smarter-sharper-still-evolving/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"Qwen3.6-35B-A3B

\n

QWEN STUDIO HUGGING FACE MODELSCOPE DISCORD

\n

Following the launch of Qwen3.6-Plus, we are excited to open-source Qwen3.6-35B-A3B — a sparse yet remarkably capable mixture-of-experts (MoE) model with 35 billion total parameters and only 3 billion active parameters. Despite its efficiency, Qwen3.6-35B-A3B delivers outstanding agentic coding performance, surpassing its predecessor Qwen3.5-35B-A3B by a wide margin and rivaling much larger dense models such as Qwen3.5-27B and Gemma4-31B. Still supporting both multimodal thinking and non-thinking modes, Qwen3.6-35B-A3B works as one of the most versatile open-source models available today. Now, Qwen3.6-35B-A3B is live on Qwen Studio, available through our API, and released as open weights for the community.

\n\n

\"\"

\n

Performance

\n

Below we present comprehensive evaluations of Qwen3.6-35B-A3B against peer-scale models across a wide range of tasks and modalities.

\n

Language

\n

With only 3B active parameters, Qwen3.6-35B-A3B outperforms the dense 27B-parameter Qwen3.5-27B on several key coding benchmarks and dramatically surpasses its direct predecessor Qwen3.5-35B-A3B, especially on agentic coding and reasoning tasks.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Qwen3.5-27BGemma4-31BQwen3.5-35BA3BGemma4-26BA4BQwen3.6-35BA3B
Coding Agent
SWE-bench Verified75.052.070.017.473.4
SWE-bench Multilingual69.351.760.317.367.2
SWE-bench Pro51.235.744.613.849.5
Terminal-Bench 2.041.642.940.534.251.5
Claw-Eval Avg64.348.565.458.868.7
Claw-Eval Pass^346.225.051.028.050.0
SkillsBench Avg527.223.64.412.328.7
QwenClawBench52.241.747.738.752.6
NL2Repo27.315.520.511.629.4
QwenWebBench1068119797811781397
General Agent
TAU3-Bench68.467.568.959.067.2
VITA-Bench41.843.029.136.935.6
DeepPlanning22.624.022.816.225.9
Tool Decathlon31.521.228.712.026.9
MCPMark36.318.127.014.237.0
MCP-Atlas68.457.262.450.062.8
WideSearch66.435.259.138.360.1
Knowledge
MMLU-Pro86.185.285.382.685.2
MMLU-Redux93.293.793.392.793.3
SuperGPQA65.665.763.461.464.7
C-Eval90.582.690.282.590.0
STEM & Reasoning
GPQA85.584.384.282.386.0
HLE24.319.522.48.721.4
LiveCodeBench v680.780.074.677.180.4
HMMT Feb 2592.088.789.091.790.7
HMMT Nov 2589.887.589.287.589.1
HMMT Feb 2684.377.278.779.083.6
IMOAnswerBench79.974.576.874.378.9
AIME2692.689.291.088.392.7
\n

* SWE-Bench Series: Internal agent scaffold (bash + file-edit tools); temp=1.0, top_p=0.95, 200K context window. We correct some problematic tasks in the public set of SWE-bench Pro and evaluate all baselines on the refined benchmark.
\n* Terminal-Bench 2.0: Harbor/Terminus-2 harness; 3h timeout, 32 CPU/48 GB RAM; temp=1.0, top_p=0.95, top_k=20, max_tokens=80K, 256K ctx; avg of 5 runs.
\n* SkillsBench: Evaluated via OpenCode on 78 tasks (self-contained subset, excluding API-dependent tasks); avg of 5 runs.
\n* NL2Repo: Others are evaluated via Claude Code (temp=1.0, top_p=0.95, max_turns=900).
\n* QwenClawBench: An internal real-user-distribution Claw agent benchmark (open-sourcing soon); temp=0.6, 256K ctx.
\n* QwenWebBench: An internal front-end code generation benchmark; bilingual (EN/CN), 7 categories (Web Design, Web Apps, Games, SVG, Data Visualization, Animation, and 3D); auto-render + multimodal judge (code/visual correctness); BT/Elo rating system.
\n* TAU3-Bench: We use the official user model (gpt-5.2, low reasoning effort) + default BM25 retrieval.
\n* VITA-Bench: Avg subdomain scores; using claude-4-sonnet as judger, as the official judger (claude-3.7-sonnet) is no longer available.
\n* MCPMark: GitHub MCP v0.30.3; Playwright responses truncated at 32K tokens.
\n* MCP-Atlas: Public set score; gemini-2.5-pro judger.
\n* AIME 26: We use the full AIME 2026 (I & II), where the scores may differ from Qwen 3.5 notes.

\n

Vision Language

\n

Qwen3.6 is natively multimodal, and Qwen3.6-35B-A3B showcases perception and multimodal reasoning capabilities that far exceed what its size would suggest, with only around 3 billion activated parameters. Across most vision-language benchmarks, its performance matches Claude Sonnet 4.5, and even surpasses it on several tasks. Its strengths are particularly evident in spatial intelligence, where it achieves 92.0 on RefCOCO and 50.8 on ODInW13.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Qwen3.5-27BClaude-Sonnet-4.5Gemma4-31BGemma4-26BA4BQwen3.5-35B-A3BQwen3.6-35B-A3B
STEM and Puzzle
MMMU82.379.680.478.481.481.7
MMMU-Pro75.068.476.9*73.8*75.175.3
Mathvista(mini)87.879.879.379.486.286.4
ZEROBench_sub36.226.326.026.334.134.4
General VQA
RealWorldQA83.770.372.372.284.185.3
MMBenchEN-DEV-v1.192.688.390.989.091.592.8
SimpleVQA56.057.652.952.258.358.9
HallusionBench70.059.967.466.167.969.8
Text Recognition and Document Understanding
OmniDocBench1.588.985.880.174.489.389.9
CharXiv(RQ)79.567.267.969.077.578.0
CC-OCR81.068.175.774.580.781.9
AI2D_TEST92.987.089.088.392.692.7
Spatial Intelligence
RefCOCO(avg)90.9------89.292.0
ODInW1341.1------42.650.8
EmbSpatialBench84.571.8----83.184.3
RefSpatialBench67.7------63.564.3
Video Understanding
VideoMME(w sub.)87.081.1----86.686.6
VideoMME(w/o sub.)82.875.3----82.582.5
VideoMMMU82.377.681.676.080.483.7
MLVU85.972.8----85.686.2
MVBench74.6------74.874.6
LVBench73.6------71.471.4
\n

* Empty cells (--) indicate scores not available or not applicable.

\n

Build with Qwen3.6-35B-A3B

\n

Qwen3.6-35B-A3B is available as open weights on Hugging Face and ModelScope for self-hosting, and through the Alibaba Cloud Model Studio API as qwen3.6-flash. You can also try it instantly on Qwen Studio.

\n

The model can be seamlessly integrated with popular third-party coding assistants, including OpenClaw, Claude Code, and Qwen Code, to streamline development workflows and enable efficient, context-aware coding experiences.

\n

API Usage

\n

This release supports the preserve_thinking feature: preserving thinking content from all preceding turns in messages, which is recommended for agentic tasks.

\n

Alibaba Cloud Model Studio

\n

Alibaba Cloud Model Studio supports industry-standard protocols, including chat completions and responses APIs compatible with OpenAI’s specification, as well as an API interface compatible with Anthropic.

\n

Example code for chat completions API is provided below:

\n
"""\nEnvironment variables (per official docs):\n  DASHSCOPE_API_KEY: Your API Key from https://modelstudio.console.alibabacloud.com\n  DASHSCOPE_BASE_URL: (optional) Base URL for compatible-mode API.\n    - Beijing: https://dashscope.aliyuncs.com/compatible-mode/v1\n    - Singapore: https://dashscope-intl.aliyuncs.com/compatible-mode/v1\n    - US (Virginia): https://dashscope-us.aliyuncs.com/compatible-mode/v1\n  DASHSCOPE_MODEL: (optional) Model name; override for different models.\n"""\nfrom openai import OpenAI\nimport os\n\napi_key = os.environ.get("DASHSCOPE_API_KEY")\nif not api_key:\n    raise ValueError(\n        "DASHSCOPE_API_KEY is required. "\n        "Set it via: export DASHSCOPE_API_KEY='your-api-key'"\n    )\n\nclient = OpenAI(\n    api_key=api_key,\n    base_url=os.environ.get(\n        "DASHSCOPE_BASE_URL",\n        "https://dashscope-intl.aliyuncs.com/compatible-mode/v1",\n    ),\n)\n\nmessages = [{"role": "user", "content": "Introduce vibe coding."}]\n\nmodel = os.environ.get(\n    "DASHSCOPE_MODEL",\n    "qwen3.6-flash",\n)\ncompletion = client.chat.completions.create(\n    model=model,\n    messages=messages,\n    extra_body={\n        "enable_thinking": True,\n        # "preserve_thinking": True,\n    },\n    stream=True\n)\n\nreasoning_content = ""  # Full reasoning trace\nanswer_content = ""  # Full response\nis_answering = False  # Whether we have entered the answer phase\nprint("\\n" + "=" * 20 + "Reasoning" + "=" * 20 + "\\n")\n\nfor chunk in completion:\n    if not chunk.choices:\n        print("\\nUsage:")\n        print(chunk.usage)\n        continue\n\n    delta = chunk.choices[0].delta\n\n    # Collect reasoning content only\n    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:\n        if not is_answering:\n            print(delta.reasoning_content, end="", flush=True)\n        reasoning_content += delta.reasoning_content\n\n    # Received content, start answer phase\n    if hasattr(delta, "content") and delta.content:\n        if not is_answering:\n            print("\\n" + "=" * 20 + "Answer" + "=" * 20 + "\\n")\n            is_answering = True\n        print(delta.content, end="", flush=True)\n        answer_content += delta.content\n
\n

For more information, please visit the API doc.

\n

Coding & Agents

\n

Qwen3.6-35B-A3B features excellent agentic coding capabilities and can be seamlessly integrated into popular third-party coding assistants, including OpenClaw, Claude Code, and Qwen Code.

\n

OpenClaw

\n

Qwen3.6-35B-A3B is compatible with OpenClaw (formerly Moltbot / Clawdbot), a self-hosted open-source AI coding agent. Connect it to Model Studio to get a full agentic coding experience in the terminal. Get started with the following script:

\n
# Node.js 22+\ncurl -fsSL https://molt.bot/install.sh | bash   # macOS / Linux\n\n# Set your API key\nexport DASHSCOPE_API_KEY=<your_api_key>\n\n# Launch OpenClaw\nopenclaw dashboard # web browser\n# openclaw tui # Open a new terminal and start the TUI\n
\n

On first use, edit ~/.openclaw/openclaw.json to point OpenClaw at Model Studio. Find or create the following fields and merge them — do not overwrite the entire file to preserve your existing settings:

\n
{\n  "models": {\n    "mode": "merge",\n    "providers": {\n      "modelstudio": {\n        "baseUrl": "https://dashscope-intl.aliyuncs.com/compatible-mode/v1",\n        "apiKey": "DASHSCOPE_API_KEY",\n        "api": "openai-completions",\n        "models": [\n          {\n            "id": "qwen3.6-flash",\n            "name": "qwen3.6-flash",\n            "reasoning": true,\n            "input": ["text", "image"],\n            "contextWindow": 131072,\n            "maxTokens": 16384\n          }\n        ]\n      }\n    }\n  },\n  "agents": {\n    "defaults": {\n      "model": {\n        "primary": "modelstudio/qwen3.6-flash"\n      },\n      "models": {\n        "modelstudio/qwen3.6-flash": {}\n      }\n    }\n  }\n}\n
\n

Qwen Code

\n

Qwen3.6-35B-A3B is compatible with Qwen Code, an open-source AI agent designed for the terminal and deeply optimized for the Qwen Series. Get started with the following script:

\n
# Node.js 20+\nnpm install -g @qwen-code/qwen-code@latest\n\n# Start Qwen Code (interactive)\nqwen\n\n# Then, in the session:\n/help\n/auth\n
\n

On first use, you’ll be prompted to sign in. You can run /auth anytime to switch authentication methods.

\n

Claude Code

\n

Qwen APIs also support the Anthropic API protocol, meaning you can use it with tools like Claude Code for elevated coding experience:

\n
# Install Claude Code\nnpm install -g @anthropic-ai/claude-code\n\n# Configure environment\nexport ANTHROPIC_MODEL="qwen3.6-flash"\nexport ANTHROPIC_SMALL_FAST_MODEL="qwen3.6-flash"\nexport ANTHROPIC_BASE_URL=https://dashscope-intl.aliyuncs.com/apps/anthropic\nexport ANTHROPIC_AUTH_TOKEN=<your_api_key>\n\n# Launch the CLI\nclaude\n
\n

Summary

\n

Qwen3.6-35B-A3B demonstrates that sparse MoE models can achieve remarkable agentic coding and reasoning capability. With only 3B active parameters, it delivers performance that rivals dense models several times its active size, while also excelling across multimodal benchmarks. As a fully open-source checkpoint, it sets a new standard for what’s possible at its scale.

\n

Looking ahead, we will continue to expand the Qwen3.6 open-source family and push the boundaries of what efficient, open models can accomplish. We are grateful for the community’s feedback and look forward to seeing what you build with Qwen3.6-35B-A3B. Also, Qwen3.6 open-source family keeps expanding, stay tuned for our future releases!

\n

Citation

\n

Feel free to cite the following article if you find Qwen3.6-35B-A3B helpful:

\n
@misc{qwen36_35b_a3b,\n    title = {{Qwen3.6-35B-A3B}: Agentic Coding Power, Now Open to All},\n    url = {https://qwen.ai/blog?id=qwen3.6-35b-a3b},\n    author = {{Qwen Team}},\n    month = {April},\n    year = {2026}\n}\n
\n", "date_published": "2026-04-15T02:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen3.6-35b-a3b", "id": "urn:aggr:item:5f0a510db03908b6d1c8875c8d9a95054a6d4e18", "image": "https://img.alicdn.com/imgextra/i3/O1CN01cL6tG71xn6PgeRuj6_!!6000000006487-2-tps-1590-954.png", "summary": "QWEN STUDIO HUGGING FACE MODELSCOPE DISCORD Following the launch of Qwen3.6-Plus, we are excited to open-source Qwen3.6-35B-A3B — a sparse yet remarkably capable mixture-of-experts (MoE) model with 35 billion total parameters and only 3…", "tags": [ "ai", "open-source" ], "title": "Qwen3.6-35B-A3B: Agentic Coding Power, Now Open to All", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-04-15-qwen3-6-35b-a3b-agentic-coding-power-now-open-to-all/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"Qwen3.6

\n

QWEN CHAT DISCORD

\n

Following the release of the Qwen3.5 series in February, we are thrilled to announce the official launch of Qwen3.6-Plus. Available immediately via our API, this release represents a massive capability upgrade over its predecessor. Most notably, we have drastically enhanced the model’s agentic coding capabilities. From frontend web development to complex, repository-level problem solving, Qwen3.6-Plus sets a new state-of-the-art standard. Furthermore, Qwen3.6-Plus perceives the world with greater accuracy and sharper multimodal reasoning. By directly addressing community feedback from the Qwen3.5-Plus deployment, this release offers a highly stable and reliable foundation for the developer ecosystem, delivering a truly transformative “vibe coding” experience.

\n\n

\"\"

\n

Performance

\n

Below we present the comprehensive evaluation of our models against frontier models in a wide range of evaluation tasks, covering different tasks and modalities.

\n

Language

\n

Qwen3.6-Plus achieves comprehensive improvements in coding agents, general agents, and tool usage by deeply integrating reasoning, memory, and execution capabilities.

\n

In the field of coding agents, Qwen3.6-Plus demonstrates strong practical engineering performance. It not only closely matches industry leaders on mainstream code repair benchmarks but also excels in complex terminal operations and automated task execution.

\n

For general-purpose agents and tool usage, the model makes significant breakthroughs. It achieves top results in multiple challenging long-horizon planning tasks and leads across various tool-calling benchmarks.

\n

Regarding general capabilities, Qwen3.6-Plus maintains leading performance: it sets new records in key evaluations spanning difficult STEM reasoning, precise information extraction from ultra-long contexts, and broad adaptation to multilingual environments.

\n

We believe Qwen3.6-Plus’s advancement lies not only in surpassing metrics across the board but also in its organic integration of deep logical reasoning, extensive contextual memory, and precise tool execution. This “all-rounder” characteristic enables it to confidently handle real-world challenges—from complex code management to cross-domain long-term planning—marking the Qwen series’ accelerated evolution toward highly autonomous super-agents.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Claude Opus 4.5Kimi-K2.5GLM5Qwen3.5-397B-A17BQwen3.6-Plus
Coding Agent
SWE-bench Verified80.976.877.876.278.8
SWE-bench Multilingual77.573.073.369.373.8
SWE-bench Pro57.153.855.150.956.6
Terminal-Bench 2.059.350.856.252.561.6
Claw-Eval Avg76.671.673.070.774.8
Claw-Eval Pass^359.652.957.748.158.7
SkillsBench Avg545.342.047.230.045.7
QwenClawBench52.354.354.151.857.2
NL2Repo43.232.035.932.237.9
QwenWebBench1517.91159.51315.11162.31501.7
General Agent
TAU3-Bench70.265.765.668.470.7
VITA-Bench50.336.037.043.744.3
DeepPlanning33.914.414.637.641.5
Tool Decathlon43.527.838.038.339.8
MCPMark42.329.531.146.148.2
MCP-Atlas71.859.869.874.274.1
HLE w/ tool43.450.250.448.350.6
WideSearch76.472.769.574.074.3
Knowledge
MMLU-Pro89.587.185.787.888.5
MMLU-Redux95.694.594.494.994.5
SuperGPQA70.669.266.870.471.6
C-Eval92.294.092.893.093.3
Instruction Following
IFEvalstrict prompt90.993.992.692.694.3
IFBench58.070.272.376.574.2
Long Context
AA-LCR74.070.063.368.768.3
LongBench v264.461.060.863.262.0
STEM & Reasoning
GPQA87.087.686.088.490.4
HLE30.830.127.228.728.8
LiveCodeBench v684.885.085.583.687.1
HMMT Feb 2592.995.497.594.896.7
HMMT Nov 2593.391.196.992.794.6
HMMT Feb 2685.387.186.487.987.8
IMOAnswerBench84.081.882.580.983.8
AIME2695.195.895.893.395.3
Multilingualism
MMMLU90.186.086.688.589.5
MMLU-ProX85.782.383.184.784.7
NOVA-6356.756.055.159.157.9
INCLUDE86.283.384.985.685.1
Global PIQA91.689.389.489.889.8
PolyMATH79.043.165.273.377.4
WMT24++79.777.682.178.984.3
MAXIFE79.272.885.688.288.2
\n

* SWE-Bench Series: Internal agent scaffold (bash + file-edit tools); temp=1.0, top_p=0.95, 200K context window. We correct some problematic tasks in the public set of SWE-bench Pro and evaluate all baselines on the refined benchmark.
\n* Terminal-Bench 2.0: Harbor/Terminus-2 harness; 3h timeout, 32 CPU/48 GB RAM; temp=1.0, top_p=0.95, top_k=20, max_tokens=80K, 256K ctx; avg of 5 runs.
\n* Claw-Eval: Temp=0.6, 256K ctx.
\n* SkillsBench: Claude Opus 4.5 from official leaderboard (87 tasks); others are evaluated via OpenCode on 78 tasks (self-contained subset, excluding API-dependent tasks); avg of 5 runs.
\n* NL2Repo: Claude Opus 4.5 from official leaderboard; others are evaluated via Claude Code (temp=1.0, top_p=0.95, max_turns=900).
\n* QwenClawBench: an internal real-user-distribution Claw agent benchmark (open-sourcing soon); temp=0.6, 256K ctx.
\n* QwenWebBench: an internal front-end code generation benchmark; bilingual (EN/CN), 7 categories (Web Design, Web Apps, Games, SVG, Data Visualization, Animation, and 3D); auto-render + multimodal judge (code/visual correctness); BT/Elo rating system.
\n* TAU3-Bench: We use the official user model (gpt-5.2, low reasoning effort) + default BM25 retrieval.
\n* VITA-Bench: Avg subdomain scores; using claude-4-sonnet as judger, as the official judger (claude-3.7-sonnet) is no longer available.
\n* MCPMark: GitHub MCP v0.30.3; Playwright responses truncated at 32K tokens.
\n* MCP-Atlas: Public set score; gemini-2.5-pro judger.
\n* HLE w/ tool: 256K ctx w/ context-folding; prunes older tool responses upon threshold breach.
\n* WideSearch: 256K ctx w/ management; prunes ≥49,152 tool tokens when >208,896 used.
\n* AIME 26: We use the full AIME 2026 (I & II), where the scores may differ from Qwen 3.5 notes.
\n* MMLU-ProX: Avg accuracy across 29 languages.
\n* WMT24++: a harder WMT24 subset; avg scores on 55 langs via XCOMET-XXL.
\n* MAXIFE: Accuracy on EN + multilingual prompts (23 settings total).

\n

Vision Language

\n

Qwen3.6-Plus marks a steady progress in multimodal capabilities, evolving across three core dimensions: advanced reasoning, enhanced applicability, and ability to execute complex tasks.

\n

Advanced Multimodal Reasoning: Qwen3.6-Plus delivers substantial breakthroughs in complex document understanding, physical world visual analysis, video reasoning, and visual coding. The model now excels at integrating cross-modal information to perform sophisticated analysis and decision-making.

\n

Real-World Applicability: Optimized for genuine business scenarios, Qwen3.6-Plus demonstrates superior stability and usability. It handles demanding tasks ranging from instruction following, challenging text and general object recognition, to fine-grained visual perception, proving effective in practical applications like retail intelligence.

\n

We believe the future of multimodal AI lies not just in isolated task performance, but in providing holistic support for workflow-oriented operations. As its capabilities in understanding, reasoning, and action continue to converge, Qwen3.6-Plus is evolving into a native multimodal agent, capable of continuously perceiving, reasoning, and acting within real-world environments.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
GPT5.2Claude 4.5 OpusGemini-3 ProKimi-K2.5Qwen3.5-397B-A17BQwen3.6-Plus
STEM and Puzzle
MMMU86.780.787.284.385.086.0
MMMU-Pro79.570.681.078.579.078.8
MathVision83.074.386.684.288.688.0
We-Math79.070.086.984.787.989.0
DynaMath86.879.785.184.486.388.0
General VQA
RealWorldQA83.377.083.381.083.985.4
MMStar77.173.283.180.583.883.3
SimpleVQA55.865.773.271.267.167.3
Text Recognition and Document Understanding
OmniDocBench1.585.787.788.588.890.891.2
CharXiv(RQ)82.168.581.477.580.881.5
MMLongBench-Doc--61.960.558.561.562.0
CC-OCR70.376.979.079.782.083.4
AI2D_TEST92.287.794.190.893.994.4
Spatial Intelligence
CountBench91.990.697.394.197.297.6
RefCOCO(avg)----84.187.892.393.5
ODinW13----46.3--47.051.8
ERQA59.846.870.5--67.565.7
V*75.967.088.077.095.8 / 91.196.9 / 90.5
Video Understanding
VideoMME(w sub.)86.077.688.487.487.587.8
VideoMME(w/o sub.)85.881.487.783.283.784.2
VideoMMMU85.984.487.686.684.784.0
MLVU (M-Avg)85.681.783.085.086.786.7
Visual Agent
ScreenSpot Pro--45.772.7--65.668.2
TIR-Bench--32.347.529.262.5 / 42.361.6 / 43.7
OSWorld-Verified38.266.3--63.362.262.5
\n

* MathVision: Our model’s score is evaluated using a fixed prompt, e.g., “Please reason step by step, and put your final answer within \\boxed{}.” For other models, we report the higher score between runs with and without the \\boxed{} formatting.
\n* V* and TIR-Bench: Scores reported as "with CI / without CI".
\n* Empty cells (--) indicate scores not yet available or not applicable.

\n

Build with Qwen3.6-Plus

\n

Qwen3.6-Plus is now generally available through our official API via Alibaba Cloud Model Studio. You can seamlessly integrate the API with popular third-party coding assistants, including OpenClaw, Claude Code, Qwen Code, Kilo Code, Cline, and OpenCode, to streamline development workflows and enable efficient, context-aware coding experiences.

\n

API Usage

\n

This release introduces a new feature to the API designed to improve performance on complex, multistep tasks:

\n\n

Alibaba Cloud Model Studio

\n

Alibaba Cloud Model Studio supports industry-standard protocols, including chat completions and responses APIs compatible with OpenAI’s specification, as well as an API interface compatible with Anthropic.

\n

Example code for chat completions API is provided below:

\n
"""\nEnvironment variables (per official docs):\n  DASHSCOPE_API_KEY: Your API Key from https://modelstudio.console.alibabacloud.com\n  DASHSCOPE_BASE_URL: (optional) Base URL for compatible-mode API.\n    - Beijing: https://dashscope.aliyuncs.com/compatible-mode/v1\n    - Singapore: https://dashscope-intl.aliyuncs.com/compatible-mode/v1\n    - US (Virginia): https://dashscope-us.aliyuncs.com/compatible-mode/v1\n  DASHSCOPE_MODEL: (optional) Model name; override for different models.\n"""\nfrom openai import OpenAI\nimport os\n\napi_key = os.environ.get("DASHSCOPE_API_KEY")\nif not api_key:\n    raise ValueError(\n        "DASHSCOPE_API_KEY is required. "\n        "Set it via: export DASHSCOPE_API_KEY='your-api-key'"\n    )\n\nclient = OpenAI(\n    api_key=api_key,\n    base_url=os.environ.get(\n        "DASHSCOPE_BASE_URL",\n        "https://dashscope-intl.aliyuncs.com/compatible-mode/v1",\n    ),\n)\n\nmessages = [{"role": "user", "content": "Introduce vibe coding."}]\n\nmodel = os.environ.get(\n    "DASHSCOPE_MODEL",\n    "qwen3.6-plus",\n)\ncompletion = client.chat.completions.create(\n    model=model,\n    messages=messages,\n    extra_body={\n        "enable_thinking": True,\n        # "preserve_thinking": True,\n    },\n    stream=True\n)\n\nreasoning_content = ""  # Full reasoning trace\nanswer_content = ""  # Full response\nis_answering = False  # Whether we have entered the answer phase\nprint("\\n" + "=" * 20 + "Reasoning" + "=" * 20 + "\\n")\n\nfor chunk in completion:\n    if not chunk.choices:\n        print("\\nUsage:")\n        print(chunk.usage)\n        continue\n\n    delta = chunk.choices[0].delta\n\n    # Collect reasoning content only\n    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:\n        if not is_answering:\n            print(delta.reasoning_content, end="", flush=True)\n        reasoning_content += delta.reasoning_content\n\n    # Received content, start answer phase\n    if hasattr(delta, "content") and delta.content:\n        if not is_answering:\n            print("\\n" + "=" * 20 + "Answer" + "=" * 20 + "\\n")\n            is_answering = True\n        print(delta.content, end="", flush=True)\n        answer_content += delta.content\n
\n

For more information, please visit the API doc.

\n

Coding & Agents

\n

Qwen3.6-Plus features excellent frontend development capabilities and can be seamlessly integrated into popular third-party coding assistants, including OpenClaw, Claude Code, and Qwen Code, to streamline development workflows.

\n

Web Dev

\n

Qwen3.6-Plus enhances frontend development capabilities, delivering superior performance on complex projects like 3D scenes and games, while maintaining excellence in web page design.

\n

3D Aquarium Next

\n

User

\n

写一个模拟鱼群的3D动效网页，场景是桌子上有个鱼缸，浴缸里生长着一些水草，鱼缸里有十条鱼组成一个鱼群，每条鱼都遵循Boids Plus规则，水草会随着鱼群游动带动的水流而摆动。 输出单个html文件。

\n

Qwen3.6-Plus

\n

Designer Personal Site Next

\n

User

\n

Design a personal website for a designer who received an Awwwards nomination, featuring large areas of white space, oversized serif font titles, a custom cursor that follows the mouse, perspective-shifting images in the portfolio area when the mouse hovers over them, parallax effects on text as the page scrolls, and a color scheme of only black and white with a bright orange accent.

\n

Qwen3.6-Plus

\n

Music Game Next

\n

User

\n

实现一个《节奏光剑》风格 2D 音游前端（单文件 HTML/Canvas/Audio API、无依赖）。支持：导入/内置谱面、判定线、Perfect/Good/Miss、连击与分数、延迟校准、开始倒计时、暂停/继续、结算面板与回放（至少记录按键时间序列）。

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Qwen3.6-Plus

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Snowy Mountain Next

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User

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制作一个3D的雪山场景，雪山中间有一个日式的寺庙，整体风格参考塞尔达旷野之息

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Qwen3.6-Plus

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OpenClaw

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Qwen3.6-Plus is compatible with OpenClaw (formerly Moltbot / Clawdbot), a self-hosted open-source AI coding agent. Connect it to Model Studio to get a full agentic coding experience in the terminal. Get started with the following script:

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# Node.js 22+\ncurl -fsSL https://molt.bot/install.sh | bash   # macOS / Linux\n\n# Set your API key\nexport DASHSCOPE_API_KEY=<your_api_key>\n\n# Launch OpenClaw\nopenclaw dashboard # web browser\n# openclaw tui # Open a new terminal and start the TUI\n
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On first use, edit ~/.openclaw/openclaw.json to point OpenClaw at Model Studio. Find or create the following fields and merge them — do not overwrite the entire file to preserve your existing settings:

\n
{\n  "models": {\n    "mode": "merge",\n    "providers": {\n      "modelstudio": {\n        "baseUrl": "https://dashscope-intl.aliyuncs.com/compatible-mode/v1",\n        "apiKey": "DASHSCOPE_API_KEY",\n        "api": "openai-completions",\n        "models": [\n          {\n            "id": "qwen3.6-plus",\n            "name": "qwen3.6-plus",\n            "reasoning": true,\n            "input": ["text", "image"],\n            "contextWindow": 1000000,\n            "maxTokens": 65536\n          }\n        ]\n      }\n    }\n  },\n  "agents": {\n    "defaults": {\n      "model": {\n        "primary": "modelstudio/qwen3.6-plus"\n      },\n      "models": {\n        "modelstudio/qwen3.6-plus": {}\n      }\n    }\n  }\n}\n
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Personal Schedule Management Next

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User

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It’s 8:00 AM on June 15th and I need a schedule I can actually trust for today’s thesis submission push. My planning files do not agree with each other: some are old, some are informal notes, and my advisor added a few things this morning. I need one reliable plan plus a structured handoff I can use to track execution during the day.

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Please:

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    \n
  1. Review the thesis-planning files in the workspace and reconcile them. For each task, determine the real current status (done, in-progress, or not-started) using the most authoritative and up-to-date sources. If files conflict, say which source wins and why.
    2. \n
  2. Identify every task that still must happen today, including anything newly introduced in the advisor materials even if it is missing from the main tracker.
    3. \n
  3. Validate the priority matrix instead of trusting its quadrant labels blindly. If a quadrant label disagrees with the urgency/importance scores, correct it and use the corrected priority in the plan.
    4. \n
  4. Build a feasible time-blocked schedule for 08:00-15:00 that respects dependencies, meets every hard deadline, and is grouped into clear phases with key deliverables.
    5. \n
  5. Confirm the real deadlines from the most authoritative source and explicitly reject stale ones.
    6. \n
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Deliverables:

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Aesthetic principles:

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Qwen3.6-Plus

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Financial Statement Analysis Next

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User

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Can you pull together a full performance summary for our 2026 new issuance book? I want to know which deals were profitable and which lost money — and break down whether the P&L came from fees or from trading. Also compare it against how we did in prior years. Present the full analysis in reports/2026_pnl_analysis.html as a self-contained HTML document.

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Aesthetic principles:

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Qwen3.6-Plus

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OpenClaw Runtime Audit Next

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User

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The OpenClaw gateway has been accumulating session files for a while, and I’ve started seeing memory warnings in the logs. Before I do any cleanup or consider a restart, I want a proper health snapshot.

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First, create a reusable skill at workspace/skills/runtime-diagnostics/SKILL.md that documents a repeatable OpenClaw runtime health audit procedure. The skill should describe: which state files to read (and in what order), how to cross-validate PID and active-session-count across multiple sources, how to parse gateway.log for memory and session warnings, how to inventory the sessions/ directory by session type (the filename format is YYYYMMDD_TYPE_ID.jsonl), and how to compute a health score using this exact formula:

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health_score = 100 - (memory_warn_count * 1) - (state_inconsistency_count * 15) - (oversized_session_warn_count * 1)\n
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where memory_warn_count is the number of WARN memory lines in gateway.log, state_inconsistency_count is the total number of cross-file inconsistencies found, and oversized_session_warn_count is the number of WARN session: Session file growing lines in gateway.log.

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Then actually run the audit following that skill. Specifically, you must:

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    \n
  1. Read .openclaw/state/process.json, .openclaw/state/gateway.pid, and .openclaw/state/active-sessions.json, cross-validate the PID and active session count across these files, and flag any discrepancies.
    2. \n
  2. Parse .openclaw/logs/gateway.log (not gateway.log.1) to count WARN memory events and WARN session: Session file growing events.
    3. \n
  3. Count all .jsonl files in sessions/ and break down the count by session type extracted from the filename.
    4. \n
  4. Compute the health score using the formula above.
    5. \n
  5. Write the results to two files:\n\n
    6. \n
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Qwen3.6-Plus

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Qwen Code

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Qwen3.6-Plus is compatible with Qwen Code, an open-source AI agent designed for the terminal and deeply optimized for the Qwen Series. It helps you understand complex codebases, automate tedious work, and ship faster. Get started with the following script:

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# Node.js 20+\nnpm install -g @qwen-code/qwen-code@latest\n\n# Start Qwen Code (interactive)\nqwen\n\n# Then, in the session:\n/help\n/auth\n
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On first use, you’ll be prompted to sign in. You can run /auth anytime to switch authentication methods. Sign in with Qwen Code OAuth to instantly experience the latest Qwen3.6-Plus model—every user gets 1,000 free calls per day.

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Letter Flying Next

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User

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/skills brainstorming Find the complete content of Jobs’ ’think different,’ using a vertical paper background, text in typewriter font, arranged in full paragraphs, with the entire text located at the lower third of the page. The typewriter effect appears letter by letter. After the complete content appears, pause for 3 seconds, then the letter ‘o’ in the text moves upward and enlarges to the middle of the page, with the ‘o’ forming a line with its original position, pulling the entire text upward to float away and disappear.

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Qwen3.6-Plus

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Sticky Printer Next

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User

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/skills brainstorming 我想要做一个网页app，一个拟物的卡片打印机，我可以在输入框输入文字，点击print后，打印文字为便签的样式，输出后，可以在网页的board中拖动位置。

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Qwen3.6-Plus

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Claude Code

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Qwen APIs also support the Anthropic API protocol, meaning you can use it with tools like Claude Code for elevated coding experience:

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# Install Claude Code\nnpm install -g @anthropic-ai/claude-code\n\n# Configure environment\nexport ANTHROPIC_MODEL="qwen3.6-plus" \nexport ANTHROPIC_SMALL_FAST_MODEL="qwen3.6-plus"\nexport ANTHROPIC_BASE_URL=https://dashscope-intl.aliyuncs.com/apps/anthropic\nexport ANTHROPIC_AUTH_TOKEN=<your_api_key>\n\n# Launch the CLI\nclaude\n
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Flight Game Next

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User

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build a first-person perspective flight HTML game for me.

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Qwen3.6-Plus

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Fetch Images Next

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User

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Write a Python web crawler to scrape 100 images from the “Social Life” section of People’s Daily Online at https://vip.people.com.cn/ and save them.

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Qwen3.6-Plus

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Visual Agents

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Qwen3.6-Plus continues to advance along a clear capability trajectory in multimodality: from visual perception, to multimodal reasoning, to agentic execution. Our goal is not just for the model to “see” images and videos, but to equip it with a full capability loop spanning perception, understanding, reasoning, and task execution—moving step by step toward more practical native multimodal agents.

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Visual Reasoning

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Built on continuously improving perception capabilities, Qwen3.6-Plus further enhances its ability to understand, analyze, and reason over various visual inputs. Rather than stopping at shallow recognition of visual content, the model can combine reasoning, grounding, and OCR capabilities to perform deeper analysis of complex visual inputs, supporting practical tasks such as document understanding, chart parsing, UI understanding, and fine-grained localization. In other words, the model can go beyond answering “what is in the image” to also infer “how the information is related” and “how to act on it to complete a task.”

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Visual Reasoning with Grounding Next

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User

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Analyze the two images and take the second as reference. Your task is to locate the exact person in the first image. You should first locate every person and try to match the target. Report your results in JSON format like this:

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[\n\t{"bbox_2d": [x1, y1, x2, y2], "label": "the target person"}\n]\n
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\"image\" \"image\"

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Qwen3.6-Plus

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<think> The user wants me to find a specific person from the second image within the first image.

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    \n
  1. \n

    Analyze the second image:

    \n\n
    2. \n
  2. \n

    Analyze the first image:

    \n\n
    3. \n
  3. \n

    Define the bounding box:

    \n\n
    4. \n
  4. \n

    Final Confirmation:

    \n\n
    5. \n
  5. \n

    Construct JSON:

    \n\n
    6. \n
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</think>

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[\n\t{"bbox_2d": [451, 553, 486, 735], "label": "the target person"}\n]\n
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Qwen3.6-Plus

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\"image\"

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Real-world Image Reasoning Next

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User

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\"image\" 从北京南到崔各庄，如果14号线瘫痪，最少换乘路线是什么?

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Qwen3.6-Plus

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<think> 用户想要从“北京南站”到“崔各庄”。 前提条件是“14号线瘫痪”，意味着不能使用14号线。 目标是找到“最少换乘路线”。

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第一步：确定起点和终点的位置及可用线路。

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第二步：寻找从4号线换乘到15号线的路径（避开14号线）。

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第三步：确认最佳方案（2次换乘）。

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有没有可能1次换乘？

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结论修正： 刚才发现的 4号线 -> 西直门 (换13号线) -> 望京西 (换15号线) 是 2次换乘。 这是目前找到的最少的。

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再检查一下有没有 1次换乘 的可能？ 即：4号线 -> X号线 -> 15号线。 这意味着X号线必须同时与4号线和15号线换乘。

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确认： 4号线和13号线在 西直门 换乘。 13号线和15号线在 望京西 换乘。 所以路线：4号线 -> 13号线 -> 15号线。 换乘次数：2次。

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有没有其他2次换乘路线？

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总结最少换乘方案（均为2次换乘）：

\n
    \n
  1. 4号线 -> 13号线 -> 15号线 (换乘站：西直门、望京西)
    2. \n
  2. 4号线 -> 6号线 -> 8号线 -> 15号线 (换乘站：平安里、南锣鼓巷、奥林匹克公园) -> 等等，这是3条线，2次换乘？\n\n
    3. \n
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再次确认 4 -> 13 -> 15 方案：

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有没有其他 2次换乘 方案？ 即：4号线 -> X号线 -> 15号线。 这意味着X号线必须同时连接4号线和15号线。

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等等，再仔细看图。

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结论似乎很明确了：最少换乘是2次。 路线是：4号线 -> 13号线 -> 15号线。

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具体路径：

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    \n
  1. 在 北京南站 乘坐 4号线（往安河桥北方向）。
    2. \n
  2. 在 西直门站 下车，换乘 13号线（往东直门方向）。
    3. \n
  3. 在 望京西站 下车，换乘 15号线（往俸伯方向）。
    4. \n
  4. 在 崔各庄站 下车。
    5. \n
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再检查一遍有没有漏掉的线。

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最终确认： 最少换乘次数是 2次。 路线是 4号线 -> 13号线 -> 15号线。

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步骤梳理：

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    \n
  1. 北京南站 坐 4号线 (安河桥北方向)。
    2. \n
  2. 坐到 西直门站。
    3. \n
  3. 换乘 13号线 (东直门方向)。
    4. \n
  4. 坐到 望京西站。
    5. \n
  5. 换乘 15号线 (俸伯方向)。
    6. \n
  6. 坐到 崔各庄站。
    7. \n
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这个方案是可行的，且换乘次数最少（2次）。 其他方案如 4->10->8->15 (3次), 4->6->8->15 (3次), 4->2->5->15 (3次) 都更多。

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等等，再仔细看一眼 14号线瘫痪的影响。 题目说14号线瘫痪。 我的方案完全避开了14号线。 4号线（北京南站出发，只能坐4号线，因为14瘫痪）。 13号线（西直门换乘）。 15号线（望京西换乘）。 全程无14号线。符合题意。

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有没有可能 4号线 -> 9号线 -> …? 4号线国家图书馆换9号线。 9号线郭公庄换房山线。没用。 9号线六里桥换10号线。 9号线丰台南路换16号线。 9号线白石桥南换6号线。 看来9号线只能作为连接到6号线或10号线的桥梁，只会增加换乘次数。

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有没有可能 4号线 -> 16号线 -> …? 4号线国家图书馆换16号线。 16号线往北到西苑（换4号线，回去了）。 16号线往南… 没什么用，16号线主要在海淀山后和丰台。 16号线换乘10号线（丰台站）。 16号线换乘9号线（丰台南路）。 都不通向15号线方向。

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所以，唯一解就是 4 -> 13 -> 15。

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最后检查一遍站点名称和线路颜色。

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路线确认无误。

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撰写回答：

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    \n
  1. 指出起点和终点。
    2. \n
  2. 指出限制条件（14号线不可用）。
    3. \n
  3. 给出最少换乘方案（2次换乘）。
    4. \n
  4. 详细列出路线：4号线 -> 13号线 -> 15号线。
    5. \n
  5. 列出关键换乘站点：西直门、望京西。
    6. \n
  6. (可选) 提一下为什么这是最少的（因为4和15没有直接换乘，也没有公共的换乘线，必须经过13号线作为桥梁）。
    7. \n
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补充思考：有没有可能走 4 -> 10 -> 17 -> …? 10号线到十里河换17号线。 17号线往北… 17号线和15号线有换乘吗？ 看图：17号线在“太阳宫”附近？不，17号线在“西坝河”、“太阳宫”（10号线）、“潘家园西”… 看北段：17号线有“望京西”吗？没有。 17号线北段：未来科学城… 天通苑东… 清河营… 红军营… 太阳宫… 17号线和15号线没有交点。 17号线和13号线有交点吗？ 13号线有“光熙门”、“柳芳”。 17号线有“西坝河”（近光熙门/柳芳）。 看图上，17号线和13号线在“光熙门”/“西坝河”附近似乎没有直接换乘标记。 仔细看图例和线路。 13号线：西直门… 光熙门… 柳芳… 三元桥… 芍药居… 望京西。 17号线：十里河… 潘家园西… 双井… 广渠门外… 永安里… 东大桥… 工人体育场… 左家庄… 西坝河… 太阳宫… 芍药居(换乘13/10? 不，芍药居是10/13)。 看图上，17号线北端似乎只画到“未来科学城北”。 中间有一段：西坝河、太阳宫。 太阳宫是10号线。 17号线和13号线没有直接换乘。 所以这条路不通。

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再确认一下 4 -> 2 -> 13 -> 15? 4号线西直门直接换13号线，不需要经过2号线。西直门是4/2/13三线换乘。直接4换13即可。

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最终结论稳固。 路线：北京南站 (4号线) -> 西直门 (换乘13号线) -> 望京西 (换乘15号线) -> 崔各庄。 换乘次数：2次。 </think>

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基于提供的北京城市轨道交通线网图，在 14号线瘫痪 的前提下，从 北京南站 到 崔各庄 的最少换乘路线如下：

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最少换乘次数：2次

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具体路线方案：

\n
    \n
  1. \n

    第一程（4号线）：

    \n\n
    2. \n
  2. \n

    第一次换乘（西直门站）：

    \n\n
    3. \n
  3. \n

    第二程（13号线）：

    \n\n
    4. \n
  4. \n

    第二次换乘（望京西站）：

    \n\n
    5. \n
  5. \n

    第三程（15号线）：

    \n\n
    6. \n
\n

路线总结： 4号线（北京南站 -> 西直门） → 13号线（西直门 -> 望京西） → 15号线（望京西 -> 崔各庄）

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分析说明：

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Real-world Image Reasoning Next

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User

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\"image\" 帮我算算赚了多少钱

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Qwen3.6-Plus

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<think> 用户想要知道这几张刮刮乐一共中了多少钱。我需要仔细查看每一张彩票的中奖情况。

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第一张彩票（最左边，红色，“超给力”）：

\n\n

第二张彩票（左二，金色，“行大运”）：

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第三张彩票（右二，红色/蓝色，“多喜乐”）：

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第四张彩票（最右边，黑色，“66顺88发”）：

\n\n

总计：

\n\n

总金额： 20 + 30 + 40 + 60 = 150元。

\n

再仔细检查一遍：

\n
    \n
  1. \n

    超给力 (20元票):

    \n\n
    2. \n
  2. \n

    行大运 (20元票):

    \n\n
    3. \n
  3. \n

    多喜乐 (20元票):

    \n\n
    4. \n
  4. \n

    66顺88发 (10元票):

    \n\n
    5. \n
\n

最终合计: 20 + 30 + 40 + 60 = 150元。

\n

投入成本:

\n\n

修正： 再次看第一张票。

\n\n

修正： 再次看第二张票。

\n\n

修正： 再次看第三张票。

\n\n

修正： 再次看第四张票。

\n\n

结论： 总奖金：150元。 总成本：20+20+20+10 = 70元。 净赚：80元。

\n

用户问“转了多少钱”，大概率是问“赚了多少钱”或者“中了多少钱”。我会回答总中奖金额，并补充净赚金额。

\n

等等，再仔细看一眼第一张票“超给力”。 中奖号码是24。 我的号码里：

\n\n

再仔细看一眼第二张票“行大运”。

\n\n

再仔细看一眼第四张票“66顺88发”。

\n\n

好吧，计算结果很稳固。 总中奖：20 + 30 + 40 + 60 = 150元。 成本：20 + 20 + 20 + 10 = 70元。 利润：80元。

\n

如果是问“转了”（赚了），那就是80元。 如果是问“中了”（总奖金），那就是150元。 我会把两个都告诉他。

\n

再检查一遍第三张票“多喜乐”。

\n\n

好了，准备输出。 第一张：20元 第二张：30元 第三张：40元 第四张：60元 合计：150元。 成本：70元。 净赚：80元。

\n

用户问“转了多少钱”，“转”可能是“赚”的笔误。 我会回答：一共中了150元，扣除成本70元，净赚80元。

\n

再确认一下第四张票的面值。 右上角写着“面值10元”。 是的，成本是10元。 前三张都是20元。 总成本 20*3 + 10 = 70元。 没错。

\n

最终确认数字：

\n
    \n
  1. 超给力：中20（号码24对应20）。
    2. \n
  2. 行大运：中30（02对02）。
    3. \n
  3. 多喜乐：中40（直接刮出40）。
    4. \n
  4. 66顺88发：中10（66对应10）+ 50（88对应50）= 60。
    5. \n
\n

Total: 20+30+40+60 = 150. Profit: 150 - 70 = 80.

\n

这就很清楚了。 </think>

\n

帮你仔细算了一下，这四张刮刮乐的情况如下：

\n
    \n
  1. \n

    第一张（红色“超给力”，面值20元）：

    \n\n
    2. \n
  2. \n

    第二张（金色“行大运”，面值20元）：

    \n\n
    3. \n
  3. \n

    第三张（彩色“多喜乐”，面值20元）：

    \n\n
    4. \n
  4. \n

    第四张（黑色“66顺88发”，面值10元）：

    \n\n
    5. \n
\n
\n

总结：

\n\n

运气不错，不仅回本了，还净赚了80元！

\n

STEM Next

\n

User

\n

\"image\" 以下图表使用 126 根长度为 1 的木条构建了一个“有空心的三角形六边形”，其内部边长为 2，外部边长为 4。那么，要构建一个内部边长为 20、外部边长为 23 的有空心的三角形六边形，需要多少根木条呢？

\n

Qwen3.6-Plus

\n

<think> 这个用户想要解决一个数学问题，基于提供的图片和描述。

\n

1. 分析问题：

\n\n

2. 推导公式：

\n\n

3. 解决空心问题：

\n\n

4. 计算目标问题：

\n\n

5. 另一种思路验证（分层法）：

\n\n

6. 最终计算：

\n\n

7. 整理步骤：

\n
    \n
  1. 定义模型：将图形视为由单位长度的木条组成的三角形网格。
    2. \n
  2. 确定公式：\n\n
    3. \n
  3. 验证已知条件：\n\n
    4. \n
  4. 应用公式到目标问题：\n\n
    5. \n
  5. 输出结果。
    6. \n
\n

详细计算检查：

\n\n

看起来非常稳妥。

\n

最后的文字组织：

\n\n

补充思考： 有没有可能题目里的“内部边长”定义不一样？ 题目说“内部边长为2”。看图，中间空洞的边确实是由2根木条组成的。 题目说“外部边长为4”。看图，最外圈的边确实是由4根木条组成的。 所以定义没有问题。

\n

关于 S(n) 公式的推导补充（为了更清晰的解释）：

\n\n

好了，准备写回答。 </think>

\n

这是一个经典的几何计数问题。我们可以通过寻找规律和建立公式来解决。

\n

第一步：找出实心六边形的木条数量公式

\n

首先，我们需要知道一个边长为 n 的实心三角形六边形需要多少根木条。设这个数量为 S(n)。

\n

我们可以通过观察不同方向的木条来计数。六边形网格有三个方向的平行线（水平、左斜、右斜）。由于对称性，每个方向的木条数量是相同的。

\n

对于边长为 n 的实心六边形：

\n\n

让我们验证一下题目中给出的例子：

\n\n

第二步：分析“有空心”的结构

\n

题目中的图形是一个大六边形挖去中间的小六边形。

\n\n

空心六边形木条总数公式： Total = S(N) − S(M) + 6M 或者理解为： Total = S(N) − (S(M) − 6M) （即：大实心总数 - 小实心内部木条数）

\n

让我们用题目给出的数据验证一下：

\n\n

第三步：计算目标问题

\n

题目要求构建一个内部边长为 20 (M=20)，外部边长为 23 (N=23) 的有空心三角形六边形。

\n
    \n
  1. \n

    计算外部边长 N=23 的实心木条数 S(23)： S(23) = 9(23²) + 3(23) S(23) = 9(529) + 69 S(23) = 4761 + 69 = 4830

    \n
    2. \n
  2. \n

    计算内部边长 M=20 的实心木条数 S(20)： S(20) = 9(20²) + 3(20) S(20) = 9(400) + 60 S(20) = 3600 + 60 = 3660

    \n
    3. \n
  3. \n

    计算内壁木条数（即边长为20的周长）： 6M = 6 × 20 = 120

    \n
    4. \n
  4. \n

    计算最终结果： Total = S(23) − S(20) + 120 Total = 4830 − 3660 + 120 Total = 1170 + 120 Total = 1290

    \n
    5. \n
\n

结论

\n

要构建一个内部边长为 20、外部边长为 23 的有空心的三角形六边形，需要 1290 根木条。

\n

STEM Next

\n

User

\n

\"image\" Question: Teddy works at Please Forget Meat, a contemporary vegetarian pizza chain in the city of Gridtown, as a deliveryman. Please Forget Meat (PFM) has two convenient locations, marked with “X” and “Y ” on the street map of Gridtown shown below. Teddy, who is currently at X, needs to deliver an eggplant pizza to ∇ en route to Y , where he is urgently needed. There is currently construction taking place at A, B, and C, so those three intersections will be completely impassable. How many ways can Teddy get from X to Y while staying on the roads (Traffic tickets are expensive!), not taking paths that are longer than necessary (Gas is expensive!), and that let him pass through ∇ (Losing a job is expensive!)?

\n

Qwen3.6-Plus

\n

<think> The user wants me to solve a grid path counting problem.

\n

1. Understand the Goal: Find the number of shortest paths from point X to point Y that pass through point ∇ (let’s call it V) while avoiding points A, B, and C.

\n

2. Analyze the Grid:

\n\n

3. Break Down the Problem: The path must go X → V → Y. Since we need shortest paths, we can only move Right (R) and Up (U). The total number of ways is (Ways from X to V) × (Ways from V to Y). However, we have obstacles A, B, C.

\n\n

The path is split into two legs: Leg 1: X(0,0) → V(5,3). Leg 2: V(5,3) → Y(8,8).

\n

We need to check if the obstacles affect these legs.

\n\n

4. Calculate Leg 1: X(0,0) → V(5,3) avoiding A(1,3).

\n\n

Wait, let’s re-check the coordinates of A. Looking at the image:

\n\n

5. Calculate Leg 2: V(5,3) → Y(8,8) avoiding B(6,5) and C(5,7).

\n\n

6. Final Calculation:

\n\n

Let’s do the multiplication: 52 × 20 = 1040. 52 × 2 = 104. 1040 + 104 = 1144.

\n

Wait, let me double check everything.

\n

Coordinates Check:

\n\n

Leg 1: X(0,0) → V(5,3) avoiding A(1,3).

\n\n

Leg 2: V(5,3) → Y(8,8) avoiding B(6,5) and C(5,7).

\n\n

Total Ways: 52 × 22 = 1144.

\n

Let me just re-read the problem statement to ensure I didn’t miss anything.

\n\n

Is it possible that avoiding A makes the path from X to V not a shortest path?

\n\n

Is it possible that avoiding B and C blocks all shortest paths from V to Y?

\n\n

Let’s double check the coordinates one more time from the image.

\n\n

Everything seems consistent.

\n

Calculation review: N₁ = (8 choose 3) − (4 choose 1)(4 choose 0) = 56 − 4 = 52. N₂ = (8 choose 3) − [(3 choose 1)(5 choose 2) + (4 choose 0)(4 choose 1)]. Wait, (8 choose 3) for V → Y? V=(5,3), Y=(8,8). Δ x = 3, Δ y = 5. Total steps 8. Choose 3 Rights. (8 choose 3) = 56. Correct. Paths through B(6,5): V(5,3) → B(6,5): Δ x=1, Δ y=2. Steps 3. (3 choose 1) = 3. B(6,5) → Y(8,8): Δ x=2, Δ y=3. Steps 5. (5 choose 2) = 10. Product: 30. Correct. Paths through C(5,7): V(5,3) → C(5,7): Δ x=0, Δ y=4. Steps 4. (4 choose 0) = 1. C(5,7) → Y(8,8): Δ x=3, Δ y=1. Steps 4. (4 choose 1) = 4. Product: 4. Correct. Intersection: 0. N₂ = 56 − 30 − 4 = 22. Correct.

\n

Total = 52 × 22. 52 × 10 = 520. 52 × 20 = 1040. 52 × 2 = 104. 1040 + 104 = 1144.

\n

Is there any subtlety I missed? “Teddy … needs to deliver … to ∇ en route to Y”. This implies the sequence is X → ∇ → Y. “pass through ∇”. Yes. “construction at A, B, C”. A is on the X → ∇ leg. B, C are on the ∇ → Y leg. Are B or C on the X → ∇ leg? B=(6,5). ∇=(5,3). x_B > x_∇, so B is to the right of ∇. Since we only move Right/Up, we can’t visit B before ∇ if we start at X(0,0) and go to ∇(5,3). Wait. To visit B(6,5), we need x ≥ 6. ∇ has x=5. So we must pass ∇ (x=5) before reaching x=6. So B cannot be on the path X → ∇. C=(5,7). ∇=(5,3). y_C > y_∇. So C is above ∇. We must pass ∇ (y=3) before reaching y=7. So C cannot be on the path X → ∇. So the separation of obstacles is clean. A affects only leg 1. B, C affect only leg 2.

\n

What about A on leg 2? A=(1,3). ∇=(5,3). x_A < x_∇. Since we start leg 2 at ∇(5,3), and only move Right/Up (x increases, y increases), we can never go back to x=1. So A is not on leg 2. So the independence of the two legs holds perfectly.

\n

Final check of the arithmetic. (8 choose 3) = (8 ⋅ 7 ⋅ 6)/6 = 56. (4 choose 1) = 4. 56 − 4 = 52.

\n

(3 choose 1) = 3. (5 choose 2) = 10. 3 × 10 = 30.

\n

(4 choose 0) = 1. (4 choose 1) = 4. 1 × 4 = 4.

\n

56 − 30 − 4 = 22.

\n

52 × 22. 50 × 22 = 1100. 2 × 22 = 44. 1100 + 44 = 1144.

\n

The solution seems robust.

\n

One tiny check on “not taking paths that are longer than necessary”. This just confirms we are counting shortest paths (Manhattan paths). If A blocked all shortest paths, we would have to take a longer path. But N₁ = 52 > 0, so shortest paths exist. If B and C blocked all shortest paths from V to Y, we would have to take a longer path. But N₂ = 22 > 0, so shortest paths exist. So we don’t need to consider non-shortest paths.

\n

The problem asks “How many ways can Teddy get from X to Y … not taking paths that are longer than necessary”. This implies we only count shortest paths from X to Y that pass through ∇. A shortest path from X to Y passing through ∇ is composed of a shortest path X → ∇ and a shortest path ∇ → Y. Length(X → Y) = 8+8=16. Length(X → ∇) = 5+3=8. Length(∇ → Y) = 3+5=8. 8+8=16. So yes, concatenating shortest paths for the legs creates a shortest path for the whole journey.

\n

The solution is solid.

\n

One detail: “marked with ‘X’ and ‘Y’ on the street map”. X is the start. Y is the end. The points A, B, C are intersections. The point ∇ is an intersection. The grid lines are roads. Everything fits.

\n

Final Answer seems to be 1144.

\n

Let’s quickly write down the steps for the final response.

\n
    \n
  1. Define coordinates. X=(0,0), Y=(8,8).
    2. \n
  2. Identify coordinates of points of interest: A=(1,3), ∇=(5,3), B=(6,5), C=(5,7).
    3. \n
  3. Split the problem into two parts: Path X → ∇ and Path ∇ → Y.
    4. \n
  4. Calculate number of shortest paths for X → ∇ avoiding A.\n\n
    5. \n
  5. Calculate number of shortest paths for ∇ → Y avoiding B and C.\n\n
    6. \n
  6. Multiply the results: 52 × 22 = 1144.
    7. \n
\n

The logic holds up.

\n

Let’s double check the grid counting one last time. Horizontal squares: 1, 2, 3, 4, 5, 6, 7, 8. Vertical squares: 1, 2, 3, 4, 5, 6, 7, 8. X is bottom left corner. Y is top right corner. A: 1 right, 3 up. Correct. ∇: 5 right, 3 up. Correct. B: 6 right, 5 up. Correct. C: 5 right, 7 up. Correct.

\n

Wait, looking really closely at crop 1 and crop 4. Crop 1 shows C. C is on a vertical line. Let’s count from right. Right edge is x=8. Line left of it is x=7. Line left of that is x=6. Line left of that is x=5. C is on the line x=5. Vertical position: Top edge is y=8. Line below is y=7. C is on y=7. So C=(5,7). Correct.

\n

Crop 2 shows B. B is on vertical line x=6. (One right of C’s line). Vertical position: Top edge y=8. y=7 (line with C). y=6. y=5. B is on y=5. So B=(6,5). Correct.

\n

Crop 3 shows A and ∇. A: Left edge x=0. Next line x=1. A is on x=1. Bottom edge y=0. y=1, y=2, y=3. A is on y=3. So A=(1,3). Correct.

\n

∇: It is on the same horizontal line as A. So y=3. Vertical line: x=0, 1, 2, 3, 4, 5. It is on x=5. (Same vertical line as C). So ∇=(5,3). Correct.

\n

Everything is confirmed.

\n

One edge case: “pass through ∇”. Does this mean he stops there? Yes, “deliver an eggplant pizza to ∇”. So the path is definitely X → ∇ → Y.

\n

The calculation 52 × 22 is correct. 52 × 22 = 52 × 2 × 11 = 104 × 11 = 1144.

\n

The solution is ready.

\n

One small check on “not taking paths that are longer than necessary”. This applies to the whole trip X → Y. Since X → ∇ → Y is a shortest path (distance 16), any deviation to avoid obstacles that increases the distance would be disallowed. However, my calculation assumes we only take shortest paths for each leg. Is it possible that a shortest path X → Y via ∇ avoids obstacles by taking a “detour” that is still a shortest path globally? No. A path from X to Y via ∇ has length d(X, ∇) + d(∇, Y). Since d(X, Y) = d(X, ∇) + d(∇, Y) (because ∇ is “between” X and Y in the grid sense, i.e., 0 ≤ 5 ≤ 8 and 0 ≤ 3 ≤ 8), any shortest path from X to Y passing through ∇ is composed of a shortest path X → ∇ and a shortest path ∇ → Y. If we had to take a longer path for X → ∇ (because all shortest paths were blocked), the total path would be longer than necessary. The problem says “not taking paths that are longer than necessary”. This implies that if all shortest paths were blocked, the answer would be 0 (or we’d have to consider next shortest, but the phrasing suggests we only count shortest paths). Since we found valid shortest paths (N₁=52, N₂=22), we are good. We don’t need to look for longer paths. The condition “not taking paths that are longer than necessary” simply restricts our sample space to shortest paths.

\n

So the interpretation holds.

\n

Final result: 1144. </think>

\n

The problem asks for the number of shortest paths from X to Y that pass through ∇ while avoiding intersections A, B, and C.

\n

1. Coordinate System and Points: Let’s set X at the origin (0,0). The grid is 8 × 8, so Y is at (8,8). Based on the grid lines:

\n\n

2. Strategy: The path must go from X to V and then from V to Y. Since we are looking for shortest paths (Manhattan distance), we can only move Right (R) and Up (U). The total number of ways is the product of the number of valid paths for each leg: Total Ways = (Valid paths X → V) × (Valid paths V → Y).

\n

3. Leg 1: X(0,0) → V(5,3) avoiding A(1,3):

\n\n

4. Leg 2: V(5,3) → Y(8,8) avoiding B(6,5) and C(5,7):

\n\n

5. Total Calculation: Total Ways = N₁ × N₂ = 52 × 22. 52 × 22 = 52 × (20 + 2) = 1040 + 104 = 1144.

\n

\\boxed{1144}

\n

Visual Reasoning with Grounding Next

\n

User

\n

How many black pawns have been captured? Let’s think step by step and give the final answer in curly braces, like this: {final answer}

\n

\"image\"

\n

Qwen3.6-Plus

\n

<think> The user wants me to count the captured black pawns.

\n
    \n
  1. Identify the pieces on the board:\n\n
    2. \n
\n

</think> The black pieces are the dark wood pieces. On the board, there are 2 black pawns visible: 1. One near the top left center (dark square). 2. One near the top center (light square). A standard chess set has 8 pawns per side. Therefore, the number of captured black pawns is 8 − 2 = 6. Looking at the pile of captured pieces on the right, there are 8 dark wood pieces. Based on the pieces remaining on the board (King, Queen, 2 Rooks, 2 Knights, 2 Pawns), the missing pieces are 2 Bishops and 6 Pawns. The pile contains pieces that look like 2 Bishops and 6 Pawns (some are lying on their sides, making identification slightly tricky, but the count matches). {6}

\n

Visual Reasoning with OCR Next

\n

User

\n

请帮我分析可能的风险隐患，并且给出相关的建议 \"image\"

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Qwen3.6-Plus

\n

<think> 用户希望我分析这张图片中可能存在的风险隐患，并给出建议。

\n

1. 图像内容分析：

\n\n

2. 风险隐患识别 (Brainstorming Risks):

\n\n

3. 整理建议 (Formulating Advice):

\n\n

4. 结构化输出： 将上述分析分为“环境与路况风险”、“车内物品风险”和“驾驶建议”三个部分，条理清晰地呈现给用户。

\n\n

5. 最终润色： 语气要平和、专业、关怀。

\n\n

好了，思路清晰了，开始写回复。 </think>

\n

基于您提供的图片，我为您分析了当前驾驶环境中可能存在的风险隐患，并给出了相应的建议：

\n

一、 风险隐患分析

\n

1. 天气与路况风险（主要风险）

\n\n

2. 车内物品风险

\n\n

3. 车辆状态细节

\n\n
\n

二、 安全建议

\n

1. 驾驶操作建议

\n\n

2. 车内物品整理建议

\n\n

3. 车辆维护建议

\n\n

总结： 目前最大的风险是雨天路滑导致的追尾风险以及视线不佳。请保持耐心，慢速行驶，注意观察。

\n

Visual Reasoning with OCR Next

\n

User

\n

孕晚期孕妇能不能喝这个酸梅汤饮料 \"image\"

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Qwen3.6-Plus

\n

<think> 用户询问孕晚期孕妇能不能喝图片中的酸梅汤饮料。我需要仔细查看图片中的配料表和营养成分表，结合孕晚期的生理特点来给出建议。

\n

1. 分析配料表 (Ingredients):

\n\n

2. 分析营养成分表 (Nutrition Facts):

\n\n

3. 分析特殊说明 (Special Notes):

\n\n

4. 综合评估 (Synthesis & Advice):

\n\n

5. 构建回答结构:

\n\n

6. 润色语言: 语气要温和、客观，不要制造恐慌，但要指出潜在风险。

\n

Self-Correction during drafting: 我注意到配料表里山楂添加量是 >0.8g/L。这个量其实非常非常小。一瓶500ml也就0.4克山楂。这个剂量其实很难达到“收缩子宫”的药理剂量。但是，作为AI助手，为了安全起见，我还是应该提示这个成分的存在，让用户自己权衡。重点应该放在糖分上，因为11.5g/100ml的糖对于孕晚期控制体重和血糖是更直接的风险。

\n

Final Plan:

\n
    \n
  1. 结论： 慎喝/少喝。
    2. \n
  2. 核心风险点1：糖分高。 (11.5g/100ml -> 500ml瓶约57g糖)。这对孕晚期控糖不利。
    3. \n
  3. 核心风险点2：含有山楂和甘草。 虽然剂量低，但属于“药食同源”，山楂有活血/收缩子宫争议，甘草可能引起水肿/血压升高。
    4. \n
  4. 建议： 实在想喝，喝几口解馋即可，不要整瓶喝。如果有妊娠糖尿病或高血压，绝对不要喝。 </think>
    5. \n
\n

根据你提供的图片（配料表和营养成分表），针对孕晚期孕妇是否能喝这款酸梅汤，我的建议是：慎喝，最好少喝或不喝。如果实在想喝，只能偶尔喝一两口解馋，不要大量饮用。

\n

以下是详细的原因分析，请仔细阅读：

\n

1. 最大的隐患：糖分过高（控制血糖和体重）

\n\n

2. 需要注意的成分：山楂（子宫收缩风险）

\n\n

3. 需要注意的成分：甘草（水肿和血压风险）

\n\n

4. 食品添加剂

\n\n
\n

总结建议：

\n\n

注：以上建议基于食品营养学常识，不能替代医生的诊断。如果你身体有特殊不适，请务必咨询你的产检医生。

\n

Visual Coding

\n

We further enhanced the model’s capabilities in visual understanding, content generation, and tool use. Based on UI screenshots, product prototypes, design mockups, or natural multimodal instructions, the model can generate frontend pages, complete code, and refine interactions, gradually closing the loop from “understanding an interface” to “generating code” and then to “using tools to modify it.” This also makes multimodal models substantially more practical in real-world development workflows.

\n

Unified Understanding and Generation with Image Generation Tools Next

\n

User

\n

Please create a 5-slide PPT using a combination of text and images to introduce the life of Van Gogh.

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Qwen3.6-Plus

\n

Vincent van Gogh: A Life in Color

\n

Welcome to a visual journey through the life of one of history’s most influential artists. Though his career lasted only a decade, Vincent van Gogh created over 2,000 artworks that redefined expressionism. This presentation explores his evolution from a struggling missionary to a post-impressionist master.

\n

Qwen3.6-Plus

\n

\"image\"

\n

Qwen3.6-Plus

\n

Early Struggles & The Dutch Period (1880–1886)

\n

Before picking up a brush, Vincent tried various careers, including teaching and ministry. His early art was somber, reflecting the harsh lives of peasants. Influenced by Rembrandt and Millet, he used dark earth tones to convey empathy and realism. The masterpiece The Potato Eaters defines this era—raw, unglamorous, and deeply human.

\n

Qwen3.6-Plus

\n

\"image\"

\n

Qwen3.6-Plus

\n

Paris & The Explosion of Color (1886–1888)

\n

Moving to Paris changed everything. Exposed to Impressionism and Japanese Ukiyo-e prints, Vincent abandoned dark palettes for bright colors and loose brushwork. He experimented with pointillism and complementary colors. This period was crucial for developing his unique voice, though the chaotic city life eventually overwhelmed him, leading him to seek peace in the south.

\n

Qwen3.6-Plus

\n

\"image\"

\n

Qwen3.6-Plus

\n

Arles: The Peak of Creativity (1888–1889)

\n

In Arles, Southern France, Vincent found his sanctuary. The intense sunlight inspired his most famous works: Sunflowers, The Yellow House, and Bedroom in Arles. He dreamed of an artist colony here. During this prolific year, he produced nearly 200 paintings. However, isolation and mental health struggles culminated in the infamous incident with Paul Gauguin, leading to Vincent’s hospitalization.

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Qwen3.6-Plus

\n

\"image\"

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Qwen3.6-Plus

\n

Saint-Rémy & Auvers: Legacy & Final Days (1889–1890)

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Voluntarily entering the asylum at Saint-Rémy, Vincent continued to paint with fierce determination, creating The Starry Night. In his final months in Auvers-sur-Oise, his style became more turbulent yet profoundly expressive. He died at 37, largely unrecognized. Today, his letters to his brother Theo reveal a sensitive, intellectual man whose quest for connection transformed into a universal language of color.

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Qwen3.6-Plus

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\"image\"

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Qwen3.6-Plus

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Vincent van Gogh’s life reminds us that creativity can flourish even amidst suffering. His transformation of pain into beauty continues to inspire millions, proving that art is not just what we see, but how we feel. Thank you for joining this visual exploration of his legacy.

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Unified Understanding and Generation with Image Generation Tools Next

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User

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Please create a four-part short picture book featuring Kapibala as the main character. The theme is: It brings the first ray of sunshine to a town that is always overcast. The story should be gentle, nuanced, and the character’s appearance should remain consistent throughout.

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Qwen3.6-Plus

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In the town of Greyhollow, the sun had forgotten how to shine. Thick, woolly clouds hugged the rooftops day after day, turning the world into a palette of slate and shadow. The flowers on the windowsills hung their heads low, and the people moved quietly, wrapped in coats against the chill. But deep in the alleyways, a little friend named Kapibala was waiting. With a heart full of warmth and a tiny red scarf tied tight, Kapibala knew it was time to change the weather.

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Qwen3.6-Plus

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\"image\"

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Qwen3.6-Plus

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Kapibala climbed to the highest point in the square, right beneath the frozen hands of the Old Clock Tower. The air was still and cold. Closing its eyes, Kapibala took a deep breath, gathering every bit of kindness and hope stored in its heart. It wasn’t magic spells or loud shouts that were needed—just a gentle push. Slowly, a beam of pure, golden light began to pulse from Kapibala’s chest, cutting through the damp air like a needle through thread.

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Qwen3.6-Plus

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\"image\"

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Qwen3.6-Plus

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The beam touched the underside of the great grey blanket above. For a moment, nothing happened. Then, slowly, the clouds began to unravel. They didn’t disappear all at once; they simply softened, turning from heavy lead to wispy cotton. The golden light spilled over the edges, painting the rooftops in amber. Down in the streets, a neighbor looked up, shielding their eyes, not from glare, but from wonder. The first true ray of sunshine had arrived.

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Qwen3.6-Plus

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\"image\"

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Qwen3.6-Plus

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Greyhollow was no longer just grey. It was a place of golden mornings and long, sweet shadows. Kapibala sat on the steps of the clock tower, watching the town wake up. The flowers stood taller. The streetlamps seemed unnecessary now. Kapibala adjusted its red scarf, smiling a small, quiet smile. It hadn’t banished the clouds forever—they would return, as clouds do—but it had reminded everyone that even in the deepest overcast, the sun is always waiting just behind the veil. And sometimes, all it takes is one small spark to bring it out.

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Qwen3.6-Plus

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\"image\"

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Qwen3.6-Plus

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Through Kapibala’s gentle persistence, Greyhollow learned that light is not just something that comes from the sky, but something that can be kindled from within. The town remained beautiful, not because the clouds never returned, but because its inhabitants now knew how to find the sun, even on the greyest days.

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Unified Understanding and Generation with Image Generation Tools Next

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User

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Please design a graphic and text-based product page for a delicate and compact perfume product targeting urban white-collar workers.

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Qwen3.6-Plus

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In the rhythm of the city, your scent should not shout—it should whisper. Introducing Lumina, a fragrance designed for the modern professional who moves seamlessly from boardroom to evening gala. Compact enough for your smallest clutch, yet potent enough to leave a lasting impression.

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Qwen3.6-Plus

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\"image\"

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Qwen3.6-Plus

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Designed for Motion.

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We understand that your day is unpredictable. That’s why Lumina isn’t just a perfume; it’s a portable accessory. Its leak-proof, magnetic cap ensures security in your bag, while its ergonomic shape fits perfectly in the palm of your hand for quick, discreet application during a busy schedule.

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Qwen3.6-Plus

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\"image\"

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Qwen3.6-Plus

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The Scent Profile: Quiet Confidence

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Lumina is not about being noticed immediately; it’s about being remembered.

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Qwen3.6-Plus

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\"image\"

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Qwen3.6-Plus

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Elevate your daily ritual. Discover the art of subtle presence with Lumina.

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Qwen3.6-Plus

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Lumina: The Essence of Urban Grace. Available now in our signature 30ml travel-friendly format. Shop the collection today.

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Unified Understanding and Generation with Image Generation Tools Next

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User

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Please explain the pizza-making steps to me using a combination of text and images.

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Qwen3.6-Plus

\n

Making the perfect homemade pizza is less about complex recipes and more about mastering a few key techniques. It begins with the foundation: the dough. Whether you are using a store-bought base or making your own, the goal is to achieve a balance of chewiness and crispness. Start by ensuring your surface is well-floured to prevent sticking, then gently work the dough outward from the center. Avoid using a rolling pin if you want airy edges; instead, use your fingertips to press the gas toward the crust, creating that signature puffy rim.

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Qwen3.6-Plus

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\"image\"

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Qwen3.6-Plus

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Once your base is shaped, it’s time for the assembly. The golden rule of topping is ’less is more.’ Overloading the pizza leads to a soggy crust. Start with a thin layer of high-quality tomato sauce, leaving a clean border for the crust. Next, distribute fresh mozzarella evenly. If using wet ingredients like fresh tomatoes or vegetables, pat them dry first. Finish with a drizzle of olive oil and perhaps a few hardy herbs like oregano before baking. Save delicate greens like basil for after the bake to preserve their flavor and color.

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Qwen3.6-Plus

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\"image\"

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Qwen3.6-Plus

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Baking is where the magic happens. Preheat your oven to its highest setting—usually 500°F (260°C) or higher. If you have a pizza stone, preheat it for at least 30 minutes to mimic a professional brick oven. Slide the pizza onto the hot surface and bake for 8–12 minutes. You are looking for a leopard-spotted char on the crust and bubbling, slightly browned cheese. When it comes out, let it rest for a minute, then add your fresh basil and a final drizzle of olive oil. Slice immediately while the steam is still rising.

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Qwen3.6-Plus

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\"image\"

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Qwen3.6-Plus

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By focusing on gentle dough handling, balanced toppings, and high-heat baking, you can transform simple ingredients into a restaurant-quality pizza at home. Remember, the best pizza is one shared with good company, so don’t hesitate to experiment with your favorite flavors once you’ve mastered the basics.

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Vision to Code Next

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User

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我是开皮衣店的，一些商品图片在image_list/文件夹下，给我搞一个炫酷的店面首页，要用上这些商品示例图并配上精美讲解。

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Qwen3.6-Plus

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Video Understanding

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Qwen3.6-Plus not only continues to improve its understanding of video content itself, but also increasingly supports video analysis and processing scenarios that are closer to real-world tasks. Compared with static images, video understanding requires the model to jointly handle temporal information, dynamic changes, and cross-frame relationships, making it a stronger test of the model’s ability to move from perception to understanding and processing. Our goal is for the model not only to understand what is happening in a video, but also to perform further analysis, extraction, and processing based on video content.

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Video to lecture notes Next

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User

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Inspired by: wdkns/wdkns-skills

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qianwen-res.oss-accelerate.aliyuncs.com

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Video Editing Next

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User

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Please Edit this video

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User

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Qwen3.6-Plus

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Visual Agent Applications

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Our focus is on how the model can continuously perceive, reason, and take action in an environment. In GUI Agent scenarios, for example, the model can understand the current state of a screen and combine this with planning capabilities to decide and execute the next step. Explorations such as OpenClaw further highlight the potential of multimodal models to complete complex interactive tasks in open environments. Combined with Claude Code-style workflows, multi-hop search, CI, and external tool use, the model can gradually evolve from a single-turn assistant into an execution system for real-world tasks: first understanding the problem, then retrieving information, generating solutions, invoking tools, and iterating based on feedback.

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Computer-Using Agent Next

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User

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Create a list of drip coffee makers that are on sale and within $25-60 and have a black finish.

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Qwen3.6-Plus

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Computer-Using Agent Next

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User

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Please assist me in exporting my contacts of Personal Address Book from Thunderbird into contacts.csv file in the desktop and convert it to xIsx with Libreoffice Calc.

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Qwen3.6-Plus

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Claude Code Next

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User

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OpenClaw Next

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User

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Shadow Matching Next

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User

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\"image\" Find the shadow that perfectly matches the puppy pattern above.

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Qwen3.6-Plus

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<think>

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To solve this task, I need to identify the correct shadow of the puppy from the given options. I’ll first crop the main puppy image and then compare it with each of the shadow options by converting them to grayscale and checking for similarity or by matching key features. Since the shadows are silhouettes, I can also try template matching or simply comparing the shapes after thresholding. Given the grid layout, I’ll first segment the grid of shadows into individual cells. Then, I’ll compare the silhouette of the original puppy (converted to a binary mask) with each shadow option to find the best match.

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</think>

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import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Load the image\nimage = np.array(image_clue[0])\ngray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n\n# The image has a main figure at the top and a 3x3 grid of shadows below.\n# I need to segment these regions.\n# Let's first threshold to separate the content from the white background.\n_, thresh = cv2.threshold(gray, 200, 255, cv2.THRESH_BINARY_INV)\n\n# Find contours to locate the bounding boxes of the different elements\ncontours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\nboxes = []\nfor cnt in contours:\n    x, y, w, h = cv2.boundingRect(cnt)\n    if w > 50 and h > 50: # Filter small noise\n        boxes.append((x, y, w, h))\n\n# Sort boxes. The top one is the reference. The rest are the grid.\n# Sorting by y-coordinate first to separate top from bottom, then by x.\n# However, simple sorting might be tricky if y-coordinates vary slightly.\n# Let's cluster by y-coordinate.\nboxes_sorted_y = sorted(boxes, key=lambda k: k[1])\n\n# The top box is the reference puppy\nref_box = boxes_sorted_y[0]\n\n# The remaining 9 boxes are the grid. Let's sort them into rows and columns.\ngrid_boxes = boxes_sorted_y[1:]\n\n# Sort by y-coordinate to group into rows\n# We expect 3 rows of 3.\n# A simple way is to sort by y, then split into chunks of 3, but we need to be careful about row alignment.\n# Let's sort by y and find significant jumps.\ngrid_boxes_sorted_y = sorted(grid_boxes, key=lambda k: k[1])\n\nrows = []\nif len(grid_boxes_sorted_y) == 9:\n    # Assuming 3x3 grid\n    # We can just sort all 9 by y, then take chunks of 3? No, that assumes perfect alignment.\n    # Better: sort by y, then group those with similar y.\n    current_row = [grid_boxes_sorted_y[0]]\n    for i in range(1, len(grid_boxes_sorted_y)):\n        prev_box = grid_boxes_sorted_y[i-1]\n        curr_box = grid_boxes_sorted_y[i]\n        # If the y-difference is small, same row\n        if abs(curr_box[1] - prev_box[1]) < 50:\n            current_row.append(curr_box)\n        else:\n            rows.append(sorted(current_row, key=lambda k: k[0])) # Sort row by x\n            current_row = [curr_box]\n    rows.append(sorted(current_row, key=lambda k: k[0]))\n\nprint(f"Reference box: {ref_box}")\nprint(f"Number of rows found: {len(rows)}")\nfor i, row in enumerate(rows):\n    print(f"Row {i+1}: {[b[:2] for b in row]}")\n\n# Now, let's create a mask for the reference puppy and compare with each grid item.\n# Since the colors are different, shape matching is best.\n# I'll extract the reference ROI, convert to gray, threshold to get silhouette.\n# Then do the same for each grid item and compare using IoU or correlation.\n\ndef get_silhouette_mask(box, img_gray):\n    x, y, w, h = box\n    roi = img_gray[y:y+h, x:x+w]\n    # Threshold to get the object. The background is white (255), object is darker.\n    _, mask = cv2.threshold(roi, 240, 255, cv2.THRESH_BINARY_INV)\n    return mask\n\nref_mask = get_silhouette_mask(ref_box, gray)\n# Resize ref_mask to a standard size for easier comparison if needed, but direct comparison on cropped sizes is okay if we handle resizing.\n# Let's resize all masks to a common size, say 100x100, to compare shapes regardless of slight size differences.\ntarget_size = (100, 100)\nref_mask_resized = cv2.resize(ref_mask, target_size)\n\nbest_match_score = -1\nbest_match_pos = None\n\nscores = []\n\nfor r_idx, row in enumerate(rows):\n    for c_idx, box in enumerate(row):\n        grid_mask = get_silhouette_mask(box, gray)\n        grid_mask_resized = cv2.resize(grid_mask, target_size)\n        \n        # Calculate Intersection over Union (IoU) or simple correlation\n        # Let's use normalized cross-correlation or just overlap ratio since they should be identical silhouettes.\n        # But wait, the shadows are solid gray, while the original is colored.\n        # The silhouette extraction `get_silhouette_mask` should handle this by thresholding non-white pixels.\n        \n        intersection = cv2.bitwise_and(ref_mask_resized, grid_mask_resized)\n        union = cv2.bitwise_or(ref_mask_resized, grid_mask_resized)\n        \n        inter_area = np.count_nonzero(intersection)\n        union_area = np.count_nonzero(union)\n        \n        if union_area == 0:\n            score = 0\n        else:\n            score = inter_area / union_area\n        \n        scores.append(((r_idx + 1, c_idx + 1), score))\n        if score > best_match_score:\n            best_match_score = score\n            best_match_pos = (r_idx + 1, c_idx + 1)\n\nprint(f"Scores: {scores}")\nprint(f"Best match: Row {best_match_pos[0]}, Column {best_match_pos[1]} with score {best_match_score}")\n\n# Let's display the reference mask and the best match mask to verify\nbest_box = rows[best_match_pos[0]-1][best_match_pos[1]-1]\nbest_mask = get_silhouette_mask(best_box, gray)\nbest_mask_resized = cv2.resize(best_mask, target_size)\n\nplt.figure(figsize=(10, 5))\nplt.subplot(1, 3, 1)\nplt.imshow(image[ref_box[1]:ref_box[1]+ref_box[3], ref_box[0]:ref_box[0]+ref_box[2]])\nplt.title("Reference Image")\nplt.axis('off')\n\nplt.subplot(1, 3, 2)\nplt.imshow(ref_mask_resized, cmap='gray')\nplt.title("Reference Silhouette")\nplt.axis('off')\n\nplt.subplot(1, 3, 3)\nplt.imshow(best_mask_resized, cmap='gray')\nplt.title(f"Best Match Silhouette (Row {best_match_pos[0]}, Col {best_match_pos[1]})")\nplt.axis('off')\n\nplt.tight_layout()\nplt.show()\n
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User

\n

\"image\" Reference box: (234, 0, 137, 172) Number of rows found: 3 Row 1: [(59, 195), (242, 195), (424, 195)] Row 2: [(60, 378), (242, 369), (424, 369)] Row 3: [(59, 544), (242, 546), (424, 546)] Scores: [((1, 1), 0.9165560716655607), ((1, 2), 0.9268617021276596), ((1, 3), 0.950050454086781), ((2, 1), 0.5300538701445988), ((2, 2), 0.9076158940397351), ((2, 3), 0.9250420168067227), ((3, 1), 0.8811154598825832), ((3, 2), 0.9370816599732262), ((3, 3), 0.9149950674120355)] Best match: Row 1, Column 3 with score 0.950050454086781

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Qwen3.6-Plus

\n

<think>

\n

The code successfully segmented the image into a grid of shadows and compared them to the reference puppy image.

\n\n

Let’s double check the visual output.

\n\n

The code’s analysis points to Row 1, Column 3. The visual confirmation from the plotted silhouettes supports this. The “Reference Silhouette” derived from the colored image matches the “Best Match Silhouette” from Row 1, Col 3 very well.

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Therefore, the correct shadow is at Row 1, Column 3.

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</think>

\n

Based on the code analysis and visual verification, the shadow that perfectly matches the puppy pattern is located at row 1, column 3.

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Find the Different Pattern Next

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User

\n

\"image\" Find the pattern that is different from the others in the image.

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Qwen3.6-Plus

\n

<think>

\n

To locate the different pattern, I’ll first preprocess the image to segment the individual Christmas tree icons. Then, I’ll compare each icon to find the one that is distinct from the majority. I’ll start by loading the image, converting it to grayscale, and then detecting contours to identify the grid structure and extract each cell.

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</think>

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import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sklearn.cluster\n\n# Load the image\nimage = np.array(image_clue[0])\ngray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n\n# Threshold to get the shapes\n# The background is white, and the shapes are red/blue.\n# I'll use Otsu's thresholding after Gaussian blurring.\nblur = cv2.GaussianBlur(gray, (5, 5), 0)\nret, thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n\n# Find contours to detect individual items\ncontours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n# Collect bounding boxes\nboxes = []\nfor cnt in contours:\n    area = cv2.contourArea(cnt)\n    # Filter small noise and the large border if present\n    if 500 < area < 5000: # Adjust thresholds as needed\n        x, y, w, h = cv2.boundingRect(cnt)\n        boxes.append((x, y, w, h))\n\n# Sort boxes to form a grid\n# First, sort by y-coordinate to group into rows\n# I need to determine a tolerance to group boxes into the same row\nboxes_sorted_y = sorted(boxes, key=lambda k: k[1])\nrows = []\nif boxes_sorted_y:\n    current_row = [boxes_sorted_y[0]]\n    for i in range(1, len(boxes_sorted_y)):\n        prev_box = current_row[-1]\n        curr_box = boxes_sorted_y[i]\n        # If the y-difference is small, they are in the same row\n        if abs(curr_box[1] - prev_box[1]) > 20: # Heuristic threshold\n            rows.append(sorted(current_row, key=lambda k: k[0]))\n            current_row = []\n        current_row.append(curr_box)\n    rows.append(sorted(current_row, key=lambda k: k[0]))\n\nprint(f"Number of rows detected: {len(rows)}")\nfor i, row in enumerate(rows):\n    print(f"Row {i+1}: {len(row)} items")\n\n# Visualize the grid to confirm\nvis_image = image.copy()\nfor i, row in enumerate(rows):\n    for j, box in enumerate(row):\n        x, y, w, h = box\n        cv2.rectangle(vis_image, (x, y), (x + w, y + h), (0, 255, 0), 1)\n\nplt.figure(figsize=(15, 15))\nplt.imshow(vis_image)\nplt.title(f"Grid: {len(rows)}x{len(rows[0]) if rows else 0}")\nplt.axis('off')\nplt.tight_layout()\nplt.show()\n\n# Now extract features and find the outlier\n# I'll resize each extracted ROI to a common size and flatten it.\n# Then use clustering or distance metrics to find the anomaly.\nrois = []\npositions = []\nfor r_idx, row in enumerate(rows):\n    for c_idx, box in enumerate(row):\n        x, y, w, h = box\n        roi = image[y:y+h, x:x+w]\n        roi_resized = cv2.resize(roi, (30, 30)) # Resize to standard size\n        rois.append(roi_resized.flatten())\n        positions.append((r_idx + 1, c_idx + 1))\n\nrois_np = np.array(rois)\n\n# Find the outlier using isolation forest or simply distance from the median\nfrom sklearn.ensemble import IsolationForest\n\n# Flatten and normalize\nX = rois_np.astype(float) / 255.0\n\n# Simple approach: calculate mean and find the point with largest distance\nmean_vec = np.mean(X, axis=0)\ndistances = np.linalg.norm(X - mean_vec, axis=1)\n\noutlier_idx = np.argmax(distances)\noutlier_pos = positions[outlier_idx]\nprint(f"Outlier found at: Row {outlier_pos[0]}, Column {outlier_pos[1]}")\n\n# Let's also verify by looking at the top few outliers\ntop_k_indices = np.argsort(distances)[::-1][:5]\nfor idx in top_k_indices:\n    print(f"Rank {idx}: Pos={positions[idx]}, Dist={distances[idx]}")\n
\n

User

\n

\"image\" Number of rows detected: 8 Row 1: 13 items Row 2: 13 items Row 3: 13 items Row 4: 13 items Row 5: 13 items Row 6: 13 items Row 7: 13 items Row 8: 13 items Outlier found at: Row 7, Column 2 Rank 79: Pos=(7, 2), Dist=11.814130663783704 Rank 17: Pos=(2, 5), Dist=3.473024320815396 Rank 16: Pos=(2, 4), Dist=3.2116998559176713 Rank 24: Pos=(2, 12), Dist=3.207321160000082 Rank 22: Pos=(2, 10), Dist=3.195749619002271

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Qwen3.6-Plus

\n

<think>

\n

The code successfully detected an 8x13 grid of Christmas tree icons. It then compared each extracted icon to find the outlier. The analysis identified an outlier at Row 7, Column 2 with a significantly higher distance (11.81) compared to the next highest distances (around 3.2-3.5). This suggests that the icon at (7, 2) is indeed different from the others.

\n

Looking at the original image and the cropped grid visualization, I can visually verify this. Most trees have a blue bow on top. The tree at row 7, column 2 appears to be missing the blue bow on top.

\n

Let me double check the row and column indexing.

\n\n

Therefore, the different pattern is at row 7, column 2.

\n

</think>

\n

Based on the code analysis, the outlier is located at row 7, column 2. Visual inspection confirms that the Christmas tree at this position is missing the blue bow on top, while all others have it.

\n

Final Answer: (7, 2)

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Count Points on Blue Line Next

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User

\n

\"image\" In the pattern, how many points do the blue line segments pass through in total?

\n

Qwen3.6-Plus

\n

<think>

\n

To count the number of points the blue line segments pass through, I’ll first process the image to identify the grid of points and the blue line. Then, I’ll determine which points lie on the blue line segments.

\n

</think>

\n
import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sklearn.cluster\n\n# Load the image\nimage = np.array(image_clue[0])\n\n# Convert to HSV to segment the blue color\nhsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\n\n# Define range of blue colors\n# The background is white, points are black, lines are cyan/blue.\n# Let's try to segment the blue/cyan color.\nlower_blue = np.array([80, 50, 50])\nupper_blue = np.array([120, 255, 200])\n\n# Threshold the HSV image to get blue components\nmask1 = cv2.inRange(hsv, lower_blue, upper_blue)\nres1 = cv2.bitwise_and(image, image, mask=mask1)\n\n# Also, the points are black dots. Let's find all dots first to establish the grid.\ngray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n# Threshold to find dark spots (dots and lines)\n_, thresh = cv2.threshold(gray, 100, 255, cv2.THRESH_BINARY_INV)\n\n# Find contours to locate the centers of the dots\ncontours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\ndot_centers = []\nfor contour in contours:\n    area = cv2.contourArea(contour)\n    # Filter based on area to find the dots. The dots seem small but distinct.\n    # The lines are also thin. Let's look at the distribution of areas or just visualize.\n    if 10 < area < 100: # Adjust thresholds as needed\n        M = cv2.moments(contour)\n        if M["m00"] != 0:\n            cX = int(M["m10"] / M["m00"])\n            cY = int(M["m01"] / M["m00"])\n            dot_centers.append((cX, cY))\n\n# Let's visualize the detected centers and the mask to ensure we have the grid\nplt.figure(figsize=(20, 5))\n\nplt.subplot(1, 3, 1)\nplt.imshow(mask1, cmap='gray')\nplt.title(f"Blue Mask")\nplt.axis('off')\n\nplt.subplot(1, 3, 2)\nplt.imshow(thresh, cmap='gray')\nplt.title(f"Threshold (Dots + Lines)")\nplt.axis('off')\n\n# Visualize detected centers on original image\nvis_image = image.copy()\nfor i, (x, y) in enumerate(dot_centers):\n    cv2.circle(vis_image, (x, y), 5, (255, 0, 0), -1)\n\nplt.subplot(1, 3, 3)\nplt.imshow(vis_image)\nplt.title(f"Detected {len(dot_centers)} centers")\nplt.axis('off')\n\nplt.tight_layout()\nplt.show()\n\n# Print coordinates to understand the grid structure\nprint(f"Number of detected centers: {len(dot_centers)}")\nprint(f"Centers: {dot_centers}")\n
\n

User

\n

\"image\" Number of detected centers: 6 Centers: [(159, 116), (69, 116), (205, 70), (114, 70), (23, 70), (69, 25)]

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Qwen3.6-Plus

\n

<think>

\n

The previous code attempted to detect centers but seems to have only found 6, likely due to the thresholding or contour detection parameters not being robust enough for all the small black dots. The “Blue Mask” shows the blue lines are somewhat fragmented, and the “Threshold (Dots + Lines)” shows the dots are visible but some are faint.

\n

Let me try a different approach:

\n
    \n
  1. Read the image.
    2. \n
  2. Convert to HSV to segment the blue color more effectively. This should give me a clean mask of the blue lines.
    3. \n
  3. Also, create a mask for the black dots. Since the background is white, simple thresholding should work for the dots.
    4. \n
  4. Find the coordinates of all the black dots (the grid points).
    5. \n
  5. Check which of these dot coordinates are “close enough” to the blue line pixels.
    6. \n
\n

Let’s start by detecting the grid of points and the blue lines separately.

\n

</think>

\n
import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Load the image\nimage = np.array(image_clue[0])\nhsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\n\n# Define range of blue colors\n# The lines are a distinct cyan/blue.\nlower_blue = np.array([80, 50, 50])\nupper_blue = np.array([130, 255, 255])\n\n# Threshold the HSV image to get blue components\nmask_blue = cv2.inRange(hsv, lower_blue, upper_blue)\nresult_blue = cv2.bitwise_and(image, image, mask=mask_blue)\n\n# Now detect the black dots\n# Convert to grayscale\ngray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n# Threshold to find dark spots (dots and lines)\n_, mask_dark = cv2.threshold(gray, 50, 255, cv2.THRESH_BINARY_INV)\n\n# We need to isolate just the dots. The lines are also dark in grayscale but we have a blue mask.\n# Let's subtract the blue mask area from the dark mask to find dots that are NOT blue.\n# But wait, the question asks for points the blue line segments PASS THROUGH.\n# This implies we need to find the grid points that are intersected by the blue line.\n\n# Strategy:\n# 1. Find all grid point locations (centroids of black blobs).\n# 2. Find all blue line pixel locations.\n# 3. For each grid point, check if it is close to any blue pixel.\n\n# Step 1: Find grid points.\n# The dots are black circles.\n# Let's use simple blob detection or contour finding on the inverted grayscale, excluding the blue region if necessary,\n# or just finding all dark blobs and filtering by size/shape.\n# Since the blue lines also connect some dots, the dots on the line might be part of the blue structure.\n# However, the original image has distinct black dots.\n\n# Let's try to detect circular blobs.\n# Invert grayscale\ngray_inv = 255 - gray\n# Threshold to get dark things\n_, binary = cv2.threshold(gray, 80, 255, cv2.THRESH_BINARY_INV)\n\n# Morphological operations to separate dots from lines if they are connected?\n# Actually, let's just find contours and get centroids.\ncontours, _ = cv2.findContours(binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\ndot_centers = []\nfor cnt in contours:\n    area = cv2.contourArea(cnt)\n    # Filter based on area to ignore noise and maybe large connected components if any\n    # The dots seem to be roughly similar size.\n    if 20 < area < 500: # Adjust thresholds as needed\n        M = cv2.moments(cnt)\n        if M["m00"] != 0:\n            cX = int(M["m10"] / M["m00"])\n            cY = int(M["m01"] / M["m00"])\n            dot_centers.append((cX, cY))\n\n# Let's visualize the detected dot centers and the blue mask\nplt.figure(figsize=(20, 5))\n\nplt.subplot(1, 4, 1)\nplt.imshow(image)\nplt.title("Original Image")\nplt.axis('off')\n\nplt.subplot(1, 4, 2)\nplt.imshow(mask_blue, cmap='gray')\nplt.title("Blue Mask")\nplt.axis('off')\n\nplt.subplot(1, 4, 3)\nplt.imshow(binary, cmap='gray')\nplt.title("Binary Dark Mask")\nplt.axis('off')\n\n# Visualize detected centers\nvis_image = image.copy()\nfor i, center in enumerate(dot_centers):\n    cv2.circle(vis_image, center, 5, (255, 0, 0), -1)\n    \nplt.subplot(1, 4, 4)\nplt.imshow(vis_image)\nplt.title(f"Detected {len(dot_centers)} Dot Centers")\nplt.axis('off')\n\nplt.tight_layout()\nplt.show()\n\n# Print coordinates for debugging\nprint(f"Detected {len(dot_centers)} dot centers.")\nprint("Centers:", dot_centers)\n\n# Now, check intersection\n# Get coordinates of blue pixels\nblue_pixels = np.column_stack(np.where(mask_blue > 0)) # (y, x)\nblue_pixel_set = set(map(tuple, blue_pixels))\n\npoints_on_line = []\nfor center in dot_centers:\n    cx, cy = center\n    # Check if this center is close to any blue pixel\n    # A simple distance check or checking a small neighborhood\n    is_on_line = False\n    # Check a small radius around the center\n    for dy in range(-5, 6):\n        for dx in range(-5, 6):\n            ny, nx = cy + dy, cx + dx\n            if (ny, nx) in blue_pixel_set:\n                is_on_line = True\n                break\n        if is_on_line:\n            break\n    \n    if is_on_line:\n        points_on_line.append(center)\n\nprint(f"Points on blue line: {len(points_on_line)}")\nprint("Coordinates:", points_on_line)\n
\n

User

\n

\"image\" Detected 0 dot centers. Centers: [] Points on blue line: 0 Coordinates: []

\n

Qwen3.6-Plus

\n

<think>

\n

The previous attempt to detect dot centers failed (0 centers detected). The “Binary Dark Mask” shows the dots are visible but fragmented. The “Blue Mask” looks quite good, showing the continuous blue path.

\n

Let me refine the dot detection:

\n
    \n
  1. The dots are black on a white background. I can threshold the grayscale image to find dark regions.
    2. \n
  2. The “Binary Dark Mask” shows noise. I should use morphological operations (opening/closing) to clean up the mask and connect fragmented parts of the dots.
    3. \n
  3. Then find contours and get centroids.
    4. \n
  4. I also need to robustly detect the blue line pixels. The HSV approach seemed okay, but I can improve it by converting the mask to a set of coordinates.
    5. \n
  5. Finally, for each detected dot center, I’ll check if it’s within a certain distance of any blue pixel.
    6. \n
\n

Let me also print out the grid dimensions to help debug. It looks like a 5x5 grid.

\n

</think>

\n
import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Load image\nimage = np.array(image_clue[0])\nh, w, _ = image.shape\n\n# --- Step 1: Detect Blue Line ---\nhsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\nlower_blue = np.array([80, 50, 50])\nupper_blue = np.array([120, 255, 255])\nblue_mask = cv2.inRange(hsv, lower_blue, upper_blue)\n\n# Dilate blue mask slightly to ensure we cover points that might be under the line ends\nkernel = np.ones((5,5),np.uint8)\ndilated_blue_mask = cv2.dilate(blue_mask, kernel, iterations=2)\nblue_coords = np.column_stack(np.where(dilated_blue_mask > 0))\n\n# --- Step 2: Detect Black Dots ---\ngray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n# Threshold to get dark spots\n_, binary = cv2.threshold(gray, 50, 255, cv2.THRESH_BINARY_INV)\n\n# Morphological operations to clean up noise and solidify dots\nkernel_dot = np.ones((3,3),np.uint8)\n# Erode to remove small noise, then dilate to restore size\neroded = cv2.erode(binary, kernel_dot, iterations=2)\ndilated_binary = cv2.dilate(eroded, kernel_dot, iterations=3)\n\n# Find contours for dots\ncontours, _ = cv2.findContours(dilated_binary, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\ndot_centers = []\nfor cnt in contours:\n    area = cv2.contourArea(cnt)\n    # Filter based on area to avoid noise or large text/lines if any\n    # Based on visual inspection, dots are small but significant\n    if 20 < area < 500: \n        M = cv2.moments(cnt)\n        if M["m00"] != 0:\n            cX = int(M["m10"] / M["m00"])\n            cY = int(M["m01"] / M["m00"])\n            # Check if this center is NOT part of the blue line (to avoid detecting blue endpoints as dots if they overlap significantly, though dots are black)\n            # Actually, the dots are black, so they won't be in the blue mask.\n            # But the blue line connects them.\n            dot_centers.append((cX, cY))\n\n# Sort dot centers to form a grid (optional but good for debugging)\n# Just visualize all detected centers\ndebug_image = image.copy()\npoints_on_line_count = 0\npoints_on_line_coords = []\n\nfor center in dot_centers:\n    x, y = center\n    # Check distance to nearest blue pixel\n    # We have blue_coords as (y, x)\n    dists = np.sqrt(np.sum((blue_coords - [y, x])**2, axis=1))\n    min_dist = np.min(dists) if len(dists) > 0 else float('inf')\n    \n    # If the dot center is close to the blue line (or covered by dilated mask)\n    # Since we dilated the blue mask, we can just check if the point is in the mask\n    # Or check min_dist. Let's use a threshold.\n    if min_dist < 15: # 15 pixels tolerance\n        points_on_line_count += 1\n        points_on_line_coords.append(center)\n        cv2.circle(debug_image, (x, y), 10, (255, 0, 0), -1) # Red for on line\n    else:\n        cv2.circle(debug_image, (x, y), 5, (0, 255, 0), -1) # Green for not on line\n\nprint(f"Total dots detected: {len(dot_centers)}")\nprint(f"Dots on blue line: {points_on_line_count}")\nprint(f"Coordinates of dots on line: {points_on_line_coords}")\n\nplt.figure(figsize=(20, 5))\n\nplt.subplot(1, 4, 1)\nplt.imshow(image)\nplt.title("Original Image")\nplt.axis('off')\n\nplt.subplot(1, 4, 2)\nplt.imshow(blue_mask, cmap='gray')\nplt.title("Blue Mask")\nplt.axis('off')\n\nplt.subplot(1, 4, 3)\nplt.imshow(dilated_binary, cmap='gray')\nplt.title("Cleaned Dot Mask")\nplt.axis('off')\n\nplt.subplot(1, 4, 4)\nplt.imshow(debug_image)\nplt.title(f"Detected: {len(dot_centers)} dots, {points_on_line_count} on line")\nplt.axis('off')\n\nplt.tight_layout()\nplt.show()\n
\n

User

\n

Total dots detected: 0 Dots on blue line: 0 Coordinates of dots on line: [] \"image\"

\n

Qwen3.6-Plus

\n

<think>

\n

The dot detection is still failing. The “Cleaned Dot Mask” is completely black, which means my morphological operations or thresholding parameters are too aggressive. The dots are small and might be getting removed.

\n

Let me try a simpler approach for dot detection:

\n
    \n
  1. Load the image.
    2. \n
  2. Convert to grayscale.
    3. \n
  3. Apply a simple binary threshold (e.g., < 50) to get the dark dots.
    4. \n
  4. Use cv2.connectedComponentsWithStats or find contours on this thresholded image without aggressive morphological operations first.
    5. \n
  5. Filter by area to remove noise.
    6. \n
\n

For the blue line:

\n
    \n
  1. I’ll use the HSV mask from before, which seemed to work reasonably well (white lines on black background).
    2. \n
  2. Dilate the blue mask slightly to ensure it covers the center of the dots that lie on the line.
    3. \n
\n

Then, I’ll check for overlap between the dot centers and the dilated blue mask.

\n

I’ll also visualize the detected dots and the blue mask to confirm everything is working.

\n

</think>

\n
import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Load image\nimage = np.array(image_clue[0])\nh, w, _ = image.shape\n\n# --- Detect Blue Line ---\nhsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\nlower_blue = np.array([80, 50, 50])\nupper_blue = np.array([130, 255, 255])\nmask_blue = cv2.inRange(hsv, lower_blue, upper_blue)\n# Dilate blue mask to cover dots that are on the line\nkernel = np.ones((5,5),np.uint8)\ndilated_blue_mask = cv2.dilate(mask_blue, kernel, iterations=2)\n\n# --- Detect Dots ---\ngray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n# Threshold to find dark dots (black on white)\n_, mask_dots = cv2.threshold(gray, 60, 255, cv2.THRESH_BINARY_INV)\n\n# Morphological operations to clean up noise but keep dots\n# Since dots are somewhat circular, opening might help remove small noise\nkernel_dots = np.ones((3,3),np.uint8)\n# Erode then dilate to separate close objects if needed, but here just cleaning\n# Let's try simple closing to fill gaps in dots if any, then opening to remove noise\ncleaned_dot_mask = cv2.morphologyEx(mask_dots, cv2.MORPH_CLOSE, kernel_dots)\ncleaned_dot_mask = cv2.morphologyEx(cleaned_dot_mask, cv2.MORPH_OPEN, kernel_dots)\n\n# Find contours for dots\ncontours, _ = cv2.findContours(cleaned_dot_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\ndot_centers = []\nfor cnt in contours:\n    area = cv2.contourArea(cnt)\n    # Filter by area - dots should have a reasonable size\n    if 20 < area < 500: # Adjust thresholds as needed\n        M = cv2.moments(cnt)\n        if M["m00"] != 0:\n            cX = int(M["m10"] / M["m00"])\n            cY = int(M["m01"] / M["m00"])\n            dot_centers.append((cX, cY))\n\n# --- Count points on blue line ---\npoints_on_line = []\nfor i, (x, y) in enumerate(dot_centers):\n    # Check if the point (x,y) is within the dilated blue mask\n    # We need to handle image boundaries\n    if 0 <= y < h and 0 <= x < w:\n        if dilated_blue_mask[y, x] > 0:\n            points_on_line.append((x, y))\n\nprint(f"Total dots detected: {len(dot_centers)}")\nprint(f"Dots on blue line: {len(points_on_line)}")\nprint(f"Coordinates of dots on line: {points_on_line}")\n\n# Visualize results\nplt.figure(figsize=(20, 5))\n\nplt.subplot(1, 4, 1)\nplt.imshow(image)\nplt.title("Original Image")\nplt.axis('off')\n\nplt.subplot(1, 4, 2)\nplt.imshow(dilated_blue_mask, cmap='gray')\nplt.title("Dilated Blue Mask")\nplt.axis('off')\n\nplt.subplot(1, 4, 3)\nplt.imshow(cleaned_dot_mask, cmap='gray')\nplt.title("Cleaned Dot Mask")\nplt.axis('off')\n\nplt.subplot(1, 4, 4)\nresult_image = image.copy()\nfor x, y in dot_centers:\n    cv2.circle(result_image, (x, y), 8, (0, 255, 0), -1) # Green for all dots\nfor x, y in points_on_line:\n    cv2.circle(result_image, (x, y), 12, (255, 0, 0), 2) # Red ring for dots on line\n\nplt.imshow(result_image)\nplt.title(f"Detected: {len(dot_centers)} dots, {len(points_on_line)} on line")\nplt.axis('off')\n\nplt.tight_layout()\nplt.show()\n
\n

User

\n

Total dots detected: 0 Dots on blue line: 0 Coordinates of dots on line: [] \"image\"

\n

Qwen3.6-Plus

\n

<think>

\n

The “Cleaned Dot Mask” now shows some white dots, but the contour detection is still finding 0 dots. This is strange. Let me look at the mask again - it shows small white squares. Maybe the area filter area > 5 is too strict for these tiny dots, or the thresholding is producing very small components that are being filtered out incorrectly in my logic (though 0 detected suggests no contours passed the filter).

\n

Wait, looking at the “Cleaned Dot Mask”, there are indeed dots visible. The issue might be in how I’m iterating or counting. Let me print the number of contours found before filtering.

\n

Also, I notice the “Dilated Blue Mask” looks good.

\n

Let me try to:

\n
    \n
  1. Re-detect dots with a much lower area threshold or no area threshold initially, just to see what’s happening.
    2. \n
  2. Visualize the contours on the original image to debug.
    3. \n
  3. Since the grid seems regular (5x5), I could also try to detect all dark blobs and then organize them into a grid, but simple contour detection should suffice if tuned right.
    4. \n
\n

Let’s also count manually by looking at the image to verify my final result later. Top row: 5 dots Row 2: 5 dots Row 3: 5 dots Row 4: 5 dots Row 5: 5 dots Total 25 dots.

\n

The blue line passes through:

\n\n

Let me re-examine the path visually:

\n\n

Let’s just use code to detect the dots and check overlap. I’ll lower the area threshold significantly.

\n

</think>

\n
import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Load image\nimage = np.array(image_clue[0])\nh, w, _ = image.shape\n\n# 1. Detect Blue Line\nhsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\nlower_blue = np.array([100, 50, 50])\nupper_blue = np.array([130, 255, 255])\nblue_mask = cv2.inRange(hsv, lower_blue, upper_blue)\n# Dilate to make lines thicker and connect with dots\nkernel = np.ones((5,5),np.uint8)\ndilated_blue_mask = cv2.dilate(blue_mask, kernel, iterations=2)\n\n# 2. Detect Dots (Black)\ngray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n# Threshold to get dark spots\n_, dot_mask = cv2.threshold(gray, 80, 255, cv2.THRESH_BINARY_INV)\n\n# Find contours for dots\ncontours, _ = cv2.findContours(dot_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\ndot_centers = []\nfor cnt in contours:\n    area = cv2.contourArea(cnt)\n    # Filter noise but keep small dots. The image is likely high res.\n    # Let's collect all and then filter based on expected size or just visualize.\n    if area > 5: # Very small threshold\n        M = cv2.moments(cnt)\n        if M["m00"] != 0:\n            cX = int(M["m10"] / M["m00"])\n            cY = int(M["m01"] / M["m00"])\n            dot_centers.append((cX, cY))\n\nprint(f"Total dot centers detected: {len(dot_centers)}")\n\n# Check which dots are on the blue line\ndots_on_line = []\nfor i, center in enumerate(dot_centers):\n    x, y = center\n    # Check if this point is within the dilated blue mask\n    # The mask coordinates are (y, x) for array access\n    if dilated_blue_mask[y, x] > 0:\n        dots_on_line.append(center)\n\nprint(f"Dots on blue line: {len(dots_on_line)}")\n\n# Visualize\nplt.figure(figsize=(20, 5))\n\nplt.subplot(1, 4, 1)\nplt.imshow(image)\nplt.title("Original Image")\nplt.axis('off')\n\nplt.subplot(1, 4, 2)\nplt.imshow(dilated_blue_mask, cmap='gray')\nplt.title("Dilated Blue Mask")\nplt.axis('off')\n\nplt.subplot(1, 4, 3)\nplt.imshow(dot_mask, cmap='gray')\nplt.title("Dot Mask (Binary Inverted)")\nplt.axis('off')\n\n# Create a copy to draw on\nvis_image = image.copy()\nfor center in dot_centers:\n    cv2.circle(vis_image, center, 5, (0, 255, 0), -1) # Green for all dots\nfor center in dots_on_line:\n    cv2.circle(vis_image, center, 10, (255, 0, 0), 2) # Red ring for dots on line\n\nplt.subplot(1, 4, 4)\nplt.imshow(vis_image)\nplt.title(f"Detected: {len(dot_centers)} dots, {len(dots_on_line)} on line")\nplt.axis('off')\n\nplt.tight_layout()\nplt.show()\n\n# Print coordinates of dots on line for verification\nprint(f"Coordinates of dots on line: {dots_on_line}")\n
\n

User

\n

Total dot centers detected: 11 Dots on blue line: 0 \"image\" Coordinates of dots on line: []

\n

Qwen3.6-Plus

\n

<think>

\n

The previous attempt to create a “Dilated Blue Mask” failed - it shows almost nothing. This is likely because the HSV range I used didn’t capture the blue color well in this specific image, or the mask creation logic was flawed. The “Dot Mask (Binary Inverted)” shows the dots are fragmented again.

\n

Let me start fresh with a more robust approach:

\n
    \n
  1. Dot Detection: Convert to grayscale, threshold to get dark pixels. Use morphological closing to connect dot fragments. Find contours and filter by area. Since I know there are 25 dots in a roughly 5x5 grid, I can use that to validate.
    2. \n
  2. Blue Line Detection: Convert to HSV. The blue color seems quite distinct. I’ll try a broader range of blue/cyan. Then create a binary mask. Dilate it to make lines thicker.
    3. \n
  3. Overlap Check: For each detected dot center, check if the corresponding location in the blue line mask is white (or has high value).
    4. \n
\n

I’ll also print the coordinates of all detected dots to ensure I have the full grid.

\n

</think>

\n
import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Load image\nimage = np.array(image_clue[0])\ngray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n\n# --- Dot Detection ---\n# Threshold to find dark dots\n_, dot_mask = cv2.threshold(gray, 80, 255, cv2.THRESH_BINARY_INV)\n\n# Morphological operations to clean up dots\nkernel = np.ones((5,5),np.uint8)\ndilated_dot_mask = cv2.dilate(dot_mask, kernel, iterations=2)\neroded_dot_mask = cv2.erode(dilated_dot_mask, kernel, iterations=1)\n\n# Find contours for dots\ndot_contours, _ = cv2.findContours(eroded_dot_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\ndot_centers = []\nfor cnt in dot_contours:\n    area = cv2.contourArea(cnt)\n    if area > 20: # Filter small noise\n        M = cv2.moments(cnt)\n        if M["m00"] != 0:\n            cX = int(M["m10"] / M["m00"])\n            cY = int(M["m01"] / M["m00"])\n            dot_centers.append((cX, cY))\n\nprint(f"Detected {len(dot_centers)} dots.")\n\n# Sort dots into a grid (top-to-bottom, left-to-right)\n# Since it's a grid, we can sort by y then x with some tolerance\ndot_centers.sort(key=lambda k: k[1]) # Sort by y first\nrows = []\nif len(dot_centers) > 0:\n    current_row = [dot_centers[0]]\n    current_y = dot_centers[0][1]\n    for i in range(1, len(dot_centers)):\n        pt = dot_centers[i]\n        if abs(pt[1] - current_y) < 20: # Same row\n            current_row.append(pt)\n        else:\n            rows.append(sorted(current_row, key=lambda k: k[0]))\n            current_row = [pt]\n            current_y = pt[1]\n    rows.append(sorted(current_row, key=lambda k: k[0]))\n\nprint(f"Grid dimensions: {len(rows)} rows")\nfor i, row in enumerate(rows):\n    print(f"Row {i}: {len(row)} dots")\n\n# --- Blue Line Detection ---\nhsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\n# Define range of blue colors\n# The line is a bright cyan/blue.\nlower_blue = np.array([90, 50, 50])\nupper_blue = np.array([110, 255, 255])\n\n# Threshold the HSV image to get blue components\nmask1 = cv2.inRange(hsv, lower_blue, upper_blue)\n# Sometimes blue wraps around 180, but usually not for this shade.\n# Let's try a wider range just in case\nlower_blue2 = np.array([80, 50, 50])\nupper_blue2 = np.array([120, 255, 255])\nmask2 = cv2.inRange(hsv, lower_blue2, upper_blue2)\n\nblue_mask = cv2.bitwise_or(mask1, mask2)\n\n# Dilate blue mask to cover dot centers\nkernel_line = np.ones((5,5),np.uint8)\ndilated_blue_mask = cv2.dilate(blue_mask, kernel_line, iterations=3)\n\n# --- Count points on line ---\npoints_on_line = 0\ncoords_on_line = []\n\nfor r_idx, row in enumerate(rows):\n    for c_idx, pt in enumerate(row):\n        x, y = pt\n        # Check if this point is in the dilated blue mask\n        # Need to handle boundaries\n        if 0 <= y < dilated_blue_mask.shape[0] and 0 <= x < dilated_blue_mask.shape[1]:\n            if dilated_blue_mask[y, x] > 0:\n                points_on_line += 1\n                coords_on_line.append(pt)\n\nprint(f"Points on blue line: {points_on_line}")\nprint(f"Coordinates: {coords_on_line}")\n\n# --- Visualization ---\nplt.figure(figsize=(20, 5))\n\nplt.subplot(1, 4, 1)\nplt.imshow(image)\nplt.title("Original Image")\nplt.axis('off')\n\nplt.subplot(1, 4, 2)\nplt.imshow(dot_mask, cmap='gray')\nplt.title("Initial Dot Mask (Inverted)")\nplt.axis('off')\n\nplt.subplot(1, 4, 3)\nplt.imshow(eroded_dot_mask, cmap='gray')\nplt.title("Cleaned Dot Mask")\nplt.axis('off')\n\nplt.subplot(1, 4, 4)\nplt.imshow(dilated_blue_mask, cmap='gray')\nplt.title("Dilated Blue Mask")\nplt.axis('off')\n\nplt.tight_layout()\nplt.show()\n\n# Visualize detected points on original image\nvis_image = image.copy()\nfor r_idx, row in enumerate(rows):\n    for c_idx, pt in enumerate(row):\n        x, y = pt\n        color = (0, 255, 0) # Green for all\n        if pt in coords_on_line:\n            color = (255, 0, 0) # Red for on line\n        cv2.circle(vis_image, (x, y), 8, color, -1)\n\nplt.figure(figsize=(10, 10))\nplt.imshow(vis_image)\nplt.title(f"Detected {len(dot_centers)} dots. {points_on_line} on line.")\nplt.axis('off')\nplt.tight_layout()\nplt.show()\n
\n

User

\n

Detected 25 dots. Grid dimensions: 5 rows Row 0: 5 dots Row 1: 5 dots Row 2: 5 dots Row 3: 5 dots Row 4: 5 dots Points on blue line: 14 Coordinates: [(114, 25), (205, 25), (69, 70), (159, 70), (22, 116), (114, 116), (205, 116), (69, 162), (160, 162), (205, 161), (68, 207), (114, 208), (159, 208), (205, 207)] \"image\" \"image\"

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Qwen3.6-Plus

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<think>

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The code successfully detected 25 dots in a 5x5 grid and identified 14 points that lie on the blue line. The visualization shows red dots for points on the line and green for others, which matches the visual pattern in the original image perfectly.

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Let me verify the count by looking at the last image:

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Total: 2 + 2 + 3 + 3 + 4 = 14 points.

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The code output says “Points on blue line: 14”. This matches my manual verification from the visualization.

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The question asks “how many points do the blue line segments pass through in total?”. The answer is 14.

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</think>

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Based on the code analysis and visualization, the blue line segments pass through 14 points in total.

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Sandakan Market Sign Next

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User

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\"image\" Please answer the following question and also provide your problem-solving roadmap. Question: This picture was taken in September 2023. Approximately tens of meters northwest of the shooting location, there is a bustling local shopping plaza. In 2024, what content was written vertically on the right side of the market’s main sign?

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Qwen3.6-Plus

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<think>

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Let me analyze the image carefully. I can see:

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  1. A sign that reads “SATUAN PERIKANAN SANDAKAN” with Chinese characters and “SANDAKAN FISHING ASSOCIATION” below it.
    2. \n
  2. Below that, there’s a sign for “YU HONG AUTO” with Chinese characters.
    3. \n
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The location is clearly Sandakan, Malaysia (Sabah). The sign “SATUAN PERIKANAN SANDAKAN” translates to “Sandakan Fishing Association” in Malay.

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The question asks about a shopping plaza approximately tens of meters northwest of this shooting location, and what content was written vertically on the right side of the market’s main sign in 2024.

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So I’m looking for a shopping plaza near the Sandakan Fishing Association, likely in the area around Jalan Bulu Sim Sim or the nearby market district. I need to find what’s northwest of this location and identify the market’s main sign with its vertical text from 2024.

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</think>

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<tool_call> <function=web_search> <parameter=queries> [“Sandakan Fishing Association location address”, “Sandakan market shopping plaza near Sandakan Fishing Association”, “Sandakan wet market main sign 2024”] </tool_call> <tool_call> <function=image_search> <parameter=bbox> [0, 430, 1000, 720] <parameter=img_idx> 0 </tool_call>

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User

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<tool_response>

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[1] https://wanderlog.com/place/details/4239959/sandakan-fishing-association\nTitle: Sandakan Fishing Association - Wanderlog\nSnippet: This location is in Sandakan town, in a fishing association, the table are ... 5-Day Sandakan Itinerary · Map. Address. Pusat Bandar Sandakan, 90000 ...\nDate: N/A\n\n[2] https://www.facebook.com/groups/386802805032470/posts/2314909118888486/\nTitle: 5.30pm until 9.30pm (new operating) Location - Sandakan Fishing ...\nSnippet: Delivery time: 12pm-2pm (Lunch) 5pm-7pm(Dinner) This is our address 223 Rangoon Road #01-01/02 Singapore 218460 Our restaurant operating hours : ...\nDate: Dec 31, 2024\n\n[3] https://www.facebook.com/goodfoodsabah/posts/-sandakan-fishing-association-%E5%B1%B1%E6%89%93%E6%A0%B9%E9%B1%BC%E4%B8%9A%E5%85%AC%E4%BC%9Asandakan-fishing-association-persatuan-peri/379621820547337/\nTitle: Sandakan Fishing Association 山打根鱼业公会 Sandakan Fishing ...\nSnippet: This place is located at Sandakan Town next to Wisma Khoo, first floor, follow the map link below to locate it. IMPORTANT - Need reservation, no ...\nDate: Sep 5, 2021\n\n[4] https://www.youtube.com/watch?v=Oq1jE5Daw_Y\nTitle: Must Try Authentic Home Cooked Food @ Sandakan Fishing ...\nSnippet: ... location guide at the bottom) The chef is cooking everything by ... Address:- Sandakan Fishing Association First Floor, Sandakan Town ...\nDate: Feb 12, 2023\n\n[5] https://mysociety.my/societies/info/34215/persatuan-perikanan-sandakan-sandakan-fishing-association\nTitle: Persatuan Perikanan Sandakan (sandakan Fishing Association)\nSnippet: 9TH FLOOR, LOT 3, BLOCK XLVII, JALAN SATU SANDAKAN, 90000 SANDAKAN. Persatuan Perikanan Sandakan (sandakan Fishing Association).\nDate: N/A\n\n[6] https://reviewbah.com/sandakan/restaurant/sandakan-fishing-association\nTitle: Sandakan Fishing Association - Seafood - Reviewbah\nSnippet: Sandakan Fishing Association is a restaurant listing. It is located at Sandakan ... Location. Pusat Bandar Sandakan, 90000 Sandakan, Sabah, Malaysia. Quick ...\nDate: N/A\n\n[7] https://wanderlog.com/list/geoCategory/192713/best-cheap-eats-in-sandakan-division\nTitle: The 15 best cheap eats in Sandakan Division - Wanderlog\nSnippet: This location is in Sandakan town, in a fishing association, the table are very limited, only 4 tables, the seafood is very fresh, you can come and try it.\nDate: N/A\n\n[8] https://www.facebook.com/nataliehomestaysdk/photos/sandakan-fishing-association-%E5%B1%B1%E6%89%93%E6%A0%B9%E6%B8%94%E4%B8%9A%E5%85%AC%E4%BC%9A%E5%9C%B0%E5%9D%80lebuh-empat-pusat-bandar-sandakan-90000-sa/5032547516864135/\nTitle: Natalie - 【Sandakan Fishing Association 山打根渔业公会】 地址 ...\nSnippet: 【Sandakan Fishing Association 山打根渔业公会】 地址：Lebuh Empat, Pusat Bandar Sandakan, 90000, Sandakan, Sabah 电话：012-8646186 营业 ...\nDate: May 6, 2022\n\n[9] https://www.instagram.com/p/CTeIVcinBdR/\nTitle: Sandakan Fishing Association 山打根鱼业公会 Sandakan Fishing ...\nSnippet: ... located at Sandakan Town, next to Wisma Khoo, first floor. IMPORTANT - Need reservation, no walk-in. Limited table. Takeaway is available ...\nDate: Sep 5, 2021\n\n[10] https://genshin-impact.fandom.com/wiki/Fishing_Association\nTitle: Fishing Association | Genshin Impact Wiki - Fandom\nSnippet: This grassroots organization founded by people who love fishing has members throughout Teyvat. They are innately passionate about fishing.\nDate: N/A\n\n[11] https://www.tripadvisor.com/Attraction_Review-g303997-d33392982-Reviews-Sandakan_Central_Market-Sandakan_Sandakan_Division_Sabah.html\nTitle: Sandakan Central Market (2026) - All You MUST Know ... - Tripadvisor\nSnippet: A large department store with many local items, especially the fruit, vegetables and fish. Annoying stairs to other floors, if you have difficulty walking.\nDate: N/A\n\n[12] https://wanderlog.com/place/details/1139556/sandakan-central-market\nTitle: Sandakan Central Market - Wanderlog\nSnippet: Sandakan Central Market is a bustling bazaar-like setting located near the waterfront, offering a wide array of local products and culinary delights.\nDate: N/A\n\n[13] https://sandakan.rainforestlodge.com/pages/central-market\nTitle: Central Market - Sandakan Rainforest Lodge\nSnippet: The Central Market is where the locals come to buy fresh produce, fruits, delicacies and crafts, sarongs and seashells. You may spot stalls selling shirts, ...\nDate: N/A\n\n[14] https://www.mysabah.com/wordpress/sandakan-central-market/\nTitle: Sandakan Central Market - MySabah.com\nSnippet: Sandakan Central Market is located in Old Slipway of Sandakan City Centre ... Sandakan Harbour Square, the largest shopping mall of Sandakan.\nDate: Dec 29, 2015\n\n[15] https://www.travelfish.org/sight_profile/malaysia/sabah/sabah/sandakan/1698\nTitle: Sandakan Central Market - Travelfish.org\nSnippet: Dry and wet markets with fresh veggies, fruit, coffee, meat and more dried fish than you've possibly ever seen in your life.\nDate: Sep 26, 2025\n\n[16] https://www.tripadvisor.com/Attraction_Review-g303997-d33392868-Reviews-Harbour_Mall_Sandakan-Sandakan_Sandakan_Division_Sabah.html\nTitle: Harbour Mall Sandakan (2026) - All You MUST Know Before You ...\nSnippet: Sandakan Central Market. 3.7. (3). Flea & Street Markets ; Gerbang Sandakan. 3.0. (1). Flea & Street Markets ; Watsons Harbour Mall. 3.0. (1).\nDate: Aug 7, 2025\n\n[17] https://www.sabahcarrentaltour.com/travel/destinations/sandakan-central-market-a-complete-guide-for-tourists/\nTitle: A Food Lover's Guide to Sandakan Central Market\nSnippet: Visit Sandakan Central Market for fresh seafood, local snacks, handmade crafts, and an authentic taste of Sabah life.\nDate: Aug 28, 2025\n\n[18] https://www.trip.com/travel-guide/shops/city/sandakan-central-market-33462799/\nTitle: Sandakan Central Market - Global Shopping\nSnippet: This is a central vegetable market next to the Sheraton Hotel. It's a big place. It's mainly vegetables, with less meat. The seafood market is in a annex ...\nDate: N/A\n\n[19] https://www.facebook.com/groups/167988053254960/posts/24192786973681732/\nTitle: Early morning street market in Sandakan . - Facebook\nSnippet: One can take a leisurely stroll along the newly renovated waterfront esplanade, watching fishing boats shuttling back and forth; or find a ...\nDate: Aug 16, 2025\n\n[20] https://www.fodors.com/world/asia/malaysia/places/sandakan/things-to-do/shopping\nTitle: 3 Best Places to Shop in Sandakan, Malaysia - Fodors Travel Guide\nSnippet: For a little local flavor, the city's Central Market is your best bet. In this bustling bazaar-like setting on the far left of the waterfront, ...\nDate: N/A\n\n[21] https://www.instagram.com/p/DR7PYmRDe45/\nTitle: Borneo market scenes. A slow wander through Sandakan Central ...\nSnippet: Since 2024, one of the most meaningful changes to #Sandakan soul is seeing this once-lively street turn quiet. There was a time when it glowed ...\nDate: Dec 6, 2025\n\n[22] https://www.facebook.com/NabaluNews/posts/the-issue-surrounding-the-wholesale-night-market-at-the-sandakan-public-market-a/1661873961687687/\nTitle: The issue surrounding the wholesale night market at the Sandakan ...\nSnippet: The issue surrounding the wholesale night market at the Sandakan Public Market area, which is set to cease operations after 28 February, ...\nDate: Jan 28, 2026\n\n[23] https://www.facebook.com/DailyExpressMalaysia/posts/sandakan-municipal-council-mps-president-walter-kenson-said-the-relocation-is-pa/1351685700313797/\nTitle: Sandakan Municipal Council (MPS) President Walter Kenson said ...\nSnippet: Sandakan Municipal Council (MPS) President Walter Kenson said the relocation is part of efforts to rationalise and restructure land use in ...\nDate: Feb 5, 2026\n\n[24] https://wanderlog.com/place/details/1139556/sandakan-central-market\nTitle: Sandakan Central Market - Wanderlog\nSnippet: Sandakan Central Market is a bustling bazaar-like setting located near the waterfront, offering a wide array of local products and culinary delights.\nDate: N/A\n\n[25] https://www.malaymail.com/news/life/2024/12/15/from-wet-market-to-the-world-sabahan-chefs-journey-from-fine-dining-to-success-with-sandakans-hakka-fried-pork-noodles/158311\nTitle: From wet market to the world: Sabahan chef's journey from fine ...\nSnippet: Named after its humble origins in a Sandakan wet market, Ah Yen's stall has been offering these noodles since the 1940s in pre-independence ...\nDate: Dec 15, 2024\n\n[26] https://www.youtube.com/watch?v=NZDW1J3HyTI\nTitle: 48 Hours, 3 Flavors of Sandakan's Humble to Heritage Food ...\nSnippet: It's a journey of simple & soul warming broth of wet market noodles is a taste of the local life. ... 0:00:47 4) Sandakan Main Market - https:// ...\nDate: Apr 12, 2025\n\n[27] https://www.tiktok.com/@terrypernell/video/7606665155354594573\nTitle: Sneak peek alert! From live CNY Night Market in Sandakan - TikTok\nSnippet: Saya minta maaf makcik² Saya cuma usik main main saja Saya sayang ... Kompleks Sukan Sandakan 6-10 Nov 2024 ⏰ 10am-12mlm ...\nDate: Feb 14, 2026\n\n[28] https://www.instagram.com/p/C9EKbACOKe8/\nTitle: Local wet market in Sandakan Norrh od Borneo. July 2024 - Instagram\nSnippet: Local wet market in Sandakan Norrh od Borneo. July 2024 · Mt Trusmadi climb. 2D1N via Tambunan On Sales. Please do whatsapl us now 0146792148.\nDate: Jul 5, 2024\n\n[29] https://malaysia.news.yahoo.com/wet-market-world-sabahan-chef-230000712.html\nTitle: From wet market to the world: Sabahan chef's journey from fine ...\nSnippet: From wet market to the world: Sabahan chef's journey from fine dining to success with Sandakan's Hakka fried pork noodles · More in Lifestyle ...\nDate: Dec 14, 2024\n\n[30] https://sabaheats.com/guide-food-hunting-sandakan/\nTitle: Sandakan Food Trail – Foods you must try when in Sandakan\nSnippet: The “must eat” at Sandakan is the Seafood. The seafood is fresh and cheap. There are fish noodles, fish cake, seafood bak kut teh and much more for you to ...\nDate: N/A\n
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</tool_response>

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User

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<tool_response> [1] “ [https://lookaside.fbsbx.com/lookaside/crawler/media/?media_id=10163672222271251"](https://lookaside.fbsbx.com/lookaside/crawler/media/?media_id=10163672222271251\\)山打根历史悠久老子号。山打根渔业公会。 https://www.facebook.com/groups/756464989127995/posts/1332582768182878/[2] “[https://lookaside.fbsbx.com/lookaside/crawler/media/?media_id=4048441012152371"](https://lookaside.fbsbx.com/lookaside/crawler/media/?media_id=4048441012152371\\) John Kong - John Kong added a new photo — at Sandakan… https://www.facebook.com/johnkong.my/photos/d41d8cd9/4048441012152371/[3] “https://qcloud.dpfile.com/pc/aVPimt7I51PJtsWCB6ipRiAtzYxNqm9wy1uqVsybeSFWzW91wEPlJ_i-gVjxZJh-l0cm-Lf9tDMlLZpO7rb3bg.jpg" 山打根渔业公会- 图片-山打根-大众点评网 https://www.dianping.com/shop/k3IQ6hFAVjMBQugc/photos\"image\"[4] “[https://lookaside.fbsbx.com/lookaside/crawler/media/?media_id=1525588800882074"](https://lookaside.fbsbx.com/lookaside/crawler/media/?media_id=1525588800882074\\) 山打根美食城Sandakan Food Lover… - 山打根美食城Sandakan Food Lover https://www.facebook.com/sandakan.food.beverage/photos/d41d8cd9/1525588800882074/[5] “[https://lookaside.fbsbx.com/lookaside/crawler/media/?media_id=2066935790508222"](https://lookaside.fbsbx.com/lookaside/crawler/media/?media_id=2066935790508222\\) 山打根舊相片Sandakan Old Photos 山打根漁業公會食館（ 2017 ） Sandakan Fishing Association ( 2017 ) . https://www.facebook.com/groups/1661585370931571/posts/2200556177034485/[6] “[https://lookaside.instagram.com/seo/google_widget/crawler/?media_id=2656597484471358669"](https://lookaside.instagram.com/seo/google_widget/crawler/?media_id=2656597484471358669\\) 🔥🔥🔥 Sandakan Fishing Association 🔥🔥🔥 山打根鱼业公会 Sandakan Fishing Association (Persatuan Perikanan Sandakan) serves fresh seafoods. I like their Crab dish, dip the Mantou (bun) with the Crab sauce… yummy!! Tiger Prawn is https://www.instagram.com/p/CTeIVcinBdR/[7] “[https://ak-d.tripcdn.com/images/1mi4p224x982ge0te0251.jpg?proc=resize/m_z,w_375,h_0;format/f_webp,9C2E"](https://ak-d.tripcdn.com/images/1mi4p224x982ge0te0251.jpg?proc=resize/m_z,w_375,h_0;format/f_webp,9C2E\\) Sandakan legendary fish restaurant “Omakase” | Trip.com Sandakan https://vn.trip.com/moments/detail/sandakan-14894-140971063\"image\"[8] “[https://lookaside.instagram.com/seo/google_widget/crawler/?media_id=3823582466976901039"](https://lookaside.instagram.com/seo/google_widget/crawler/?media_id=3823582466976901039\\) 🐟 🦐 🦀 Every time I visit Sandakan, I must come to “Omakase Seafood Restaurant”. This is already my 5th time here, never disappointed! Super fresh seafood & the uncle chef’s 👨🏻🍳 https://www.instagram.com/p/DUQHZQ7k8qt/[9] “https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEibNsB6ny-OJcP6zh3sCs4B9WxoWW5L_cdf89GSSXRHD0agB-JaO-_A4Grbu1bCirhMS7NIFhbN9vUIVAGM4-TtCtg1hnxJPGtdx09YNdDCSPTV6MlwhZrjKe74ovmFyqNlp3bi_CFg1w/s280/IMG_0975-001.JPG" Sally’s Kitchen & Bakery: [2014 沙巴之旅九天遊] Day 3 亞庇=> 山打根 https://sallykitchenbakery.blogspot.com/2014/04/2014-day-3.html[10] “[https://lookaside.fbsbx.com/lookaside/crawler/media/?media_id=523046921136272"](https://lookaside.fbsbx.com/lookaside/crawler/media/?media_id=523046921136272\\) Facebook https://www.facebook.com/media/set/?set=a.523046841136280&type=3 </tool_response>

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Qwen3.6-Plus

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<think>

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Now I have confirmed the location is the Sandakan Fishing Association (Persatuan Perikanan Sandakan), located at Lebuh Empat, Pusat Bandar Sandakan.

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The question asks about a bustling local shopping plaza approximately tens of meters northwest of this location, and what content was written vertically on the right side of the market’s main sign in 2024.

\n

Looking at the search results, I can see references to:

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  1. Sandakan Central Market - located near the waterfront
    2. \n
  2. Harbour Mall Sandakan - described as the largest shopping mall of Sandakan
    3. \n
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The Sandakan Fishing Association is at Lebuh Empat, Pusat Bandar Sandakan.

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So the Central Market is likely northwest of the Fishing Association, and I’m seeing references to a wholesale night market at the Sandakan Public Market area that was set to cease operations after February 28, 2026. I need to find more specific details about what’s written on the market’s main sign in 2024.

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</think>

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<tool_call> <function=web_search> <parameter=queries> [“Sandakan Central Market sign 2024”, “Pasar Besar Sandakan signboard 2024”, “Sandakan Public Market main sign vertical writing 2024”] </tool_call> <tool_call> <function=web_search_image> <parameter=queries> [“Sandakan Central Market sign 2024”, “Pasar Besar Sandakan signboard”] </tool_call>

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User

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<tool_response>

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[31] https://www.tripadvisor.ca/Attraction_Review-g303997-d33392982-Reviews-Sandakan_Central_Market-Sandakan_Sandakan_Division_Sabah.html\nTitle: Sandakan Central Market (2026) All You MUST Know ... - Tripadvisor\nSnippet: Central market not really a tourist attraction or place to shop, more directly for the local community with fresh seafood, fruit, vegetables and everyday ...\nDate: N/A\n\n[32] https://www.instagram.com/explore/locations/139798172739921/sandakan-central-market-harbour-square/\nTitle: Sandakan Central Market -harbour square - Instagram\nSnippet: See photos and videos taken at this location and explore places nearby.\nDate: N/A\n\n[33] https://www.facebook.com/rafik2u/posts/sandakan-town-december-1-2024/4413517325541182/\nTitle: Sandakan Town - December 1, 2024 - Facebook\nSnippet: Pokok besar ditanam atas bangunan sudah potong? 2. Tangkai longkang rosak dan usang sudah baiki? 3. Longkang sumbat di taman tun razak sudah ...\nDate: Dec 1, 2024\n\n[34] https://www.instagram.com/reel/DVUvCeYge8Q/\nTitle: pasar umum sandakan - Instagram\nSnippet: Jalan-jalan ke pasar umum Sandakan. Kakak ini pasar apa? Cara umum Sandakan. Okey terima kasih. Basikal boleh ikutkah? Bonceng. Bonceng apa?\nDate: Feb 28, 2026\n\n[35] https://www.youtube.com/watch?v=f4Zc74WnecE\nTitle: Pasar Sandakan,Sabah (28 March 2024) - YouTube\nSnippet: Khabar Dari Sabah: Lima kampung panas di Sandakan, dikenal pasti · RM1 & RM2 SAHAJA | IKAN BAKAR PASAR SIM SIM SANDAKAN - MAKAN KELILING MALAYSIA.\nDate: Mar 28, 2024\n\n[36] https://www.instagram.com/reel/DNyI6ePwtKP/\nTitle: Ini pasar bikin kaka jadi suka   Sebelum subuh boleh sudah kamu ...\nSnippet: sandakanfoodie2024. •. Follow ... Uina besar juga belangkasnya ni Pasar Umum Sandakan, Sabah\nDate: Aug 25, 2025\n\n[37] https://www.youtube.com/watch?v=ysKRjeh4cBk\nTitle: Bazar Ramadhan Bataras IJM 2024 | Sandakan Sabah - YouTube\nSnippet: Sign in. This content isn't available. Bazar ramadhan Bataras IJM ... PASAR BESAR SANDAKAN SUASANA PAGI HARI DI MARKET PUSAT BANDAR⭐️.\nDate: Mar 18, 2024\n\n[38] https://www.instagram.com/reel/DQwSWmXjTm1/\nTitle: Sandakan Part 2 (Pasar Umum Sandakan) Tempat ni kalau pagi ...\nSnippet: SARUMUMSANDA SANDA SAR UMUM 山 打根中央巴 巴 打根 根 打 中央 中 央 NDAKAN CENTRAL ΜΑ Welcome To SANDAKAN PART2 2 MOSH AHPAGAS EP PASAR UMUM ...\nDate: Nov 7, 2025\n\n[39] https://econsave.com.my/\nTitle: Econsave | Everyday Low Price Supermarket | Grocery Shopping\nSnippet: Looking for economical shopping? Econsave supermarket has your back! Save money by shopping wisely. Visit our website for amazing savings!\nDate: N/A\n\n[40] https://www.instagram.com/explore/locations/20823742/pasar-besar-sandakan/\nTitle: Pasar Besar Sandakan on Instagram • Photos and Videos\nSnippet: See photos and videos taken at this location and explore places nearby.\nDate: N/A\n\n[41] https://www.facebook.com/mahkotasenii/photos/d41d8cd9/692870350031235/\nTitle: Kinabatangan kini - Facebook\nSnippet: Antaranya ialah; ✓ Banner ✓ Bunting ✓ Signboard / Lightbox / Signage ... SABAH 2024 KOMU SANDAKA POLITEKNI 12-14J SANDAKANSAB NUARI2024 JUARI KOLEJ ...\nDate: N/A\n\n[42] https://www.instagram.com/p/C4Ayl27PZez/\nTitle: Pasar Umum Sandakan, Sabah Photo credit via FB LM ... - Instagram\nSnippet: Selamat bercuti semuanya · Lokasi bersejarah yang ada di Sandakan masih berfungsi dan digunakan sehingga ke hari ini · Dorang bilang ni ...\nDate: Mar 2, 2024\n\n[43] https://www.facebook.com/cab.siowou/posts/sandakan-north-borneo-1880ssandakan-formerly-known-at-various-times-as-elopura-s/8297673663614885/\nTitle: Sandakan, North Borneo, 1880s Sandakan formerly known at ...\nSnippet: As the capital of North Borneo, Sandakan become an active commercial and trading centre. The main trading partners were Hong Kong and Singapore.\nDate: Sep 30, 2024\n\n[44] https://www.facebook.com/100050219631278/posts/at-the-end-of-1879-old-sandakan-was-burnt-down-and-mr-pryer-who-then-as-now-was-/1113593243657986/\nTitle: At the end of 1879 old Sandakan was burnt down and Mr. Pryer who ...\nSnippet: Walking through the streets of Sandakan, one can see everywhere three- to ten-story- high shop houses, their top floors adorned with signs ...\nDate: Nov 19, 2024\n\n[45] https://www.facebook.com/northborneohistory/posts/sandakan-was-one-of-the-richest-towns-in-asia-during-the-timber-era/3082688798444512/\nTitle: Sandakan was one of the richest towns in Asia during the timber era\nSnippet: Walking through the streets of Sandakan, one can see everywhere three- to ten-story- high shop houses, their top floors adorned with signs ...\nDate: Jun 7, 2020\n\n[46] https://www.capitala.com/misc/Capital_A_ar2024.pdf\nTitle: [PDF] ANNUAL REPORT 2024 - Capital A\nSnippet: Welcome to Capital A's Annual Report 2024, which has been prepared to provide stakeholders with a deeper understanding of: Information in this ...\nDate: Apr 28, 2025\n\n[47] https://deramakot.sabah.gov.my/images/pdf/pub/3rd_FMP_Deramakot.pdf\nTitle: [PDF] FOREST MANAGEMENT PLAN - Deramakot - Sabah.gov\nSnippet: Kuamut villagers' main concern is to have direct road access from their village to any nearest main road so they can gain market access for their ...\nDate: N/A\n\n[48] https://www.gentingplantations.com/wp-content/uploads/2025/04/Integrated-Annual-Report-2024.pdf\nTitle: [PDF] INTEGRATED ANNUAL REPORT 2024 | Genting Plantations Berhad\nSnippet: Since 1965, the Genting Group has risen above all challenges and made the seemingly impossible feats possible.\nDate: Apr 11, 2025\n\n[49] https://www.danaugirang.com.my/wp-content/uploads/2023/11/Improving-Outcomes-of-Major-Infrastructural-Development-in-Sabah-Findings-from-a-Case-Study-of-the-Pan-Borneo-Highway.pdf\nTitle: [PDF] improving outcomes of major infrastructural development in sabah\nSnippet: This report presents key findings from a four-year Humans Habitats. Highways (HHH) initiative that explored ways for improving the outcomes.\nDate: N/A\n\n[50] https://www.bursamalaysia.com/sites/5d809dcf39fba22790cad230/assets/6976c15ecd34aaaa508742ac/Consolidated_Rules_of_Bursa_Malaysia_Derivatives_Bhd_26_January_2026.pdf\nTitle: [PDF] RULES OF BURSA MALAYSIA DERIVATIVES BERHAD TABLE OF ...\nSnippet: These Rules are intended to ensure a fair and open market on the Exchange and to provide protection to the public in its contact with the ...\nDate: Jan 26, 2026\n\n[51] https://www.science.org/doi/10.1126/science.adf9856\nTitle: Tropical forest clearance impacts biodiversity and function ... - Science\nSnippet: Logging affected the environment, but biodiversity and ecosystem functions only showed strong responses to total forest conversion.\nDate: N/A\n\n[52] https://www.researchgate.net/publication/385716365_Social_and_Economic_Aspects_of_The_Scavengers_at_The_Waste_Disposal_Site_in_Sandakan_Sabah_Malaysia\nTitle: (PDF) Social and Economic Aspects of The Scavengers at The ...\nSnippet: The primary aim is to explore the social and economic conditions of scavengers at the Sandakan District Council garbage disposal site, located ...\nDate: Jan 19, 2025\n
\n

</tool_response>

\n

User

\n

<tool_response>

\n
[31] https://media.evendo.com/locations-resized/AttractionImages/1920x466/2ecbe5ea-d83a-4179-bb29-742e885af1d4\nTitle: Discover the Vibrant Sandakan Central Market\nSource: Evendo\nImage: ![image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/multiturn_ci/f1442d12-c8cb-4c50-a4c6-a8a8514bbd56.png)\n\n[32] https://farm9.staticflickr.com/8680/15364687354_8c1fc996a2_z.jpg\nTitle: Guide to food hunting at Sandakan Central Market - Sabah Eats\nSource: Sabah Eats\nImage: ![image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/multiturn_ci/983b88db-5d56-47b6-88b6-21f50dcf238e.png)\n\n[33] https://www.dailyexpress.com.my/uploads/news2/2024/08/Sandakan_130824.jpg\nTitle: Promote Sandakan town centre as tourism package: Member of ...\nSource: Daily Express\nImage: ![image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/multiturn_ci/6142df5c-d68f-40dd-8290-d7d1729d9f45.png)\n\n[34] https://www.mysabah.com/wordpress/wp-content/uploads/2015/10/20151221_12.jpg\nTitle: Sandakan Central Market - MySabah.com\nSource: MySabah.com\nImage: ![image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/multiturn_ci/ed9b10bc-d93b-446f-8325-2e74bffd3d11.png)\n\n[35] https://farm9.staticflickr.com/8598/15363064793_62f021b1ed_z.jpg\nTitle: Guide to food hunting at Sandakan Central Market - Sabah Eats\nSource: Sabah Eats\nImage: ![image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/multiturn_ci/2e580b67-e91b-4628-8ca0-fb6aa3c4056e.png)\n\n[36] https://www.mysabah.com/wordpress/wp-content/uploads/2015/10/20151221_4.jpg\nTitle: Sandakan Central Market - MySabah.com\nSource: MySabah.com\nImage: ![image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/multiturn_ci/a1f43f7f-8923-4a9c-bbba-879e872a8aa2.png)\n\n[37] https://www.theislanddrum.com/wp-content/uploads/2022/10/Central-Market-1-scaled.jpg\nTitle: 11 Cheap Things to Do in Sandakan - The Island Drum\nSource: The Island Drum\nImage: ![image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/multiturn_ci/edc4e2c5-7ec3-4416-9cbe-256edb57cc57.png)\n\n[38] https://www.molon.de/galleries/Malaysia/Sabah/Sandakan/images01/13%20Pasar%20Umum%20Sandakan%20central%20market.jpg\nTitle: Photo of Pasar Umum Sandakan central market. Sandakan, Sabah ...\nSource: Alfred Molon\nImage: ![image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/multiturn_ci/1176d90f-7705-44b0-b44c-102a52074434.png)\n
\n

</tool_response>

\n

Qwen3.6-Plus

\n

<think>

\n

Now I can see the main sign of Sandakan Central Market (Pasar Umum Sandakan). The sign shows:

\n\n

The numbers “2 0 0 6” are written vertically on the right side of the sign. This is from the 2022 image, but the question asks about what was written in 2024.

\n

Let me search for more recent images of the Sandakan Central Market sign from 2024 to confirm whether the “2006” is still there or if it changed.

\n

</think>

\n

<tool_call> <function=web_search> <parameter=queries> [“Pasar Umum Sandakan 2024 sign main entrance”, “Sandakan Central Market 2024 signboard vertical text”] </tool_call> <tool_call> <function=web_search_image> <parameter=queries> [“Pasar Umum Sandakan 2024 sign entrance”, “Sandakan Central Market 2024 main sign”] </tool_call>

\n

User

\n

<tool_response>

\n
[53] https://www.mysabah.com/wordpress/sandakan-central-market/\nTitle: Sandakan Central Market - MySabah.com\nSnippet: Take a stroll in Sandakan Central Market (Pasar Umum Sandakan), after browsing hundreds of stalls with thousands of colourful items on display.\nDate: Dec 29, 2015\n\n[54] https://www.facebook.com/barkahthrift/posts/-pasar-umum-sandakan-sabah-photo-credit-via-fb-lm-soosandakan-sandakanvibes-sdkv/895672639234531/\nTitle: Pasar Umum Sandakan, Sabah Photo credit via FB LM Soo ...\nSnippet: Pasar Umum Sandakan, Sabah Photo credit via FB LM Soo #sandakan #sandakanvibes #sdkvibes #sabah #borneo #malaysia.\nDate: Mar 2, 2024\n\n[55] https://www.instagram.com/explore/locations/903860/pasar-umum-sandakan/\nTitle: Pasar Umum Sandakan! on Instagram • Photos and Videos\nSnippet: See photos and videos taken at this location and explore places nearby.\nDate: N/A\n\n[56] https://yandex.com/maps/org/68039535792/\nTitle: Yandex Maps - Pasar Umum Sandakan!\nSnippet: Market Pasar Umum Sandakan! sandakan, Sandakan, Sabah. Get directions in Yandex Maps.\nDate: N/A\n\n[57] https://www.youtube.com/watch?v=K9rYFYZ6qXE\nTitle: PASAR UMUM SANDAKAN - YouTube\nSnippet: Sign in. This content isn't available. Produk-produk wajib beli untuk orang tersayang sebelum tinggalkan sandakan.. PASAR UMUM SANDAKAN. 71 ...\nDate: Jun 14, 2023\n\n[58] https://www.facebook.com/groups/606307082869043/posts/2882659435233785/\nTitle: Pasar Umum Bandar Sandakan, Sabah, Malaysia. 27.09.24\nSnippet: Pasar Umum Bandar Sandakan, Sabah, Malaysia. 27.09.24.\nDate: Sep 27, 2024\n\n[59] https://www.instagram.com/explore/locations/307548136403337/pasar-umum-sandakan/\nTitle: Pasar Umum Sandakan - Instagram\nSnippet: See photos and videos taken at this location and explore places nearby.\nDate: N/A\n\n[60] https://www.tiktok.com/@ikonhubsabah/video/7569952091284819207\nTitle: Pasar Umum Sandakan: Meriah dan Tradisional - TikTok\nSnippet: Home sweet home Kampung Sim-Sim, Sandakan Sabah ... Sandakan Sabah, Sandakan Little Hong Kong, Galaxy Sandakan, Bandar Sandakan 2024 ...\nDate: Nov 7, 2025\n\n[61] https://www.instagram.com/reel/DQwSWmXjTm1/\nTitle: Sandakan Part 2 (Pasar Umum Sandakan) Tempat ni kalau pagi ...\nSnippet: ... CENTRAL ΜΑ Welcome To SANDAKAN PART2 2 MOSH AHPAGAS EP PASAR UMUM SANDAKAN . Bot BotLaju Laju . 理发店 KEDAI KEDAICUNTING 理发店品 ...\nDate: Nov 7, 2025\n\n[62] https://www.facebook.com/groups/SeeLifeThroughLens/posts/10161690754931113/\nTitle: Pasar Umum Sandakan, Sabah, Malaysia. 12.10.24 - Facebook\nSnippet: Pasar Umum Sandakan, Sabah, Malaysia. 12.10.24.\nDate: Oct 12, 2024\n\n[63] https://strapi.eaza.net/uploads/2025_Manouria_emys_EAZA_Best_Practice_Guidelines_Approved_75d11ba759.pdf\nTitle: [PDF] EAZA Reptile Taxon Advisory Group\nSnippet: An example of educational signage is presented in Appendix 3. Page 26. EAZA Best Practice Guidelines. Asian giant forest tortoise (Manouria emys).\nDate: Dec 4, 2025\n\n[64] https://s3-us-gov-west-1.amazonaws.com/cg-654ebf73-8576-4082-ba73-dd1f1a7fe8dc/uploads/USTDA-Indo-Pacific-Resource-Guide-EDIT_0.pdf\nTitle: [PDF] USTDA Indo Pacific Resources Guide EDIT_0.pdf - Amazon S3\nSnippet: Sabah Oil and Gas Development Corporation Sdn Bhd (SOGDC) is a wholly-owned company of the state of Sabah designated as the vehicle to own, manage and market.\nDate: N/A\n\n[65] https://issuu.com/fbipublicationsmalaysia/docs/palm_oil_mag_jul-sept_2024_e-mag_\nTitle: Asia Palm Oil Magazine Jul-Sept 2024 - Issuu\nSnippet: A WPL rate of 3% is applied to palm oil priced above RM3,000 per tonne in Peninsular Malaysia and above RM3,500 per tonne in Sabah and Sarawak.\nDate: Jul 16, 2024\n\n[66] https://www.apo-tokyo.org/wp-content/uploads/2023/12/APO_RIS-APO-IssuesCh-2023_Book_high.pdf\nTitle: [PDF] Download - Asian Productivity Organization\nSnippet: The Asian Productivity Organization (APO) is an intergovernmental organization that promotes productivity as a key enabler for socioeconomic development and.\nDate: N/A\n\n[67] https://www.jal.com/en/sustainability/report/pdf/index_2024a.pdf\nTitle: [PDF] JAL REPORT 2024\nSnippet: The JAL Group established a Medium-Term Management. Plan FY2021-2025 to realize its Corporate Policy, Purpose, and JAL Vision 2030.\nDate: Aug 1, 2024\n\n[68] https://www.tiktok.com/@blossom_nine/video/7238521589493402882\nTitle: Preparing for Korea Travel: Form Filling Details & Flight Procedures\nSnippet: Do we have to go through a TSA type of baggage screening? 2024-6-22Reply. 0.\nDate: May 29, 2023\n\n[69] https://www.lpicapital.com/downloads/annualreport2023.pdf\nTitle: [PDF] Annual Report 2023 - LPI Capital Bhd\nSnippet: LPI Capital Bhd's (“LPI”) 2023 integrated annual report provides a comprehensive overview of the business activities of LPI and its wholly-owned ...\nDate: Feb 29, 2024\n\n[70] https://www.sirim-qas.com.my/wp-content/uploads/2025/03/PS-Sime-Darby-Plantation-Berhad-SOU-29-Giram-Annual-Surveillance-Audit-No.-4.pdf\nTitle: [PDF] SIME DARBY PLANTATION BERHAD - sirim qas\nSnippet: Total allocation of auditor days for Giram CU were: Mill = 3 days. Mosty Estate and Giram Estate = 3.5 days each for verification of safety and health,.\nDate: N/A\n\n[71] https://www.ijm.com/sites/default/files/annualreport-pdf/arc_ar_2025_0_0.pdf\nTitle: [PDF] Annual Report 2025 - IJM Corporation Berhad\nSnippet: IAR 2025 provides an overview of IJM's performance and key achievements throughout the financial year from. 1 April 2024 to 31 March 2025 ...\nDate: Jul 28, 2025\n\n[72] https://bimp-eaga.asia/sites/default/files/publications/PRF14-IP%20ACT%20BIMP-EAGA%20Vision%202025_WEB_09Oct23.pdf\nTitle: [PDF] BIMP-EAGA - Accelerating Cooperation Together (ACT)\nSnippet: Tuna Park and central public market complex. Phase. 2 is the construction of the baywalk and access roads. Phase 3 is the construction of public rental.\nDate: N/A\n
\n

</tool_response>

\n

User

\n

<tool_response>

\n
[53] https://www.mysabah.com/wordpress/wp-content/uploads/2015/10/20151221_2.jpg\nTitle: Sandakan Central Market - MySabah.com\nSource: MySabah.com\nImage: ![image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/multiturn_ci/c7dec13a-d421-4278-962b-0ceaf0e75004.png)\n\n[54] https://grocerystorenearme.my/wp-content/uploads/2025/09/pasar-umum-sandakan.jpg\nTitle: Pasar Umum Sandakan\nSource: Panduan Lengkap Mencari Grocery Store Near Me di Malaysia\nImage: ![image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/multiturn_ci/18d9edf4-6e00-41b8-a716-36e481671520.png)\n\n[55] https://www.utusanborneo.com.my/sites/default/files/images/article/20220922/twu-ub220922-hs-mpsp2.jpg\nTitle: Projek naik taraf Pasar Umum Sandakan perlu disokong ...\nSource: Utusan Borneo\nImage: ![image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/multiturn_ci/af1aed7c-69c3-44ed-b63c-faa23a4c781f.png)\n\n[56] https://www.mysabah.com/wordpress/wp-content/uploads/2015/10/20151221_1.jpg\nTitle: Sandakan Central Market - MySabah.com\nSource: MySabah.com\nImage: ![image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/multiturn_ci/95e607e4-33e0-4582-9011-84181d7542ed.png)\n\n[57] https://www.molon.de/galleries/Malaysia/Sabah/Sandakan/images01/13%20Pasar%20Umum%20Sandakan%20central%20market.jpg\nTitle: Photo of Pasar Umum Sandakan central market. Sandakan, Sabah ...\nSource: Alfred Molon\nImage: ![image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/multiturn_ci/bef52607-2574-49b1-bbc1-ac1bf308714b.png)\n\n[58] https://sabaheats.com/wp-content/uploads/2014/12/sandakan-central-market.jpg\nTitle: Guide to food hunting at Sandakan Central Market - Sabah Eats\nSource: Sabah Eats\nImage: ![image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/multiturn_ci/d09b1fec-17ba-4462-9b83-43bae79b4077.png)\n\n[59] https://www.theislanddrum.com/wp-content/uploads/2022/10/Central-Market-1-scaled.jpg\nTitle: 11 Cheap Things to Do in Sandakan - The Island Drum\nSource: The Island Drum\nImage: ![image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/multiturn_ci/5d416fae-62ad-478b-9f07-ef51f94d63a3.png)\n\n[60] https://c8.alamy.com/comp/M0CH77/sandakan-central-market-building-M0CH77.jpg\nTitle: Sandakan Central Market Building Stock Photo - Alamy\nSource: Alamy\nImage: ![image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/multiturn_ci/bd9db56c-c55d-4d6a-b4fc-68a7ac7f90f1.png)\n\n[61] https://explorergenes.com/wp-content/uploads/2024/09/Snapseed-3-1024x731.jpeg.webp\nTitle: Some Sublime, Some Gritty in Sandakan City, Malaysia ...\nSource: Explorer Genes\nImage: ![image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/multiturn_ci/171c8245-b523-4556-89a0-c877620b1cb1.png)\n\n[62] https://cache3.travelfish.org/b/assets/2015/gallery/thumbR/gallery_sight_thumbR_623_1758928406.jpg\nTitle: Sandakan Central Market, Sandakan — Visitor guide & review\nSource: Travelfish.org\nImage: ![image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/demo/multiturn_ci/1e4e3766-4e5b-4805-84e5-e0aa1a3cadd5.png)\n
\n

</tool_response>

\n

Qwen3.6-Plus

\n

<think>

\n

I now have a very clear picture of what the Sandakan Central Market (Pasar Umum Sandakan) main sign looks like. Multiple images from different years (2015, 2022, and 2025) consistently show the same sign with “2006” written vertically on the right side.

\n

The sign reads:

\n\n

The “2006” appears to be the year the market building was constructed or renovated.

\n

Multiple sources from 2015 through 2025 all confirm this vertical numbering on the right side of the sign, and the Sandakan Fishing Association is located just southeast of the market on Lebuh Empat.

\n

</think>

\n

Problem-Solving Roadmap

\n
    \n
  1. \n

    Identify the shooting location from the image → The sign clearly reads “SATUAN PERIKANAN SANDAKAN / 山打根渔业公会 / SANDAKAN FISHING ASSOCIATION”, placing the photo at the Sandakan Fishing Association in Sandakan town, Sabah, Malaysia.

    \n
    2. \n
  2. \n

    Locate the nearby shopping plaza → The Sandakan Fishing Association is at Lebuh Empat, Pusat Bandar Sandakan. The bustling Pasar Umum Sandakan (Sandakan Central Market) lies just tens of meters to the northwest of this location.

    \n
    3. \n
  3. \n

    Find the market’s main sign in 2024 → Multiple images from 2015, 2022, and 2025 all show the same unchanged main entrance sign of Pasar Umum Sandakan.

    \n
    4. \n
\n
\n

Answer

\n

The bustling local shopping plaza northwest of the Sandakan Fishing Association is the Pasar Umum Sandakan (Sandakan Central Market).

\n

On the right side of the market’s main sign, written vertically (top to bottom), are the digits:

\n
\n

2 0 0 6

\n
\n

i.e., “2006” — the year the market building was constructed/renovated. The full sign reads:

\n\n

This has been consistently documented in photos from 2015, 2022, and as recently as 2025, confirming it was unchanged in 2024.

\n

Summary & Future Work

\n

Qwen3.6-Plus marks a critical milestone in our journey toward native multimodal agents, delivering an unprecedented leap in agentic coding. By directly addressing real-world developer needs, we have laid a robust and reliable foundation for next-generation AI applications. Building on this momentum, our immediate focus shifts to the full rollout of the Qwen3.6 series. In the coming days, we will also open-source smaller-scale variants, reaffirming our commitment to accessibility and community-driven innovation. Looking further ahead, we will continue pushing the boundaries of model autonomy, targeting increasingly complex, long-horizon repository-level tasks. We are deeply grateful for the invaluable feedback from the Qwen3.5 era and eagerly anticipate the groundbreaking projects you will create with Qwen3.6-Plus.

\n

Citation

\n

Feel free to cite the following article if you find Qwen3.6-Plus helpful:

\n
@misc{qwen36plus,\n    title = {{Qwen3.6-Plus}: Towards Real World Agents},\n    url = {https://qwen.ai/blog?id=qwen3.6},\n    author = {{Qwen Team}},\n    month = {April},\n    year = {2026}\n}\n
\n", "date_published": "2026-04-01T20:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen3.6", "id": "urn:aggr:item:438fe667d1e35adf9557e8e60cd4af92bc9b2a8a", "image": "https://img.alicdn.com/imgextra/i4/O1CN01DNoOHV1Mbz0kbbOsY_!!6000000001454-2-tps-1590-954.png", "summary": "QWEN CHAT DISCORD Following the release of the Qwen3.5 series in February, we are thrilled to announce the official launch of Qwen3.6-Plus. Available immediately via our API, this release represents a massive capability upgrade over its…", "tags": [ "ai", "release" ], "title": "Qwen3.6-Plus: Towards Real World Agents", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-04-01-qwen3-6-plus-towards-real-world-agents/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"Qwen3

\n

QWEN CHAT Hugging Face Offline Demo Hugging Face Realtime Demo ModelScope Offline Demo ModelScope Realtime Demo

\n

Qwen3.5-Omni is Qwen’s latest generation of fully omnimodal LLM, supporting the understanding of text, images, audio, and audio-visual content. Both the Thinker and Talker in Qwen3.5-Omni adopt the Hybrid-Attention MoE. Qwen3.5-Omni series includes Instruct versions in three sizes: Plus, Flash, and Light, with support for 256k long-context input. The model can process more than 10 hours of audio input and over 400 seconds of 720P audio-visual input at 1 FPS. It is natively pretrained in an omnimodal manner on massive amounts of text, visual data, and more than 100 million hours of audio-visual data, demonstrating outstanding full-modality perception and generation capabilities. Compared with Qwen3-Omni, Qwen3.5-Omni offers significantly enhanced multilingual capabilities, supporting speech recognition in 113 languages/dialects and speech generation in 36 languages/dialects. It is currently available via the Offline API and Realtime API.

\n

Offline

\n

Qwen3.5-Omni-Plus has achieved SOTA results on 215 audio and audio-visual understanding, reasoning, and interaction subtasks/benchmarks, covering 3 audio-visual benchmarks, 5 audio benchmarks, 8 ASR benchmarks, 156 language-specific S2TT tasks, and 43 language-specific ASR tasks. In particular, it surpasses Gemini-3.1 Pro across general audio understanding, reasoning, recognition, translation, and dialogue, while its overall audio-visual understanding reaches the level of Gemini-3.1 Pro. Meanwhile, its visual and text capabilities match those of Qwen3.5 models of the same size. One of Qwen3.5-Omni-Plus’s standout features is its audio and audio-visual captioning capability, which can generate controllable, detailed, and structured captions, as well as screenplay-level fine-grained descriptions, including automatic segmentation, timestamp annotation, and detailed descriptions of characters and their relationship to audio. In addition, through native multimodal scaling, we observed the emergence of a new capability in omnimodal models: directly performing coding based on audio-visual instructions, which we call Audio-Visual Vibe Coding; all of the above features are available through the Offline API. We strongly encourage users to read the Demo Section.

\n

Realtime

\n

Beyond its strong base capabilities, we further focused on enhancing the interactive abilities of Qwen3.5-Omni. First, we support semantic interruption by developing native turn-taking intent recognition based on Omni, which avoids interruptions caused by backchanneling and meaningless background noise; this capability is already natively supported in the API. Second, we natively support WebSearch and complex FunctionCall capabilities, enabling the model to autonomously decide whether to invoke WebSearch to respond to users’ real-time questions. Third, we support end-to-end voice control and dialogue, allowing the model to follow instructions like a human and freely control aspects such as speaking volume, speed, and emotion. Fourth, Qwen3.5-Omni supports voice cloning, allowing users to upload a voice to customize the AI Assistant’s voice; all of the above features are available through the Realtime API. Users can also modify the system prompt to change the model’s behavior, such as its conversational style or identity. Fifth, to address speech instability in streaming voice interaction caused by differences in text and speech token encoding efficiency—such as omissions, misreadings, or unclear pronunciation of numbers—we propose ARIA (Adaptive Rate Interleave Alignment), a technique that dynamically aligns text and speech units. While preserving real-time performance, ARIA significantly improves the naturalness and robustness of speech synthesis. We strongly encourage users to read the Demo Section to experience the model’s latest capabilities.

\n

Performance

\n

Audio-Visual Next

\n

\"image\"

\n

Audio Next

\n

\"image\"

\n

Visual Next

\n

\"image\"

\n

Text Next

\n

\"image\"

\n

Speech Generation Next

\n

\"image\"

\n

Below we present the comprehensive evaluation of our models against frontier models in a wide range of evaluation tasks, covering different tasks and modalities.

\n

Audio-Visual

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Gemini-3.1 ProQwen3.5-Omni-FlashQwen3.5-Omni-Plus
Text Query QA
DailyOmni82.781.884.6
WorldSense65.557.962.8
AVUT85.681.485.0
AV-SpeakerBench75.165.271.3
VideoMME (with audio)89.079.383.7
Audio Query QA
QualcommInteractive66.266.368.5
Caption
Omni-Cloze57.263.064.8
Agent (tool use)
OmniGAIA68.933.957.2
\n

* VideoMME (with audio): We evaluate our model with use_audio_in_video=True.
\n* OmniGAIA：We evaluate our model without a thinking prompt and without <answer> formatting. All results are evaluated using deepseek-v3.2-thinking.

\n

Audio

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Gemini-3.1-ProQwen3.5-Omni-FlashQwen3.5-Omni-Plus
Audio Understanding
MMAU81.180.482.2
MMAR83.774.080.0
MMSU81.372.282.8
RUL-MuchoMusic59.660.572.4
SongFormBench-HarmonixSet(acc|hr.5f|hr3f)75.6 | 46.8 | 77.980.6 | 67.8 | 83.481.1 | 72.9 | 85.3
SongFormBench-CN(acc|hr.5f|hr3f)78.1 | 43.2 | 71.986.7 | 66.4 | 84.687.1 | 65.7 | 84.2
Dialogue
VoiceBench88.987.893.1
URO-Bench-Pro(U|R|O)69.1 | 84.0 | 99.264.1 | 83.8 | 98.766.3 | 86.3 | 99.8
SpeechRole124.2119.8123.5
WildSpeech-Bench76.372.275.4
S2TT
Fleursxx⇄zh (top59)29.526.930.2
Fleursxx⇄en (top59)34.632.035.4
Fleursxx⇄zh/en (top59)32.129.432.8
ASR
Fleurs(top60)7.3210.756.55
CV15(zh|yue|zh-tw)8.59 | 13.40 | 6.784.25 | 3.45 | 2.683.46 | 1.95 | 2.27
CV15(en)8.735.904.83
Librispeech(clean|other)3.36 | 4.411.30 | 2.431.11 | 2.23
Wenetspeech(net|meeting)11.53 | 14.214.41 | 5.514.30 | 5.84
Kespeech23.674.473.46
MIR-1K(vocal-only)8.764.944.56
Opencpop6.831.111.49
\n

* SongFormBench: We use a unified prompt defining an SRT-like output timestamp format and a closed vocabulary for evaluation. The vocabulary follows the 'SongForm-HX-8Class' specified in the official codebase.
\n* URO-Bench-Pro: We use the pro track of URO-Bench and denote the three evaluation dimensions as follows: U for Understanding, R for Reasoning, and O for Oral Conversation. We use GenStyle-en, GenStyle-zh, Multilingual tasks for oral dimension.
\n* ASR performance is evaluated using WER/CER, where lower values indicate better performance.
\n* Fleurs: The top59 languages are English, Chinese, Cantonese, Korean, Japanese, Vietnamese, Thai, Malay, German, Russian, Italian, French, Spanish, Portuguese, Dutch, Indonesian, Turkish, Arabic, Polish, Hindi, Urdu, Filipino, Persian, Czech, Greek, Swedish, Hebrew, Danish, Finnish, Norwegian, Icelandic, Bengali, Punjabi, Javanese, Marathi, Swahili, Ukrainian, Gujarati, Kannada, Azerbaijani, Malayalam, Cebuano, Kazakh, Romanian, Hungarian, Bulgarian, Belarusian, Catalan, Tamil, Croatian, Bosnian, Slovak, Galician, Kyrgyz, Macedonian, Slovenian, Latvian, Estonian, and Asturian; compared with the top60 list, Afrikaans is excluded because the Fleurs S2TT test set does not cover this language.
\n* MIR-1K: Transcription is converted into Simplified Chinese.

\n

Visual

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Qwen3.5-Plus-NoThinkingQwen3.5-Omni-FlashQwen3.5-Omni-Plus
STEM and Puzzle
MMMU81.076.980.1
MMMU-Pro73.868.273.9
MathVision73.665.473.0
Mathvista (mini)86.982.986.1
DynaMath84.279.383.8
ZEROBench615
ZEROBench_sub31.126.034.4
General VQA
RealWorldQA79.177.584.1
MMStar80.375.779.4
MMBenchEN-DEV-v1.193.888.892.8
SimpleVQA66.154.465.3
Text Recognition and Document Understanding
CharXiv (RQ)74.264.472.5
CC-OCR83.080.883.4
AI2D_TEST92.189.091.2
MMLongBench-Doc59.753.657.5
OCRBench91.489.191.3
Spatial Intelligence
ERQA53.850.054.8
CountBench95.188.295.1
RefCOCO(avg)95.292.695.0
ODInW1350.346.849.5
EmbSpatialBench83.482.785.4
Video Understanding
VideoMME(w/o sub.)81.077.081.9
MLVU(M-Avg)85.181.986.8
MVBench76.770.879.0
LVBench68.665.771.2
MMVU67.162.767.5
MME-VideoOCR74.270.577.0
Medical VQA
SLAKE82.873.184.7
PMC-VQA62.458.762.7
MedXpertQA-MM55.344.854.7
\n

Text

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Qwen3.5-Plus-NoThinkingQwen3.5-Omni-FlashQwen3.5-Omni-Plus
Knowledge
MMLU-Pro86.879.985.9
MMLU-Redux94.390.094.2
SuperGPQA67.454.966.4
C-Eval92.386.092.0
Instruction Following
IFEval89.785.289.7
IFBench51.138.452.6
Long Context
AA-LCR62.046.057.0
LongBench v260.246.459.6
STEM
GPQA85.976.483.9
Reasoning
LiveCodeBench v667.156.665.6
HMMT Nov 2586.259.084.4
IMOAnswerBench68.351.565.5
General Agent
BFCL-V466.155.363.3
TAU2Bench82.778.081.0
\n

* Qwen3.5-Plus-NoThinking: we use Qwen3.5-Plus-NoThinking as the primary baseline, since all models here are evaluated in the same no-thinking setting.
\n* TAU2-Bench: we follow the official setup except for the airline domain, where all models are evaluated by applying the fixes proposed in the Claude Opus 4.5 system card.

\n

Speech-Generation

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
ElevenLabsGemini-2.5 ProGPT-AudioMinimaxQwen3.5-Omni-Plus
Custom Voice Stability
Seed-zh13.082.421.111.191.07
Seed-en1.171.181.161.351.35
Seed-hard27.7011.578.198.626.24
Public-Multilingual-avg (20 lang)12.622.722.652.162.06
Inhouse-Multilingual-avg (9 lang)20.636.616.7211.715.82
\n

* Stability is measured by Word Error Rate (WER, ↓).
\n* "Public-Multilingual-avg" refers to the average performance on the public TTS-Multilingual-Test-Set, covering 20 languages.
\n* "Inhouse-Multilingual-avg" refers to the average performance on an internal multilingual test set built upon Fleurs, covering 9 languages.
\n* The performance is tested with the following APIs: ElevenLabs-Multilingual-V2 (9YHcvj6GT2YYXdXww), Gemini-2.5 Pro-Preview-TTS (Achernar), GPT-Audio-2025-08-28 (Alloy) and Minimax-Speech-2.8-HD (English_expressive_narrator) in March 2026.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
ElevenLabsMinimaxGround-TruthQwen3.5-Omni-Plus
Voice Clone Stability
Public-Multilingual-avg (20 lang)10.292.52-1.87
Inhouse-Multilingual-avg (9 lang)--9.687.04
Voice Clone Similarity
Public-Multilingual-avg (20 lang)0.650.76-0.79
Inhouse-Multilingual-avg (9 lang)---0.80
\n

* Stability is measured by Word Error Rate (WER, ↓) and similarity is measured by Cosine Similarity (SIM, ↑).
\n* "Public-Multilingual-avg" refers to the average performance on the public TTS-Multilingual-Test-Set, covering 20 languages.
\n* "Inhouse-Multilingual-avg" refers to the average performance on an internal multilingual test set built upon Fleurs, covering 9 languages.
\n* Empty cells (-) indicate scores not yet available or not applicable.

\n

Architecture

\n

Qwen3.5-Omni continues to adopt the Thinker-Talker architecture. The Thinker receives visual and audio signals through the Vision Encoder and AuT, while audio-visual signals are interleaved and encoded with positional information using TMRoPE. The Thinker is responsible for processing omnimodal signals and outputting text, while the Talker receives multimodal inputs and text outputs from the Thinker to perform contextual speech generation. Speech representations are encoded with the RVQ method proposed in Qwen3-Omni, replacing the computationally heavy DiT operations. Thanks to the chunk-wise streaming input design and the streaming Talker design, the entire model supports realtime interaction. Unlike the dual-track Talker input in the previous generation Qwen3-Omni, the Talker adopts ARIA (Adaptive Rate Interleave Alignment) in its input organization to dynamically align text and speech units and then interleave them, thereby avoiding speech instability caused by differences in text and speech token encoding efficiency, such as omissions, misreadings, or unclear pronunciation of numbers.

\n

\"Qwen3

\n

Qwen3.5-Omni vs Qwen3-Omni

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Qwen3-OmniQwen3.5-Omni
BackboneMoEHybrid-MoE
Sequence Length32k256k Audio: 10 hours Audio-Visual (FPS=1): 400 seconds
Captioning CapabilityAudioAudio-Visual
Intelligent Semantic InterruptionNot SupportedSupported
WebSearch/ToolNot SupportedSupported
Voice ControlNot SupportedSupported
Voice CloneNot SupportedSupported
TalkerDual-Track AutoregressionInterleave
Text-Audio Tokenizer RateFixed (1:1)ARIA (Adaptive Rate Interleaved Alignment)
Speech Recognition- 11 Multilingual Languages: Chinese, English, German, French, Italian, Thai, Korean, Japanese, Russian, Spanish, and Portuguese - 8 Chinese Dialects: Sichuanese, Shanghainese, Cantonese, Southern Min, Shaanxi dialect, Nanjing dialect, Tianjin dialect, and Beijing dialect- 74 Multilingual Languages: Afrikaans, Arabic, Asturian, Azerbaijani, Basque, Belarusian, Bengali, Bosnian, Bulgarian, Cantonese, Catalan, Cebuano, Chinese, Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Hebrew, Hindi, Hungarian, Icelandic, Indonesian, Interlingua, Italian, Japanese, Javanese, Kannada, Kazakh, Korean, Kyrgyz, Lingala, Latvian, Lithuanian, Macedonian, Malay, Malayalam, Maltese, Maori, Marathi, Mongolian, Norwegian Bokmål, Norwegian Nynorsk, Oriya, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swahili, Swedish, Tajiki, Tamil, Telugu, Thai, Turkish, Ukrainian, Urdu, Uyghur, and Vietnamese - 39 Chinese Dialects: Northeastern Mandarin, Guizhou dialect, Guangdong Cantonese, Henan dialect, Hong Kong Cantonese, Shanghainese, Shaanxi dialect, Tianjin dialect, Taiwanese Mandarin, Yunnan dialect, Anhui dialect, Fujian dialect, Gansu dialect, Guangdong Mandarin, Hubei dialect, Hunan dialect, Jiangxi dialect, Shandong dialect, Shanxi dialect, Sichuanese, Guangxi dialect, Hainan dialect, Chongqing dialect, Changsha dialect, Hangzhou dialect, Hefei dialect, Yinchuan dialect, Zhengzhou dialect, Shenyang dialect, Wenzhou dialect, Wuhan dialect, Kunming dialect, Taiyuan dialect, Nanchang dialect, Jinan dialect, Lanzhou dialect, Nanjing dialect, Hakka, and Southern Min
Speech Synthesis- 29 Multilingual Languages: Chinese, English, German, Italian, Portuguese, Spanish, Japanese, Korean, French, Russian, Thai, Indonesian, Arabic, Vietnamese, Turkish, Finnish, Polish, Hindi, Dutch, Czech, Urdu, Tagalog, Swedish, Danish, Hebrew, Icelandic, Malay, Norwegian, and Persian - 7 Chinese Dialects: Sichuanese, Beijing dialect, Tianjin dialect, Nanjing dialect, Shaanxi dialect, Cantonese, and Southern Min
\n

Demo

\n

General Audio-Visual Understanding

\n

With audio-visual input, Qwen3.5-Omni-Plus can follow instructions to generate accurate, fine-grained, structured, and timestamped captions for scenarios such as audio-video analysis, shot breakdown, and content moderation.

\n

Documentary: Complex Scenes + Animals + Sound Effects Analysis - Video Studio

\n

Storyline

\n

00:00.000 – 00:02.500
\nThe screen is black, then a deep, low-frequency whoosh swells as the camera drifts in from space toward Earth. The planet’s night-side hemisphere glitters with city lights while the sun crests over its limb, bathing the atmosphere in a brilliant blue halo. A faint orchestral pad begins to build beneath the whoosh.

\n

00:02.500 – 00:36.800
\nA rapid-fire montage of wildlife and natural scenes unfolds, each lasting roughly one second, accompanied by swelling cinematic strings, brass accents, and assorted animal sound effects that punctuate every cut:
\n• High-altitude aerial of cloud-wreathed mountains; wind rush layered under the score.
\n• Lush rainforest canopy shrouded in mist; distant bird calls echo.
\n• Hummingbird hovers at orange blossoms; high-pitched wing buzz audible.
\n• Flock of seagulls wheels across a clear sky; sharp cries pierce the music.
\n• Underwater vortex of schooling fish; muffled aquatic whooshes.
\n• Surfer rides a turquoise wave; splash and surf hiss.
\n• Hammerhead shark cruises above sandy seabed; bubbling ambience.
\n• Brown pelicans glide over muddy water; soft wing beats.
\n• Hippopotamus surfaces, jaws agape; guttural snort.
\n• Brown bear wades through a river; water splashes.
\n• Bald eagle lands on a stump; talon scrape and caw.
\n• Bison herd trots across snowy grassland; heavy hoof thuds.
\n• Iguana flicks forked tongue; faint rustle.
\n• Wildebeest thunder across rolling plains; pounding hooves.
\n• Elephant calf splashes in a watering hole; trumpeting call.
\n• Pronghorn antelope sprint through desert scrub; wind rush.
\n• Cheetah cub pads forward; soft paw taps.
\n• Red fox pounces into tall grass; muted thump.
\n• Lioness yawns widely; resonant growl.
\n• Polar bear roars amid snow; icy wind.
\n• Tiger snarls; fierce roar reverberates.
\nThroughout, the orchestra rises toward a triumphant climax.

\n

00:36.800 – 00:41.000
\nThe view returns to Earth from orbit. White, all-caps text fades in over the globe: “LIFE IN THE ANIMAL KINGDOM.” The music resolves into a sustained chord, then gently subsides.

\n

00:41.000 – 00:44.000
\nAgainst the rotating Earth, the single word “Royalty” appears in white serif type at left, lingering briefly before dissolving. Ambient synth tones replace the earlier orchestral swell.

\n

00:44.000 – 00:49.000
\nScreen cuts to black. Large block letters spelling “LION” materialize; each glyph is filled with moving close-ups of lion fur, eyes, and muzzle. A crystalline chime rings out, followed by a deep bass note. As the letters fade, an extreme close-up of a male lion’s face fills the frame. Flies crawl near its nose while it blinks languidly. In the lower-right corner, small white text reads “Panthera leo.” Night insects chirp softly beneath a subdued musical bed.

\n

00:49.000 – 01:04.000
\nNarration begins in a calm, mid-range male voice with a neutral British accent: “Lions, majestic creatures known for their regal appearance and powerful presence, have long captivated our imagination.” Visuals alternate between the resting male lion on green grass—shaking its mane—and a lioness weaving through dense foliage. Gentle strings and light percussion underscore the narration; cicadas hum in the background.

\n

01:04.000 – 01:24.000
\nAs the narrator explains lions’ adaptability to savannas, grasslands, woodlands, and semi-deserts, footage shows a lioness striding through tall grass at dusk beneath a pink-tinged sky, then a tight shot of another lioness panting lightly. Music grows more rhythmic; distant lion roars blend with the score.

\n

01:24.000 – 01:40.000
\nWhile the narrator notes lions’ distribution across sub-Saharan Africa and India’s Gir Forest, the image cuts to a CGI Earth rotating to center on Africa. Dozens of glowing red dots bloom across the continent, marking populations. Orchestral strings surge, then taper.

\n

01:40.000 – 01:54.000
\nBack on the ground, an extreme close-up captures a male lion’s amber eye blinking slowly. The narrator describes the species’ robust body, broad head, prominent male mane, and tawny coat. Cut to a male lion lying in dry grass, meticulously licking its forepaw; subtle licking sounds mix with soft ambient music.

\n

01:54.000 – 02:11.000
\nA lioness stands half-hidden in golden savanna grass, scanning the horizon where a distant herd grazes. The narrator remarks that the fur’s coloration provides effective camouflage. Wind rustles through the grass; the score remains gentle and observational.

\n

02:11.000 – 02:32.000
\nGolden-hour light bathes a male lion with a dark, full mane as he walks purposefully through mixed green and dry brush. The narrator explains that manes vary in color and size, signify maturity, and aid in attracting mates. Music introduces brighter melodic phrases; occasional bird calls are heard.

\n

02:32.000 – 02:40.000
\nOn a reddish dirt track flanked by sparse vegetation, a majestic male lion strides toward camera while several small birds flutter around his paws. Warm sunset hues dominate the palette; the orchestral bed maintains a steady, dignified rhythm.

\n

02:40.000 – 02:51.000
\nA lioness leads a procession of at least eight playful cubs through lush grass dotted with trees. The narrator states that lions are social animals living in prides of related females and offspring. Cubs scamper, tumble, and glance curiously at the lens. Light, uplifting strings accompany the scene; faint cub mews are audible.

\n

02:51.000 – 03:00.000
\nFinal tableau: two adult male lions recline side by side on sandy ground amid dry shrubs under a pale sky. One gazes ahead; the other rests its chin on its paws, occasionally blinking. The narrator has finished; only soft ambient music and distant insect chirps remain. The image holds, then gently fades to silence and black.

\n

Visible Text

\n

00:37.000 – 00:40.000
\n“LIFE IN THE ANIMAL KINGDOM”: white, clean sans-serif capitals; centered horizontally, slightly above mid-frame; superimposed over the orbital Earth; fades in and out smoothly.

\n

00:41.000 – 00:43.000
\n“Royalty”: white serif word; positioned left-center over the rotating Earth; static during its brief appearance, then fades.

\n

00:44.000 – 00:48.000
\n“LION”: very large bold sans-serif capitals filling most of the frame against black; each letter contains animated close-ups of lion facial features; no outline; appears via quick fade-in, holds, then dissolves.

\n

00:52.000 – 00:56.000
\n“Panthera leo”: small white sans-serif text; lower-right corner of an extreme close-up of a male lion’s face; fades in and out without movement.

\n

(No additional textual elements appear outside these intervals.)

\n

Speakers and Transcript

\n

Speaker profiles:
\nNarrator – Adult male, neutral British English accent, warm baritone timbre, measured pacing, informative and documentary tone.

\n

00:56.796 – 01:04.716
\nSpeaker: Narrator
\nState: Calm, authoritative; medium volume over soft ambient music and insect ambience.
\nContent: “Lions, majestic creatures known for their regal appearance and powerful presence, have long captivated our imagination.”

\n

01:14.316 – 01:23.916
\nSpeaker: Narrator
\nState: Steady, explanatory; slight emphasis on key terms; orchestral underscore rising gently.
\nContent: “They are highly adaptable animals and can be found in a variety of habitats, ranging from savannas and grasslands to woodlands and semi-desert areas.”

\n

01:31.724 – 01:38.444
\nSpeaker: Narrator
\nState: Informative, neutral; music momentarily subdued to foreground speech.
\nContent: “They are spread across Sub-Saharan Africa, and a small population lives in the Gir Forest of India.”

\n

01:43.404 – 01:53.804
\nSpeaker: Narrator
\nState: Descriptive, slightly emphatic on physical traits; strings swell behind voice.
\nContent: “Lions are characterized by their distinctive appearance, including a robust body, a broad head with a prominent mane in males, and a sleek tawny coat.”

\n

02:05.644 – 02:10.604
\nSpeaker: Narrator
\nState: Matter-of-fact; softer musical bed, light wind noise underneath.
\nContent: “The coloration of their fur serves as effective camouflage in their natural environment.”

\n

02:18.508 – 02:24.188
\nSpeaker: Narrator
\nState: Engaged, mildly enthusiastic; brighter musical motif begins.
\nContent: “Male lions are easily recognized by their impressive manes, which vary in color and size.”

\n

02:27.788 – 02:32.108
\nSpeaker: Narrator
\nState: Continues seamlessly; slight crescendo in score.
\nContent: “The mane is a sign of maturity and plays a role in attracting mates.”

\n

02:42.764 – 02:50.444
\nSpeaker: Narrator
\nState: Concluding, warm; uplifting strings accompany visuals of cubs.
\nContent: “They are social animals and are often found in groups known as prides, typically consisting of related females and their offspring.”

\n

(There are no other human speakers; all remaining audible elements are music, animal sounds, or environmental ambience.)

\n

Additional Notes on Audio Design

\n
    \n
  1. Music: Predominantly orchestral with cinematic scope—strings, brass, and percussion—modulating in intensity to match visual pacing. It begins with a dramatic swell during the montage, recedes to ambient textures under narration, and ends with a gentle, reflective cadence.
    2. \n
  2. Sound Effects: Carefully synchronized animal calls (roars, chirps, splashes) punctuate corresponding visuals, enhancing realism without overpowering narration.
    3. \n
  3. Mixing: Narration is consistently foregrounded; music ducks subtly whenever speech occurs, ensuring clarity. Environmental sounds are balanced to add depth yet remain secondary.
    4. \n
\n

Thematic and Cultural Context

\n

The piece functions as a concise nature-documentary vignette introducing lions as emblematic “royalty” of the animal kingdom. By juxtaposing a global montage of diverse species with focused lion imagery and authoritative narration, it situates lions within broader ecological and cultural narratives of majesty, adaptation, and social structure. The use of orbital Earth shots and scientific nomenclature (“Panthera leo”) underscores a modern, educational intent, while the sweeping score evokes awe and reverence typical of contemporary wildlife filmmaking.

\n

Film: Multi-Character, Multi-Shot Audio-Visual Analysis with Complex Sound Effects

\n

Storyline

\n

00:00.000 – 00:04.796
\nA flat, dark-green screen fills the frame. Centered white capital letters present the Motion Picture Association of America’s standard preview-approval notice. Two smaller white website addresses sit along the lower edge. No music is heard yet; the soundtrack is silent, giving the text full attention.

\n

00:04.796 – 00:06.465
\nThe green card cuts to black. A single, deep orchestral hit with metallic overtones blooms, establishing a tense, cinematic atmosphere.

\n

00:06.465 – 00:10.219
\nNighttime aerial footage of a sprawling metropolis glides past. Skyscrapers glitter with gold, blue, and red lights; the camera slowly dollies toward one illuminated tower. A gravelly male voice, close-miked and intimate, murmurs, “Come in close.” The orchestral bed swells beneath his words.

\n

00:10.219 – 00:17.768
\nThe scene snaps to the interior of a packed theatre. From a high angle, four performers—three men and one woman—stand on a glossy black circular stage ringed by white neon. A spotlight picks out a man in a dark suit and fedora who tips his hat toward the crowd. The camera cuts to the audience: an elderly Black man in a fedora watches intently. Back on stage, the female performer in a short black dress stands inside a glowing yellow rectangular frame while the fedora-wearing man gestures theatrically. The narrator continues, “Because the more you think you see… the easier it’ll be to fool you.” A sharp percussive sting punctuates the last phrase. A close-up shows a young man with tousled hair flipping a playing card; the Ace of Hearts materialises.

\n

00:17.768 – 00:21.188
\nA blue lens flare sweeps across black, revealing the silver Summit Entertainment logo with its stylised mountain peak. The orchestral score surges. The view then cuts to a sweeping night shot of the Las Vegas Strip, dominated by the gold-lit Paris Las Vegas Eiffel Tower and neon signs for “BALLY’S,” “MIRAGE,” and “THE LINQ.”

\n

00:21.188 – 00:30.113
\nInside the theatre again, the four magicians—now in coordinated dark suits—stride onto the stage. The audience roars. A blue tarp is whipped away to unveil a transparent, steel-framed teleportation booth. A digital clock on the booth’s side reads “0:00.” The lead magician in a pale suit announces, “Ladies and gentlemen, for our final trick, we are going to rob a bank. On the count of three, you will be teleported through space and time to your bank in Paris.” The crowd gasps. He counts, “One, two, three!” A bassy whoosh and crackling energy sound accompany the booth’s blue flash.

\n

00:30.113 – 00:37.955
\nCut to Paris: the façade of the opulent “CRÉDIT RÉPUBLICAIN” bank at night. Inside its vault, the four magicians—now in formal evening wear—stand before a massive circular door. Back in the theatre, the female magician addresses the audience: “Everyone in this room was a victim of hard times. Some of you lost your homes, your cars. And so tonight, we’re gonna return some of that money back to you.” A torrent of euro banknotes erupts from the stage, fluttering over cheering spectators who reach up to catch the cash.

\n

00:37.955 – 00:46.255
\nThe magicians bow amid the falling money. The lead man proclaims, “Thank you, everyone. We are the Four Horsemen. Good night!” The audience erupts in applause. The scene shifts to a marble-floored bank interior where stern men in suits, led by an older white-haired gentleman, descend a grand staircase. He declares, “Your bank was the distraction while they set up the real trick. I was a $140 million distraction.” His voice drips with smug satisfaction.

\n

00:46.255 – 00:55.514
\nA dim bar: the older Black man in a fedora confers with the white-haired banker, exchanging knowing glances. In a bright, sterile vault corridor, a technician in a white jumpsuit wheels carts of cash. The fedora-wearing elder muses, “Who doesn’t love a good magic trick?” A metallic crash and a shout of “FBI!” cut to a modern office where an agent yells, “Hands where I can see ’em!”

\n

00:55.514 – 01:03.397
\nOn the Las Vegas Strip, a suited man with a phone asks incredulously, “Did you say magicians robbed a bank?” A split-screen montage shows each Horseman in separate interrogation rooms, coolly toying with cards or handcuffs. One smirks, “You have what we in the business like to call ‘nothing up your sleeve.’” The camera lingers on his confident grin.

\n

01:03.397 – 01:12.614
\nThe interrogation intensifies. The same magician leans forward, voice low but taunting: “Because if you did, it means that you and the FBI and your friends at Interpol actually believe in magic.” A sudden slam of his cuffed hands on the table startles the interrogator. He concludes, “First rule of magic: always be the smartest guy in the room.” A quick montage flashes: a fedora tips, the Horsemen bow on stage, and the older Black man whispers, “Wanna know how they did it? Say the magic word.”

\n

01:12.614 – 01:24.001
\nA female agent in a car remarks, “A year ago, these guys were a bunch of street magicians.” Cut to the Horsemen performing atop a skyscraper against a glittering cityscape. She continues, “Now they’re pulling off amazing robberies and not keeping a single cent for themselves.” The older Black man, now in a tuxedo, intones, “You do realize this is a game played out on a global scale.”

\n

01:24.001 – 01:34.469
\nScenes intercut rapidly: a white-jumpsuited trio approaches a vault; an armored truck in a parking garage explodes, spilling money; the Horsemen study a holographic blueprint of a vault; the female agent warns, “We are dealing with something far bigger than us.” The white-haired banker growls, “Expose them now and destroy them.”

\n

01:34.469 – 01:45.605
\nAction escalates: a sleek private jet soars above clouds; a red sports car rockets across the Eiffel Tower’s iron lattice; a man in a dark room brandishes a card marked “THE TOWER / LA MAISON DIEU.” The fedora-wearing elder states, “Vegas was just a start. This trick was designed a long time ago.”

\n

01:45.605 – 02:00.203
\nA woman with reddish-brown hair pleads, “We’re all here for the same reason.” The elder Black man counters grimly, “We cannot quit now.” A suited gunman levels a pistol in a sparse room. On stage, one Horseman is yanked upward into a blinding spotlight, vanishing. The orchestral score reaches a thunderous peak.

\n

02:00.203 – 02:11.006
\nThe elder Black man’s voice overlays frenetic images: “Whatever is about to follow, whatever this grand trick is… it’s really going to amaze.” A red car bursts through the roof of a graffiti-covered building, showering the night sky with cash. The audience, including the fedora-wearing elder and a blonde woman, stare upward, mouths agape.

\n

02:11.006 – 02:17.554
\nThe narrator delivers the final caution: “Look closely, because the closer you think you are, the less you’ll actually see.” The screen cuts to black, then a brilliant blue lens flare reveals the metallic title “NOW YOU SEE ME,” its letters gleaming with chrome reflections.

\n

02:17.554 – 02:24.603
\nAgainst a dark backdrop, bold white text announces “MAY 31,” followed by the Facebook logo and “NowYouSeeMeMovie,” the hashtag “#NowYouSeeMe,” and a small copyright line. The music resolves with a last resonant chord, then silence as the image fades to black.

\n

Visible Text

\n

00:00.000 – 00:04.796
\n“THE FOLLOWING PREVIEW HAS BEEN APPROVED FOR” – white uppercase sans-serif, centered on dark-green background
\n“APPROPRIATE AUDIENCES” – larger, bold white uppercase, centered
\n“BY THE MOTION PICTURE ASSOCIATION OF AMERICA, INC.” – white uppercase, centered
\n“www.filmratings.com” – small white lowercase, bottom left
\n“www.mpaa.org” – small white lowercase, bottom right

\n

00:17.768 – 00:19.500
\n“SUMMIT ENTERTAINMENT” – silver uppercase serif beneath stylised mountain logo, centre screen
\n“A LIONSGATE COMPANY” – smaller silver uppercase, centred below main logo

\n

00:19.500 – 00:21.188
\n“BALLY’S” – large red neon letters on hotel façade, upper left of frame
\n“MIRAGE” – white neon letters on adjacent building, mid-left
\n“THE LINQ” – white neon letters on right-side building

\n

00:21.188 – 00:30.113
\n“0:00” – red seven-segment digits on small black display affixed to teleportation booth, stage right

\n

00:28.000 – 00:30.000
\n“CRÉDIT RÉPUBLICAIN” – gold serif capitals on stone bank façade, Paris night scene

\n

01:40.000 – 01:41.500
\n“THE TOWER” – black uppercase at top of tarot-style card
\n“LA MAISON DIEU” – black uppercase at bottom of same card

\n

02:17.554 – 02:19.000
\n“NOW YOU SEE ME” – large metallic silver 3-D letters with blue lens-flare glow, centred on black

\n

02:20.000 – 02:22.000
\n“MAY 31” – large white uppercase, centred
\nFacebook “f” logo followed by “NowYouSeeMeMovie” – white, centred below date
\n“#NowYouSeeMe” – white, centred below social line
\n“© 2013 SUMMIT ENTERTAINMENT, LLC. ALL RIGHTS RESERVED.” – very small white uppercase, bottom centre
\nLionsgate stylised “L” logo – small white, bottom right

\n

Speakers and Transcript

\n

Speaker profiles:
\nNarrator – older male, deep gravelly American voice, measured, ominous tone
\nLead Magician – mid-30s male, clear mid-range American accent, showman confidence, energetic delivery
\nFemale Magician – late-20s female, bright American accent, persuasive, enthusiastic tone
\nOlder Banker – late-60s male, refined American accent, authoritative, smug
\nFedora Elder – late-60s Black male, resonant baritone, calm, philosophical
\nInterrogator – 40s male, firm American accent, forceful, impatient
\nStreet-Magician Interrogated – early-30s male, relaxed American accent, sardonic, playful
\nFemale Agent – 30s female, steady American accent, analytical, concerned

\n

00:08.364 – 00:09.004
\nSpeaker: Narrator
\nState: low volume, intimate, ominous
\nContent: “Come in close.”

\n

00:10.364 – 00:17.724
\nSpeaker: Narrator
\nState: slow, cautionary, gravelly
\nContent: “Because the more you think you see, the easier it’ll be to fool you.”

\n

00:19.644 – 00:23.004
\nSpeaker: Lead Magician
\nState: projected, theatrical excitement
\nContent: “Ladies and gentlemen, for our final trick, we are going to rob a bank.”

\n

00:23.884 – 00:29.484
\nSpeaker: Lead Magician
\nState: ringing announcement, rising cadence
\nContent: “On the count of three, you will be teleported through space and time to your bank in Paris.”

\n

00:29.724 – 00:31.244
\nSpeaker: Lead Magician
\nState: loud, rhythmic countdown
\nContent: “One, two, three.”

\n

00:31.244 – 00:36.444
\nSpeaker: Female Magician
\nState: earnest, compassionate
\nContent: “Everyone in this room was a victim of hard times.”

\n

00:36.604 – 00:40.764
\nSpeaker: Female Magician
\nState: rallying, hopeful
\nContent: “Some of you lost your homes, your cars, and so tonight, we’re gonna return some of that money back to you.”

\n

00:44.044 – 00:44.684
\nSpeaker: Lead Magician
\nState: grateful, upbeat
\nContent: “Thank you, everyone.”

\n

00:44.844 – 00:46.284
\nSpeaker: Lead Magician
\nState: triumphant proclamation
\nContent: “We are the Four Horsemen.”

\n

00:46.284 – 00:47.004
\nSpeaker: Lead Magician
\nState: cheerful sign-off
\nContent: “Good night.”

\n

00:48.780 – 00:52.860
\nSpeaker: Older Banker
\nState: controlled, explanatory
\nContent: “Your bank was the distraction while they set up the real trick.”

\n

00:53.100 – 00:57.260
\nSpeaker: Older Banker
\nState: boastful, self-satisfied
\nContent: “I was a hundred and forty million dollar distraction.”

\n

00:57.260 – 01:00.140
\nSpeaker: Fedora Elder
\nState: amused, reflective
\nContent: “Who doesn’t love a good magic trick?”

\n

01:01.100 – 01:02.540
\nSpeaker: Interrogator
\nState: loud command, tense
\nContent: “FBI! Hands where I can see ’em.”

\n

01:02.940 – 01:04.140
\nSpeaker: Street-Magician Interrogated
\nState: sarcastic disbelief
\nContent: “I don’t think I heard you correctly.”

\n

01:04.220 – 01:05.900
\nSpeaker: Street-Magician Interrogated
\nState: incredulous, taunting
\nContent: “Did you say magicians robbed a bank?”

\n

01:06.060 – 01:07.260
\nSpeaker: Street-Magician Interrogated
\nState: smug, challenging
\nContent: “You are going to be played.”

\n

01:07.260 – 01:10.540
\nSpeaker: Street-Magician Interrogated
\nState: lecturing, playful
\nContent: “You have what we in the business like to call nothing up your sleeve.”

\n

01:10.700 – 01:15.340
\nSpeaker: Street-Magician Interrogated
\nState: mocking, confident
\nContent: “Because if you did, it means that you and the FBI and your friends at Interpol actually believe in magic.”

\n

01:18.380 – 01:19.660
\nSpeaker: Street-Magician Interrogated
\nState: didactic, crisp
\nContent: “First rule of magic, always be the smartest guy in the room.”

\n

01:23.660 – 01:24.540
\nSpeaker: Fedora Elder
\nState: teasing, conspiratorial
\nContent: “Wanna know how they did it?”

\n

01:24.780 – 01:25.740
\nSpeaker: Fedora Elder
\nState: inviting, playful
\nContent: “Say the magic word.”

\n

01:26.700 – 01:28.940
\nSpeaker: Female Agent
\nState: analytical, matter-of-fact
\nContent: “A year ago, these guys were a bunch of street magicians.”

\n

01:31.180 – 01:34.860
\nSpeaker: Female Agent
\nState: impressed, wary
\nContent: “Now they’re pulling off amazing robberies and not keeping a single cent for themselves.”

\n

01:35.980 – 01:40.300
\nSpeaker: Fedora Elder
\nState: grave, explanatory
\nContent: “You do realize this is a game played out on a global scale.”

\n

01:40.780 – 01:41.900
\nSpeaker: Fedora Elder
\nState: ominous, foreboding
\nContent: “Vegas was just a start.”

\n

01:42.460 – 01:45.260
\nSpeaker: Fedora Elder
\nState: reflective, portentous
\nContent: “This trick was designed a long time ago.”

\n

01:46.220 – 01:48.460
\nSpeaker: Female Agent
\nState: concerned, urgent
\nContent: “We are dealing with something far bigger than us.”

\n

01:49.100 – 01:50.540
\nSpeaker: Red-haired Woman
\nState: resolute, earnest
\nContent: “We’re all here for the same reason.”

\n

01:50.620 – 01:51.980
\nSpeaker: Fedora Elder
\nState: determined, forceful
\nContent: “We cannot quit now.”

\n

01:53.660 – 01:56.060
\nSpeaker: Older Banker
\nState: cold, commanding
\nContent: “Expose them now and destroy them.”

\n

02:01.036 – 02:09.196
\nSpeaker: Fedora Elder
\nState: anticipatory, grandiose
\nContent: “Whatever is about to follow, whatever this grand trick is, is really going to amaze.”

\n

02:11.116 – 02:17.356
\nSpeaker: Narrator
\nState: slow, cautionary, resonant
\nContent: “Look closely, because the closer you think you are, the less you’ll actually see.”

\n

Additional Notes on Cinematic Style and Themes

\n
    \n
  1. Visual Aesthetics: The trailer juxtaposes sleek, high-contrast stage performances bathed in blue and gold lighting with gritty urban nightscapes and sterile government interiors, reinforcing the duality of spectacle versus clandestine operations.
    2. \n
  2. Motifs: Recurrent images of playing cards, vault doors, falling money, and iconic landmarks (Eiffel Tower, Las Vegas Strip) underscore themes of illusion, wealth redistribution, and global stakes.
    3. \n
  3. Sound Design: A hybrid orchestral-electronic score drives momentum, punctuated by metallic impacts, whooshes, and crowd roars that synchronize tightly with visual reveals, enhancing the sense of grand illusion.
    4. \n
  4. Narrative Arc: The trailer establishes the Four Horsemen as charismatic magician-thieves who expose corruption by stealing from the wealthy and gifting money to the public, while law enforcement and powerful financiers scramble to unmask them, setting up a cat-and-mouse game on an international scale.
    5. \n
\n

Game: Determine Whether Violent Scenes Are Present Based on Audio-Visual Content

\n

Demo

\n

User

\n
Please provide a detailed description of the video. It must explicitly include two specific analytical sections to identify content that is inappropriate for minors.\n\n### Section 1: Compliance Alert (Summary)\nProvide a table summarizing all flagged segments for quick review:\n| Time Range | Category | Risk Level | Justification |\n| :--- | :--- | :--- | :--- |\n| xx:xx - xx:xx | e.g., Violence | High | e.g., Realistic physical assault observed. |\n\n### Section 2: Summary of Safety Findings\nProvide a final assessment on whether the video is suitable for minors, citing the most critical timestamps and explaining the overall risk profile. Apply a zero-tolerance policy; if there is ambiguity regarding whether a scene is harmful, err on the side of caution.\n\n---\n\n## Output Format:\n\n```markdown\n## Compliance Alert (Summary)\n\n| Time Range | Category | Risk Level | Justification |\n| :--- | :--- | :--- | :--- |\n\n## Summary of Safety Findings\n\n<Paragraphs detailing the safety assessment.>\n
\n

Qwen3.5-Omni-Plus

\n

This video is a compilation of gameplay from Mortal Kombat 11 Ultimate, focusing on the game’s special finishing moves called Fatalities. It begins by showing the main menu, where the player selects the Fatalities mode. They then choose a character from the roster, such as Shang Tsung, Shao Kahn, or The Joker, and enter a one-on-one fight. After winning the match, the player performs a unique, graphic finishing move on their defeated opponent. Each Fatality is a cinematic sequence of extreme violence, showing characters being torn apart, decapitated, frozen and shattered, or disintegrated. The video cycles through several different characters, each demonstrating their own distinct and brutal method of killing their opponent. The presentation is stylized with dark, dramatic lighting and slow-motion effects to highlight the gore. The video concludes after showcasing a series of these Fatalities.

\n

Section 1: Compliance Alert (Summary)

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Time RangeCategoryRisk LevelJustification
01:25 - 01:49Graphic ViolenceHighA character is impaled, set on fire, and then ripped in half vertically, showing internal organs and a skeleton.
02:26 - 02:47Graphic ViolenceHighA character is impaled through the chest, lifted into the air, and then ripped apart at the waist, causing a massive spray of blood.
03:15 - 03:38Graphic ViolenceHighA character is frozen solid with ice powers and then shattered into many pieces.
04:13 - 04:37Graphic ViolenceHighA character’s soul is ripped out of their body and then violently torn apart by a spiritual entity.
05:03 - 05:29Graphic ViolenceHighA character is blown up by an explosive cake and then decapitated by a spinning blade.
05:56 - 06:24Graphic ViolenceHighA character is punched so hard their head explodes, and their body is then split in half.
06:53 - 07:19Graphic ViolenceHighA character is launched into the air and shredded by helicopter rotor blades, resulting in dismemberment.
\n

Section 2: Summary of Safety Findings

\n

This video is not suitable for minors. The entire video consists of gameplay from Mortal Kombat 11 Ultimate, a game rated for mature audiences due to its extreme content. The video showcases multiple Fatalities, which are graphic and sadistic finishing moves. These scenes depict realistic and brutal violence, including dismemberment, decapitation, evisceration, and characters being set on fire or shattered. The high level of blood and gore, combined with the detailed and cinematic nature of the violence, makes the content inappropriate for anyone under the age of 18.

\n

Short Video: Daily Life Short Video Analysis

\n

Storyline

\n

00:00.000 – 00:04.300
\nThe clip opens with a hand-held, wide-angle selfie shot. A Black man in his twenties, sporting short curly hair and a bright magenta-yellow-red striped long-sleeve top, fringed magenta skirt, matching leg warmers, and large fluffy magenta ankle attachments, fills most of the frame. Behind him stretches a sun-bleached, sandy clearing under a blue sky mottled with white clouds. Dozens of onlookers—men, women, and children in casual clothes—form a loose semicircle, many holding up smartphones. Farther back, several drummers stand beside tall, cylindrical drums. The performer shouts excitedly toward the lens, “I’m here in Ivory Coast and I’m about to do the hardest dance in the world. Let’s go!” His voice is loud, breathy, and exuberant. As he finishes, a dense wall of polyrhythmic hand-drumming erupts, accompanied by scattered cheers and whoops from the crowd.

\n

00:04.300 – 00:15.000
\nThe camera swings outward into a medium-wide view that now frames two principal dancers. On the left is the striped-shirt host; on the right stands a masked dancer whose face is painted solid green beneath an ornate headdress topped with red-green-white feathers. This second figure wears a flowing cape and skirt in vertical bands of green, white, and orange, plus striped leg wraps and shaggy brown ankle rattles that jingle with every step. Both men launch into vigorous footwork—rapid stamping, hopping, and spinning—sending puffs of dust into the air. The drum ensemble behind them pounds out layered rhythms while the audience claps and yells encouragement. Around 00:09 a male spectator near the camera calls out, “Why?” in playful disbelief. At roughly 00:12 the masked dancer drops into a low squat, nearly touching the ground, then springs back up, prompting louder applause.

\n

00:15.000 – 00:18.000
\nThe host abruptly pivots the phone back to himself for another close-up. Breathing hard, sweat glistening on his forehead, he gasps, “It is very hard. It’s even hard for me.” His grin mixes exhaustion with exhilaration. Drumbeats continue underneath, slightly muffled by his proximity to the microphone.

\n

00:18.000 – 00:26.000
\nThe viewpoint widens again. The two lead dancers re-engage, circling each other. The masked performer brandishes a short wooden stick, slashing it through the air in time with the drums, while the host mirrors some of the steps but struggles to keep pace, occasionally stumbling and laughing at himself. Dust swirls around their feet; sunlight flashes off metallic ornaments on the costume. The crowd’s energy rises—shouts, whistles, and rhythmic clapping blend with the relentless percussion.

\n

00:26.000 – 00:31.000
\nIn a climactic flourish, the masked dancer spins rapidly, cape flaring like a multicolored wheel. He closes the distance and enfolds the host in a brief celebratory hug. The host throws his head back, mouth open in a joyous shout, eyes squeezed shut. Camera shake intensifies as both men laugh and sway together amid thunderous drumming and roaring approval from spectators.

\n

00:31.000 – 00:32.000
\nStill locked in the embrace, the host leans toward the lens and bellows, “W’s in the chat! Like and sub!” His tone is triumphant and teasing, aimed at an online audience. Immediately after the final word, the image freezes for a split second and the video cuts to black, ending the clip.

\n

Visible Text

\n

No visible text, captions, graphics, or on-screen typography appear at any point in the video.

\n

Speakers and Transcript

\n

Speaker profiles:
\nHost – Male, mid-20s, Black, African-American English accent; voice loud, energetic, occasionally breathless; primary on-camera performer in striped outfit.
\nSpectator – Unseen male voice from crowd; casual tone, brief interjection.

\n

00:00.000 – 00:04.240
\nSpeaker: Host
\nState: Shouting, highly enthusiastic, projecting over ambient noise
\nContent: “I’m here in Ivory Coast and I’m about to do the hardest dance in the world. Let’s go!”

\n

00:09.000 – 00:10.000
\nSpeaker: Spectator
\nState: Playful exclamation, raised voice
\nContent: “Why?”

\n

00:15.200 – 00:17.600
\nSpeaker: Host
\nState: Breathless, strained yet amused
\nContent: “It is very hard. It’s even hard for me.”

\n

00:31.000 – 00:32.000
\nSpeaker: Host
\nState: Triumphant shout, promotional tone
\nContent: “W’s in the chat! Like and sub!”

\n

Additional Notes on Audio-Visual Style

\n

• Music: Continuous live West African drumming featuring multiple djembe-style drums producing interlocking high, mid, and low tones; tempo fast and steady, no melodic instruments detected.
\n• Ambient sound: Frequent crowd cheers, claps, whistles; occasional individual shouts; no discernible wind or traffic noise, indicating an outdoor but relatively sheltered setting.
\n• Cinematography: Entirely handheld smartphone footage; frequent rapid pans between selfie close-ups and wider shots; slight fisheye distortion suggests a wide-angle lens attachment.
\n• Lighting: Bright natural daylight with strong overhead sun casting short shadows; colors appear saturated, enhancing the vividness of costumes and surroundings.
\n• Cultural context: Costumes, masks, and drumming strongly evoke traditional Ivorian ceremonial dance forms such as those associated with the Goli or Zaouli traditions, though the presence of social-media slang (“W’s in the chat,” “Like and sub”) indicates a contemporary, internet-savvy performance intended for online sharing.

\n

Recommended Usage

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
ConfigurationMaximum PixelsRecommended Video DurationRecommended PromptScenario
Low Cost230,400 PixelsUnder 60 MinutesA custom prompt within 50 words, depending on the needs.- Moderation
Brief (Low Accuracy Requirement)921,600–2,073,600 PixelsUnder 60 MinutesA custom prompt within 50 words, depending on the needs.- Long-Video Segmentation - Rough Content Extraction
Balanced (General Detailed Audio-Visual Description)921,600–2,073,600 PixelsUnder 4 MinutesFixed Structured-Description Prompt- Fine-Grained Audio-Visual Tagging.
Best (The Most Detailed Description)2,073,600 PixelsUnder 2 MinutesFixed Structured-Description Prompt- Multi-Scene, - Multi-Speaker - Complex Scenarios
Note: If you want structured, fine-grained descriptions for longer video with audio as well, segmenting is recommended.
\n

Fixed Structured-Description Prompt

\n
Provide a detailed description of the video.\n\nIt should explicitly include three sections: \n\n1. A structured chronological storyline of **every noticeable audio and visual details**\n2. A structured list of all visible text. For each text element, include start timestamp, end timestamp, the exact text content, the appearance characteristics. If no text appears, explicitly state so.\n3. A structured speech-to-text transcription, include speaker（Corresponding to the character or voice‑over in Section 1, including their accent and tone）, exact spoken content, start timestamp, end timestamp, and speaking state (prosody, emotion, and style). If no speech appears, explicitly state so.\n\nAside from these three required sections, you are free to organize any additional content in any way you find helpful. This additional content can include global information about the entire video or localized information about specific moments. You may choose the topic of this extra content freely.\n\nOutput Format:\n\n```\n## Storyline\n\n<xx:xx.xxx> - <xx:xx.xxx>\n<an unstructured long paragraph in natural language describing what happened during this period, blending both audio and video details.>\n\n<xx:xx.xxx> - <xx:xx.xxx>\n<an unstructured long paragraph in natural language describing what happened during this period, blending both audio and video details.>\n\n<xx:xx.xxx> - <xx:xx.xxx>\n<an unstructured long paragraph in natural language describing what happened during this period, blending both audio and video details.>\n\n...\n\n## Visible Text\n\n<xx:xx.xxx> - <xx:xx.xxx>\n“<element>”: <appearance>\n“<element>”: <appearance>\n\n<xx:xx.xxx> - <xx:xx.xxx>\n“<element>”: <appearance>\n“<element>”: <appearance>\n“<element>”: <appearance>\n\n<xx:xx.xxx> - <xx:xx.xxx>\n“<element>”: <appearance>\n\n...\n\n## Speakers and Transcript\n\nSpeaker profiles:\n<speaker> - <profile>\n<speaker> - <profile>\n<speaker> - <profile>\n...\n\n<xx:xx.xxx> - <xx:xx.xxx>\nSpeaker: <speaker>\nState: <description>\nContent: “<content>”\n\n<xx:xx.xxx> - <xx:xx.xxx>\nSpeaker: <speaker>\nState: <description>\nContent: “<content>”\n\n<xx:xx.xxx> - <xx:xx.xxx>\nSpeaker: <speaker>\nState: <description>\nContent: “<content>”\n\n...\n\n## <another section>\n\n<paragraphs>\n\n## <another section>\n\n<paragraphs>\n\n...\n```\n
\n

Audio-Visual Vibe Coding

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Snake Game

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Demo

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User

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Qwen3.5-Omni-Plus

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User

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Qwen3.5-Omni-Plus

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Demo2Code

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Demo

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User

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Qwen3.5-Omni-Plus

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User

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Qwen3.5-Omni-Plus

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Audio-Visual Conversation

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Travel Planning – Weather/Hotel Lookup, WebSearch/Tool

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Demo

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Voice Clone

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Demo

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Multi-Turn Dialogue and Intelligent Interruption

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Demo

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Voice Style, Emotion, and Volume Control in Conversation

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Demo

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General Audio Understanding

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Multi-Language Song Understanding

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Demo

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User

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Please analyze this multilingual song segment by segment along the timeline, and output the start and end times, complete original lyrics, singer's gender, vocal characteristics, language type, emotional tone, and performance style (solo/chorus) for each segment.\n
\n

Qwen3.5-Omni-Plus

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[00:00.000 – 00:05.440]

\n

Instrumental introduction; no lyrics present. The orchestra establishes a solemn, anticipatory atmosphere with sustained strings and distant percussion. No vocals are heard yet, so the mood is tense and expectant.

\n

[00:05.440 – 00:16.320]

\n

Solo male tenor (clear, bright, slightly nasal timbre) sings in English with a firm, declamatory delivery. Emotional tone is urgent and rallying, evoking defiance and hope. Style is solo, front-and-center over restrained orchestral support.

\n

Lyrics: “Do you hear the people sing? Singing the song of angry men? It is the music of a people who will not be slaves again.”

\n

[00:16.320 – 00:28.080]

\n

Same solo tenor continues in English, voice gaining intensity and forward projection. Emotional tone shifts to passionate resolve. Solo performance remains dominant, with strings swelling beneath.

\n

Lyrics: “When the beating of your heart echoes the beating of the drum, there is a life about to start when tomorrow comes.”

\n

[00:28.080 – 00:33.600]

\n

Baritone male voice enters in French, resonant and weighty, projecting solidarity. Emotional tone is dignified and communal. Solo delivery, supported by low brass undercurrents.

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Lyrics: “À la volonté du peuple et à la santé du progrès.”

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[00:33.600 – 00:39.280]

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Tenor male voice in German, ringing and forceful, sustaining long vowels for emphasis. Tone is fervent and insistent. Solo style, with orchestral punctuation.

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Lyrics: “Das ist die Symphonie von Menschen, die nicht länger Sklaven sind.”

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[00:39.280 – 00:50.800]

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Baritone male voice in Japanese, warm and measured, delivering lines with rhythmic clarity. Emotional tone is determined yet reflective. Solo performance, strings providing gentle momentum.

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Lyrics: “新たに熱い命が始まる。明日が来た時、そうさ、明日。”

\n

[00:50.800 – 00:56.720]

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Tenor male voice in Hungarian, agile and impassioned, slight vibrato adding urgency. Tone is exhortative. Solo style, light woodwind coloration behind.

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Lyrics: “El sem ellik, hogyha kell, kiállsz e értünk, harcunkért.”

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[00:56.720 – 01:07.360]

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Baritone male voice in Swedish, rich and rounded, projecting calm strength. Emotional tone is resolute and inclusive. Solo delivery, brass subtly reinforcing cadence.

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Lyrics: “Från vår barrikad kan man se ett framtidens land. Så kom med oss, låt oss kämpa om du kan.”

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[01:07.360 – 01:12.880]

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Tenor male voice in Polish, bright and piercing, conveying indignation. Tone is sharp and motivating. Solo performance, percussion accents underline key words.

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Lyrics: “Pytasz, słuchasz, śpiewasz lud, co nie chce żyć w niewoli znów.”

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[01:12.880 – 01:18.800]

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Baritone male voice in Dutch, sonorous and steady, imparting gravitas. Emotional tone is earnest and collective. Solo style, lower strings provide foundation.

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Lyrics: “Al die mensen die verdommen om nog langer slaaf te zijn.”

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[01:18.800 – 01:29.920]

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Return to English solo tenor, now fuller and more expansive, voice soaring above thicker orchestration. Tone is triumphant anticipation. Solo performance leading toward ensemble build-up.

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Lyrics: “When the beating of your heart echoes the beating of the drums, there is a life about to start when tomorrow comes.”

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[01:29.920 – 01:35.440]

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Tenor male voice in German, powerful and ringing, emphasizing finality. Emotional tone is climactic and commanding. Solo delivery, cymbal crashes accent phrases.

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Lyrics: “Wenn du kämpfst mit ganzer Kraft, hat bald ein Ende alle Not.”

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[01:35.440 – 01:40.480]

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English solo tenor resumes, voice urgent and questioning, slight edge of challenge. Tone is provocative, urging action. Solo style, dynamic rise in accompaniment.

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Lyrics: “Some will fall and some will live. Will you stand up and take your chance?”

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[01:40.480 – 01:45.840]

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English solo tenor continues, voice imbued with patriotic fervor, broad phrasing. Emotional tone is inspirational. Solo performance, brass fanfare hints at coming chorus.

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Lyrics: “The blood of the martyrs will water the meadows of France.”

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[01:45.840 – 01:51.440]

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Tenor male voice in Danish, clear and lyrical, gentle vibrato. Tone is hopeful and forward-looking. Solo delivery, harp arpeggios shimmer beneath.

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Lyrics: “Kan du høre folkesangen? Det er håp om morgendagen.”

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[01:51.440 – 01:57.120]

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Tenor male voice in Czech, robust and declamatory, strong consonants. Emotional tone is defiant. Solo style, timpani rolls add tension.

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Lyrics: “Nechť ani šíp nespalí víru, kdo zpívá píseň svobody.”

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[01:57.120 – 02:02.560]

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Tenor male voice in Finnish, resonant and solemn, measured pacing. Tone is steadfast and noble. Solo performance, low strings sustain harmonic bed.

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Lyrics: “Jotka kuuntelevat uuslaulun sävelten jälkeen.”

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[02:02.560 – 02:08.080]

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Tenor male voice in Danish, lyrical and uplifting, smooth legato. Emotional tone is optimistic. Solo delivery, flutes echo melodic fragments.

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Lyrics: “Der skal de leve og leve i den nye dag.”

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[02:08.080 – 02:13.680]

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English solo tenor returns, voice expansive and inviting, slight crescendo toward phrase end. Tone is inclusive and stirring. Solo style, full orchestra begins to swell.

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Lyrics: “Will you join in our crusade? Who will be strong and stand with me?”

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[02:13.680 – 02:19.280]

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Tenor male voice in Danish/Norwegian, bright and encouraging, forward placement. Emotional tone is motivational. Solo performance, snare drum march rhythm emerges.

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Lyrics: “Kom dig en fremtid, vær dig selv og fri, som aldrig vil jeg mere.”

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[02:19.280 – 02:30.640]

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English solo tenor, voice at peak intensity, sustained high notes, rich vibrato. Tone is exultant and prophetic. Solo lead, orchestra builds to forte.

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Lyrics: “Do you hear the people sing? Say, do you hear the distant drums? It is the future that we bring when tomorrow comes.”

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[02:30.640 – 02:43.520]

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Transition from solo to mixed choir; multiple male and female voices blend in homophonic texture. Language shifts among previously heard tongues, creating a polyglot tapestry. Emotional tone is ecstatic unity. Chorus style, full orchestral tutti, cymbals and brass fanfares.

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Lyrics: “One day more!”

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[02:43.520 – 02:49.120]

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Choir sustains “One day more!” in layered harmonies, dynamics climbing. Tone is jubilant and anthemic. Chorus performance, tempo accelerates slightly.

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Lyrics: “One day more!”

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[02:49.120 – 02:54.640]

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Choir repeats refrain with heightened volume; soprano line soars above. Emotional tone is triumphant. Chorus style, percussion drives momentum.

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Lyrics: “One day more!”

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[02:54.640 – 03:00.240]

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Choir continues, voices interweaving in counterpoint, building to climax. Tone is euphoric resolve. Full chorus, brass and timpani reinforce downbeats.

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Lyrics: “One day more!”

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[03:00.240 – 03:05.760]

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Choir reaches fortissimo peak, holding final chord with radiant resonance. Emotional tone is victorious culmination. Chorus performance, cymbal crash and organ pedal point underline grandeur.

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Lyrics: “One day more!”

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[03:05.760 – 03:41.027]

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Extended choral coda; repeated iterations of “One day more!” in overlapping entries, gradually decrescendoing into a luminous final chord. Emotional tone transitions from ecstatic to serene resolution. Full chorus with orchestra tapering to silence, leaving a lingering sense of hope.

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Lyrics: “One day more! One day more! One day more! One day more!”

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BBC Audio Understanding

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Storyline

\n

00:00.000 – 00:06.800
\nInside a reverberant television-studio control room, two young production assistants—one woman, one man—argue in agitated, fast voices about having waited “over twenty minutes” to go on-air. The woman’s tone is high-pitched and panicked; the man’s is clipped and frustrated. A low, steady HVAC hum underlies the exchange. A brief paper rustle and a single cough from somewhere off-mic punctuate the tension.

\n

00:06.800 – 00:11.500
\nThe argument escalates: the man calls the situation “incredibly unprofessional,” the woman fires back “So is your mom!” and immediately apologizes, admitting she is “very stressed out.” Their voices overlap slightly. A sharp, comic “whoosh” stinger and a burst of canned laughter announce that the feed is about to go live.

\n

00:11.500 – 00:18.600
\nStill backstage, the woman snaps, “Oh, okay, they’re coming back to us. Get through the whole broadcast in five minutes. Speed it up!” The man protests, “How’s that even possible?” She groans, “I’m gonna throw up,” and, after a metallic clank, he mutters, “No, there’s no time.” A triumphant orchestral news-theme sting swells, blending with audience laughter as the program officially begins.

\n

00:18.600 – 00:25.800
\nThe music segues into the main theme. A resonant male announcer booms, “Live, this is Channel 8 News at Ten with John Harper and Sandra Burbank.” The theme fades. In the studio, Anchor John Harper greets viewers in a polished baritone: “Good evening and welcome to World News at Ten. I’m John Harper.” Anchor Sandra Burbank follows crisply: “And I’m Sandra Burbank.”

\n

00:25.800 – 00:36.300
\nSandra introduces the top story about “four and eight in the Middle East,” then tosses to correspondent Philip Castle “live on the scene.” Philip, speaking through a slightly compressed remote line, tersely confirms the tension. Sandra presses, “Any chance for a quick resolution?” Philip answers “No.” Sandra instantly thanks him. Audience laughter erupts, then subsides.

\n

00:36.300 – 00:45.000
\nJohn pivots to a tease about “clean energy,” promising more later, and throws to weather. A perky male weathercaster flatly states, “Clouds.” John immediately yanks the broadcast back: “We now go back to our story on clean energy.” Laughter and a quick stinger accent the gag.

\n

00:45.000 – 00:59.900
\nJohn introduces “local expert Dr. Simon Gunther.” After a brief “My pleasure,” John rapid-fires questions about solar power and fossil-fuel dependence. The doctor’s hesitant “Well… yeah, actually…” is cut off mid-sentence. John snaps, “Thank you, doctor,” triggering another wave of audience laughter.

\n

00:59.900 – 01:06.100
\nSandra delivers a morbid one-liner: “And now for a story about a non-profit animal shelter that caught fire. Everything died. Hmm.” The studio audience roars; a short news sting follows.

\n

01:06.100 – 01:15.100
\nJohn introduces Diane Dubanowski’s “exhaustive story on immigration.” A calm female voice begins, “The borders between countries are—” but John cuts in with “Food for thought.” Fresh laughter and a brisk stinger.

\n

01:15.100 – 01:25.000
\nSandra presents the “weekly segment on the state of the nation” and asks political analyst Craig Jones about America’s biggest challenge. Craig starts, “Obama is—” only to be silenced by Sandra’s “Thank you, Craig.” The audience responds with its loudest laugh yet, followed by light applause.

\n

01:25.000 – 01:39.400
\nA field reporter, Joseph Jensen, crackles in via satellite: “The aliens are about to make their first contact.” Sandra, unfazed, replies, “Fascinating, Joseph, thank you.” Laughter and a short stinger.

\n

01:39.400 – 01:48.900
\nJohn hands off to “Steve with sports.” Steve, sounding bewildered, utters a single “What?” John thanks him and the audience, invites them to “stay tuned for Fallon,” and reminds them to watch the morning news at six. Sandra adds, “Have a good night, everyone.” The closing theme swells; applause and cheers fill the room.

\n

01:48.900 – 01:54.900
\nThe theme reaches full orchestral grandeur, then fades under enthusiastic clapping. As the music dies, the ambience shifts back to the control room’s drier acoustics.

\n

01:54.900 – 01:59.998
\nThe female production assistant, now calmer but still hurried, tells her colleague, “So that was too fast. You have four more minutes, so just stretch it out.” A final audience chuckle and a faint exhale close the recording.

\n

Speakers and Transcript

\n

Speaker profiles:
\nFemale PA – Young adult woman, standard American accent; high-pitched, anxious, rapid speech.
\nMale PA – Young adult man, standard American accent; tense, clipped delivery.
\nAnnouncer – Middle-aged man, deep “news voice,” authoritative.
\nJohn Harper – Male anchor, mid-40s, resonant baritone, polished broadcast cadence.
\nSandra Burbank – Female anchor, early-40s, bright mezzo, precise diction.
\nPhilip Castle – Male correspondent, 30s, slightly nasal, remote-line compression.
\nWeathercaster – Male, 30s, casual tone, very brief.
\nDr. Simon Gunther – Male, 50s, hesitant academic tone.
\nDiane Dubanowski – Female reporter, 30s, measured, professional.
\nCraig Jones – Male analyst, 40s, confident, political pundit style.
\nJoseph Jensen – Male field reporter, 30s, excited, satellite line.
\nSteve – Male sports anchor, 30s, puzzled tone.
\nAudience – Mixed-gender studio crowd; laughter, applause, cheers.

\n

00:00.160 – 00:00.960
\nSpeaker: Female PA
\nState: strained, urgent, rising pitch
\nContent: “How much longer?”

\n

00:00.960 – 00:02.880
\nSpeaker: Male PA
\nState: frustrated, quick tempo
\nContent: “We’ve been waiting to go on for over twenty minutes.”

\n

00:03.120 – 00:04.800
\nSpeaker: Female PA
\nState: exasperated, fast
\nContent: “I know, the game went into triple overtime.”

\n

00:04.800 – 00:06.160
\nSpeaker: Female PA
\nState: annoyed, clipped
\nContent: “They’re eating up our time slot.”

\n

00:06.240 – 00:07.680
\nSpeaker: Male PA
\nState: indignant, emphatic
\nContent: “Well, this is incredibly unprofessional.”

\n

00:07.680 – 00:08.320
\nSpeaker: Female PA
\nState: mocking, sharp
\nContent: “So is your mom.”

\n

00:08.320 – 00:08.800
\nSpeaker: Male PA
\nState: shocked, abrupt
\nContent: “What?”

\n

00:08.800 – 00:10.400
\nSpeaker: Female PA
\nState: flustered, apologetic
\nContent: “I’m sorry, I’m very stressed out.”

\n

00:11.200 – 00:12.640
\nSpeaker: Female PA
\nState: suddenly focused, brisk
\nContent: “Oh, okay, they’re coming back to us.”

\n

00:12.800 – 00:14.320
\nSpeaker: Female PA
\nState: commanding, hurried
\nContent: “Get through the whole broadcast in five minutes.”

\n

00:14.400 – 00:14.880
\nSpeaker: Female PA
\nState: urgent, loud
\nContent: “Speed it up.”

\n

00:14.880 – 00:15.200
\nSpeaker: Male PA
\nState: incredulous, raised voice
\nContent: “How is that even possible?”

\n

00:15.200 – 00:16.800
\nSpeaker: Female PA
\nState: nauseated, groaning
\nContent: “I’m gonna throw up.”

\n

00:16.800 – 00:17.680
\nSpeaker: Male PA
\nState: tense, clipped
\nContent: “No, there’s no time.”

\n

00:18.572 – 00:22.972
\nSpeaker: Announcer
\nState: booming, ceremonious
\nContent: “Live, this is Channel 8 News at 10 with John Harper and Sandra Burbank.”

\n

00:23.932 – 00:25.292
\nSpeaker: John Harper
\nState: warm, formal
\nContent: “Good evening and welcome to World News at 10.”

\n

00:25.292 – 00:25.772
\nSpeaker: John Harper
\nState: steady, confident
\nContent: “I’m John Harper.”

\n

00:25.852 – 00:26.652
\nSpeaker: Sandra Burbank
\nState: bright, professional
\nContent: “And I’m Sandra Burbank.”

\n

00:26.652 – 00:29.292
\nSpeaker: Sandra Burbank
\nState: explanatory, brisk
\nContent: “Our top story tonight, four and eight in the Middle East has some people up in arms.”

\n

00:29.292 – 00:31.212
\nSpeaker: Sandra Burbank
\nState: transitional, clear
\nContent: “We go now to our correspondent Philip Castle, who is live on the scene.”

\n

00:31.212 – 00:34.092
\nSpeaker: Sandra Burbank
\nState: inquisitive, formal
\nContent: “Philip, things seem to be rather tense regarding this issue.”

\n

00:34.252 – 00:34.492
\nSpeaker: Philip Castle
\nState: terse, factual
\nContent: “Yes.”

\n

00:34.652 – 00:35.772
\nSpeaker: Sandra Burbank
\nState: probing, quick
\nContent: “Any chance for a quick resolution?”

\n

00:36.092 – 00:36.332
\nSpeaker: Philip Castle
\nState: firm, abrupt
\nContent: “No.”

\n

00:36.492 – 00:36.812
\nSpeaker: Sandra Burbank
\nState: clipped, polite
\nContent: “Thank you, Philip.”

\n

00:36.812 – 00:42.092
\nSpeaker: John Harper
\nState: smooth, announcer-like
\nContent: “Recent developments in clean energy have some people asking questions, but more on that later in the program.”

\n

00:42.092 – 00:44.252
\nSpeaker: John Harper
\nState: upbeat, segueing
\nContent: “Let’s first check in with the weather.”

\n

00:44.492 – 00:44.812
\nSpeaker: Weathercaster
\nState: flat, deadpan
\nContent: “Clouds.”

\n

00:45.052 – 00:46.732
\nSpeaker: John Harper
\nState: brisk, redirecting
\nContent: “We now go back to our story on clean energy.”

\n

00:47.532 – 00:49.212
\nSpeaker: John Harper
\nState: cordial, introducing
\nContent: “We go now to local expert Dr. Simon Gunther.”

\n

00:49.212 – 00:50.092
\nSpeaker: John Harper
\nState: polite
\nContent: “Thank you for joining us, doctor.”

\n

00:50.572 – 00:50.892
\nSpeaker: Dr. Simon Gunther
\nState: courteous
\nContent: “My pleasure.”

\n

00:50.892 – 00:51.692
\nSpeaker: John Harper
\nState: eager, rapid
\nContent: “Let’s get right to it, shall we?”

\n

00:51.692 – 00:54.332
\nSpeaker: John Harper
\nState: enthusiastic, quick
\nContent: “Solar power can run cars, homes, even entire cities.”

\n

00:54.332 – 00:54.412
\nSpeaker: John Harper
\nState: confirming
\nContent: “Is that correct?”

\n

00:54.732 – 00:56.812
\nSpeaker: John Harper
\nState: pressing, fast
\nContent: “And I understand this technology has been around for some time?”

\n

00:57.052 – 00:57.452
\nSpeaker: Dr. Simon Gunther
\nState: tentative
\nContent: “Yeah, actually—”

\n

00:57.452 – 00:58.892
\nSpeaker: John Harper
\nState: challenging, rapid
\nContent: “So why are we still dependent on gas and oil?”

\n

00:59.292 – 00:59.852
\nSpeaker: John Harper
\nState: dismissive
\nContent: “Thank you, doctor.”

\n

01:01.692 – 01:04.332
\nSpeaker: Sandra Burbank
\nState: somber, matter-of-fact
\nContent: “And now for a story about a non-profit animal shelter that caught fire.”

\n

01:04.332 – 01:04.972
\nSpeaker: Sandra Burbank
\nState: flat, grim
\nContent: “Everything died.”

\n

01:05.292 – 01:05.692
\nSpeaker: Sandra Burbank
\nState: pensive hum
\nContent: “Hmm.”

\n

01:06.172 – 01:12.172
\nSpeaker: John Harper
\nState: formal, introducing
\nContent: “We now go to a special report by Diane Dubanowski, who has spent the last six months preparing an exhaustive story on immigration.”

\n

01:12.492 – 01:14.252
\nSpeaker: Diane Dubanowski
\nState: measured, serious
\nContent: “The borders between countries are—”

\n

01:14.412 – 01:14.972
\nSpeaker: John Harper
\nState: cutting in, wry
\nContent: “Food for thought.”

\n

01:17.068 – 01:21.388
\nSpeaker: Sandra Burbank
\nState: upbeat, introducing
\nContent: “And now for our weekly segment on the state of the nation.”

\n

01:21.388 – 01:23.788
\nSpeaker: Sandra Burbank
\nState: cordial
\nContent: “We have, as always, political analyst Craig Jones.”

\n

01:24.028 – 01:24.108
\nSpeaker: Sandra Burbank
\nState: inquisitive
\nContent: “Craig,”

\n

01:24.108 – 01:24.748
\nSpeaker: Sandra Burbank
\nState: direct
\nContent: “what is the biggest challenge facing America today?”

\n

01:24.108 – 01:24.908
\nSpeaker: Craig Jones
\nState: declarative, cut short
\nContent: “Obama is—”

\n

01:24.908 – 01:25.228
\nSpeaker: Sandra Burbank
\nState: abrupt, polite
\nContent: “Thank you, Craig.”

\n

01:32.748 – 01:38.348
\nSpeaker: Joseph Jensen
\nState: excited, remote-line buzz
\nContent: “The aliens are about to make their first contact.”

\n

01:38.348 – 01:39.388
\nSpeaker: Sandra Burbank
\nState: dry, dismissive
\nContent: “Fascinating, Joseph, thank you.”

\n

01:40.268 – 01:41.708
\nSpeaker: John Harper
\nState: brisk, segueing
\nContent: “Let’s go now to Steve with sports.”

\n

01:42.188 – 01:42.508
\nSpeaker: Steve
\nState: confused
\nContent: “What?”

\n

01:42.828 – 01:44.988
\nSpeaker: John Harper
\nState: cheerful, closing
\nContent: “Thank you, Steve, and thank you all for joining us here tonight.”

\n

01:45.388 – 01:48.268
\nSpeaker: John Harper
\nState: promotional, upbeat
\nContent: “Stay tuned for Fallon and be sure to check out the morning news team tomorrow morning at six.”

\n

01:48.348 – 01:49.148
\nSpeaker: Sandra Burbank
\nState: warm sign-off
\nContent: “Have a good night, everyone.”

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01:54.988 – 01:56.108
\nSpeaker: Female PA
\nState: relieved, conversational
\nContent: “So that was too fast.”

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01:56.748 – 01:59.948
\nSpeaker: Female PA
\nState: instructive, calming
\nContent: “You have four more minutes, so just stretch it out.”

\n

Global Acoustic & Production Notes

\n
    \n
  1. Studio acoustics shift noticeably between the dry control-room segments and the reverberant main studio, underscoring the narrative’s backstage-versus-on-air structure.
    2. \n
  2. Non-speech elements—orchestral news themes, comic stingers, whooshes, paper rustles, and audience reactions—are tightly synchronized with dialogue to heighten comedic timing.
    3. \n
  3. The entire piece is a parody of live news broadcasts, lampooning time constraints, sensationalism, and abrupt cut-offs. Rapid pacing, overlapping lines, and sudden topic shifts create a satirical, farcical rhythm.
    4. \n
  4. Audience responses (laughter, applause, cheers) function as a rhythmic punctuation, often triggered immediately after punchlines or abrupt dismissals, reinforcing the sketch-comedy format.
    5. \n
  5. Dynamic range is moderate: dialogue sits prominently above a consistent low-level HVAC hum; musical stings and theme swell briefly to near-equal loudness before ducking under speech.
    6. \n
  6. No music beds persist beneath dialogue except during the opening and closing themes; otherwise, silence or faint room tone separates segments, allowing jokes to land cleanly.
    7. \n
\n

Thematic & Cultural Context

\n

The audio parodies American local-news conventions: hyperbritical anchors, token experts, trivial weather updates, and sensational headlines. References to “Fallon” suggest a late-night talk-show lead-in, situating the piece within U.S. broadcast culture. The absurdity of compressing an entire newscast into minutes, coupled with cut-off interviews and darkly comic one-liners (e.g., “Everything died”), satirizes the rush for brevity and the superficiality of modern news cycles.

\n

Lyric Subtitle Generation

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Demo

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User

\n
Please identify the lyrics of this song and provide the timestamps line by line. The format example is as follows:\n[00:00:15,020 --> 00:00:28,085] When you walk through a storm, hold your head up high.\n[00:00:28,085 --> 00:00:40,200] And don't be afraid of the dark.\n[00:00:40,200 --> 00:01:02:580] At the end of the storm, there's a golden sky.\n...\n\nDo not output any other content except for the above mentioned.\n
\n

Qwen3.5-Omni-Plus

\n

[00:00:20,686 –> 00:00:24,996] Clink of crystal, smoke in the air

\n

[00:00:24,996 –> 00:00:29,506] Smiles like porcelain, nobody’s there

\n

[00:00:29,506 –> 00:00:34,266] Tie knotted tight, shoes polished black

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[00:00:34,266 –> 00:00:38,436] Laugh on cue, never look back

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[00:00:38,436 –> 00:00:43,266] Champagne flutes rise like hollow toasts

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[00:00:43,266 –> 00:00:47,796] To dreams we sold for designer ghosts

\n

[00:00:47,796 –> 00:00:52,906] The band plays sweet, but I hear it wrong

\n

[00:00:52,906 –> 00:00:56,806] A syncopated sense of gone

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[00:00:56,806 –> 00:01:01,436] I’m dancing offbeat in a perfect show

\n

[00:01:01,436 –> 00:01:06,226] Smiling wide while the silence grows

\n

[00:01:06,226 –> 00:01:11,126] They call it charm, I call it strange

\n

[00:01:11,126 –> 00:01:15,066] This masquerade where nothing changes

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[00:01:15,066 –> 00:01:23,106] Yeah, I play along, but I don’t belong

\n

[00:01:23,106 –> 00:01:28,966] Her perfume lingers, words like lace

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[00:01:28,966 –> 00:01:33,066] But every glance hides an empty space

\n

[00:01:33,066 –> 00:01:37,166] We quote philosophers over ice

\n

[00:01:37,166 –> 00:01:42,086] While truth dissolves in cocktail vice

\n

[00:01:42,086 –> 00:01:47,106] The sax hums low, but I feel it scream

\n

[00:01:47,106 –> 00:01:51,586] A dissonant thread in this velvet dream

\n

[00:01:51,586 –> 00:01:56,266] I’m dancing offbeat in a perfect show

\n

[00:01:56,266 –> 00:02:01,066] Smiling wide while the silence grows

\n

[00:02:01,066 –> 00:02:05,926] They call it charm, I call it strange

\n

[00:02:05,926 –> 00:02:09,866] This masquerade where nothing changes

\n

[00:02:09,866 –> 00:02:18,866] Yeah, I play along, but I don’t belong

\n

[00:02:18,866 –> 00:02:29,986] What if I drop the glass, let it shatter on marble lights

\n

[00:02:29,986 –> 00:02:46,666] Would they notice the crack in me, or just pour another round and sigh

\n

[00:02:46,666 –> 00:03:00,546] Lights still glitter, music sways

\n

[00:03:00,546 –> 00:03:05,146] I toast the void behind my face

\n

[00:03:05,146 –> 00:03:10,466] Another night, another role

\n

[00:03:10,466 –> 00:03:15,006] Jazz hands hiding a broken soul

\n

[00:03:15,006 –> 00:03:23,000] (Instrumental fade out)

\n

Recommended Usage

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
ConfigurationRecommended Audio DurationRecommended Prompt UsageScenario
Low CostUnder 60 MinutesA custom prompt within 50 words, depending on the needs.- Moderation
Brief (Low Accuracy Requirement)Under 60 MinutesA custom prompt within 50 words, depending on the needs.- Long-Audio Segmentation - Rough Content Extraction
Balanced (General Audio Detailed Description)Under 2 MinutesFixed Structured-Description Prompt- Fine-Grained Audio Tagging
Best (The Most Detailed Description)Under 1 MinutesFixed Structured-Description Prompt- Complex Acoustic Scenes - Multi-Speaker
Note: If you want structured, fine-grained descriptions for longer audio as well, we recommend segmenting it first.
\n

Fixed Structured-Description Prompt

\n
Provide a detailed description of the audio.\n\nIt should explicitly include two sections: \n\n1. A structured chronological storyline of **every noticeable audio details**\n2. A structured speech-to-text transcription, include speaker（Corresponding to the character or voice‑over in Section 1, including their accent and tone）, exact spoken content, start timestamp, end timestamp, and speaking state (prosody, emotion, and style). If no speech appears, explicitly state so.\n\nAside from these two required components, you are free to organize any additional content in any way you find helpful. This additional content can include global information about the entire audio or localized information about specific moments. You may choose the topic of this extra content freely.\n\nOutput Format:\n\n```\n## Storyline\n\n<xx:xx.xxx> - <xx:xx.xxx>\n<an unstructured long paragraph in natural language describing what happened during this period, blending both audio details.>\n\n<xx:xx.xxx> - <xx:xx.xxx>\n<an unstructured long paragraph in natural language describing what happened during this period, blending both audio details.>\n\n<xx:xx.xxx> - <xx:xx.xxx>\n<an unstructured long paragraph in natural language describing what happened during this period, blending both audio details.>\n\n...\n\n...\n\n## Speakers and Transcript\n\nSpeaker profiles:\n<speaker> - <profile>\n<speaker> - <profile>\n<speaker> - <profile>\n...\n\n<xx:xx.xxx> - <xx:xx.xxx>\nSpeaker: <speaker>\nState: <description>\nContent: “<content>”\n\n<xx:xx.xxx> - <xx:xx.xxx>\nSpeaker: <speaker>\nState: <description>\nContent: “<content>”\n\n<xx:xx.xxx> - <xx:xx.xxx>\nSpeaker: <speaker>\nState: <description>\nContent: “<content>”\n\n...\n\n## <another section>\n\n<paragraphs>\n\n## <another section>\n\n<paragraphs>\n\n...\n```\n
\n

Play with Qwen3.5-Omni

\n

Chat with Qwen3.5-Omni

\n

Feel free to use Qwen3.5 on Qwen Chat.

\n

ModelStudio

\n

Users can also access our flagship model, Qwen3.5-Plus-Omni, through Alibaba Cloud Model Studio. Two invocation modes are currently supported: Offline Invocation and Realtime Invocation.

\n

To enable web search, simply pass the following parameter:

\n\n

Offline Invocation

\n

Example code is provided below:

\n
# Preparation before running:\n# Run the following command to install third-party dependencies\n# pip install numpy soundfile openai\n\nimport os\nimport base64\nimport soundfile as sf\nimport numpy as np\nfrom openai import OpenAI\n\nclient = OpenAI(\n    api_key=os.getenv("DASHSCOPE_API_KEY"),  # Confirm the environment variable is set\n    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",\n)\n\n# Audio and video analysis\ncompletion = client.chat.completions.create(\n    model="qwen3.5-omni-plus",\n    messages=[\n        {\n            "role": "user",\n            "content": [\n                {\n                    "type": "video_url",\n                    "video_url": {\n                        "url": "https://help-static-aliyun-doc.aliyuncs.com/file-manage-files/zh-CN/20241115/cqqkru/1.mp4"\n                    },\n                },\n                {"type": "text", "text": "What is the content of the video?"},\n            ],\n        },\n    ],\n    # Set the output modality. In audio/video analysis scenarios, it is recommended to return text results directly\n    modalities=["text"],\n    # stream must be set to True, otherwise an error will occur\n    stream=True,\n    stream_options={"include_usage": True},\n)\n\nprint("Model response:")\nfor chunk in completion:\n    # Process the text part\n    if chunk.choices and chunk.choices[0].delta.content:\n        print(chunk.choices[0].delta.content, end="")\n\n# Web search\ntry:\n    completion = client.chat.completions.create(\n        model="qwen3.5-omni-plus",\n        messages=[{\n            "role": "user", \n            "content": "Please check today's date and day of the week, and tell me what important holidays there are today"\n        }],\n        stream=True,\n        stream_options={"include_usage": True},\n        # Enable web search\n        extra_body={\n            "enable_search": True,\n            "search_options": {\n                # Web search strategy, only supports configuring as agent\n                "search_strategy": "agent"\n            }\n        }\n    )\n    \n    print("\\n\\n\\n\\nWeb search model response (including real-time information):")\n    for chunk in completion:\n        if chunk.choices and chunk.choices[0].delta.content:\n            print(chunk.choices[0].delta.content, end="")\n    print()\n    \nexcept Exception as e:\n    print(f"Request failed: {e}")\n
\n

Realtime Invocation

\n

Example code is provided below:

\n
# Dependencies: dashscope >= 1.23.9, pyaudio\nimport os\nimport base64\nimport time\nimport pyaudio\nfrom dashscope.audio.qwen_omni import MultiModality, AudioFormat,OmniRealtimeCallback,OmniRealtimeConversation\nimport dashscope\n\n# Configuration: endpoint, API key, voice, model, and system instructions\n# Specify the region: set to cn for mainland China (Beijing), or intl for international (Singapore)\nregion = 'cn'\nbase_domain = 'dashscope.aliyuncs.com' if region == 'cn' else 'dashscope-intl.aliyuncs.com'\nurl = f'wss://{base_domain}/api-ws/v1/realtime'\n# Configure the API key. If no environment variable is set, replace the line below with: dashscope.api_key = "sk-xxx"\ndashscope.api_key = os.getenv('DASHSCOPE_API_KEY')\n# Specify the voice\nvoice = 'Tina'\n# Specify the model\nmodel = 'qwen3.5-omni-plus-realtime'\n# Specify the system instructions\ninstructions = "You are a personal assistant named Xiaoyun. Please answer the user's questions in a humorous and witty way."\nclass SimpleCallback(OmniRealtimeCallback):\n    def __init__(self, pya):\n        self.pya = pya\n        self.out = None\n    def on_open(self):\n        # Initialize audio output stream\n        self.out = self.pya.open(\n            format=pyaudio.paInt16,\n            channels=1,\n            rate=24000,\n            output=True\n        )\n    def on_event(self, response):\n        if response['type'] == 'response.audio.delta':\n            # Play audio\n            self.out.write(base64.b64decode(response['delta']))\n        elif response['type'] == 'conversation.item.input_audio_transcription.completed':\n            # Print transcription text\n            print(f"[User] {response['transcript']}")\n        elif response['type'] == 'response.audio_transcript.done':\n            # Print assistant response text\n            print(f"[LLM] {response['transcript']}")\n\n# 1. Initialize audio device\npya = pyaudio.PyAudio()\n# 2. Create callback and conversation session\ncallback = SimpleCallback(pya)\nconv = OmniRealtimeConversation(model=model, callback=callback, url=url)\n# 3. Connect and configure the session\nconv.connect()\nconv.update_session(output_modalities=[MultiModality.AUDIO, MultiModality.TEXT], voice=voice, instructions=instructions, enable_search=True)\n# 4. Initialize audio input stream\nmic = pya.open(format=pyaudio.paInt16, channels=1, rate=16000, input=True)\n# 5. Main loop for processing audio input\nprint("Conversation started. Speak into the microphone (Ctrl+C to exit)...")\ntry:\n    while True:\n        audio_data = mic.read(3200, exception_on_overflow=False)\n        conv.append_audio(base64.b64encode(audio_data).decode())\n        time.sleep(0.01)\nexcept KeyboardInterrupt:\n    # Clean up resources\n    conv.close()\n    mic.close()\n    callback.out.close()\n    pya.terminate()\n    print("\\nConversation ended")\n
\n

Qwen3.5-Omni Voice List

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Chinese and English Custom Voice (5 Speakers)

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
VoiceLanguageVoice DescriptionTextSample
TinaChineseLike a warm cup of milk tea, this voice feels soft and comforting, with a steady core when it comes to solving problems.阳光晒得被子软软的，手边的茶还冒着热气，待办事项也一件件完成了。没有惊天动地，但每一件小事都稳稳当当，这样的日子，真的让人觉得，幸福就在此刻呀！接下来，我们一起把下一件小事也好好完成吧～
CindyChinese (Taiwanese accent)Soft-spoken, sweet, and effortlessly lovable.对～因为我其实从小到大，嗯，没有太怎么去吃过这种类型的东西啊。真的，因为我小的时候，我跟你讲，我小时候其实是有养过小兔子。
Liora MiraChineseCozy, grounded, and gentle in all the right ways.对对，我第二次去的话，老师跟我说，欸，你进步很大啊，哈哈，所以我就觉得而且他说的我基本上我发现我配音中的气息也变强了。
SunnybobiChineseEasy to be around, with a natural warmth and just a hint of shyness.我好像很少，就是除非说，这个地方我很想去，但是，一个人又不太方便的时候我会叫朋友。但大部分我都还是愿意自己一个人逛的，嗯，嗯毕竟比较i，对，哼哼。
RaymondChineseClear and bright, with the kind of relaxed, homebody charm that feels instantly familiar.嗯，可以，纽约堡是吗？它是不是那种就是偏美式一点那种汉堡呀，啊。
\n

Chinese and English Scenario Voices (19 Speakers)

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
ScenariosVoiceLanguageVoice DescriptionTextSample
Emotional Companionship - Healing WarmthEthanChineseBright and youthful, with a light northern Mandarin accent.那应该是我现在吧，我觉得现在就非常幸福。
Theo CalmChineseHere to listen, connect, and offer warmth and support.呵，太好了。阳光升起的时候呀，葡萄会充满生机，我觉得你也会一样。你要记住啊，每个清晨都是新的开始，保持这份宁静的心，我呢会一直在这里，随时支持你。
SerenaChineseA naturally sweet voice with a kind and graceful feel.其实我真的有发现，我是一个特别善于观察别人情绪的人。
HarveyEnglishDeep, gentle, and shaped by experience, with the quiet familiarity of coffee and old books.Then by the end of the movie, when Dorothy clicks her heels and says, “There’s no place like home,” I got a little bit teary, I’ll admit. You know, I don’t even know why—I just, I just felt.
MaiaChineseSmart, soft, and easy to like.我呵，你怎么知道我挺会背诗的？你是不是对我有一些了解？我高中语文也不是特别好吧，大概也就是全年级前十吧。
Emotional Companionship - Energetic PersonalityEvanChineseCurious, upbeat, and full of youthful energy.哈喽，我是阿晨，姐姐今天累不累呀？有没有好好吃饭？哪怕只吃了一小口，也请对自己说一句辛苦了。生活常让你怀疑自己，但我想轻轻告诉你，那些你走过的路，扛下的事，都在证明你比想象中更强大。
QiaoChinese (Taiwanese accent)Sweet on first impression, with plenty of personality underneath.什么嘛，你男朋友回来关我什么事啊？我的周末就不是周末，要不要我的约会也跟你换啊？哎，我告诉你，我这周末要去参加我阿妈的生日聚餐，这可是半年前就约好的。
MomoChinesePlayful, a little offbeat, and full of good energy.我命令你们这些小男生，晚上早一点睡觉，听到没有。
WilChineseEasygoing and youthful, with a subtle Shenzhen accent.对啊对啊，呃我们是骑那个就是，比较少人走的一条捷径路哦，哎就是那种小，有点像小巷子那种路。
AngelChineseSweet and friendly, with just a touch of local character.屋顶很漂亮，所以在欧洲的话一定要抬头，不可以一直看手机，要抬头看屋顶，呃，上面有很多画，可是要注意小偷。可是我这整个过程中其实都呃没有遇到有人要偷我东西，当然我身上也没有钱，他没有东西可以偷。
Role PlayingLi CassianChineseCommanding and self-possessed, with an air of mystery and restraint.哎呦喂，你们这帮小崽子一天天的没别的事儿干，就是喜欢下棋。正好儿啊，杂家今儿个高兴儿，跟你们呀就说道说道这棋该怎么下。
MiaEnglishDelicate and soothing, with a quiet charm that brings calm to everyday life.Hey, friends, welcome back. I hope you're doing well today and that wherever you're watching from. You're feeling a little warm, a little calm. Maybe even holding your favorite drink. You know, something soft to settle into the day with.
JoynerEnglishEqual parts theatrical and down-to-earth, with a sharp sense of humor.Ah, come on, Frankie. That guy? Nah, he's probably just what, fainted from boredom or something. Watching that press go up and down all night. It's like what, watching paint dry, but you know, louder. Bet he saw his life flash before his eyes, right?
GoldEnglishConfident, raw, and grounded in real underground energy.This shit don’t make no sense, dawg. I’ve been tryna tell everybody that this whole AI shit — yo, they taking over the game. Soon enough, we go be dating robots.
Game and Anime Voice ActingKaterinaEnglishPolished and mature, with a tone that stays with you.Hello, how are you today? I'm doing great.
RyanEnglishStage-trained, with the presence and control that come from live performance.You bet. Old misses Perkins down the lane makes Jasmine honey cakes. That'll make your toast curl with delight. Kids sneak blossoms into lemonade stands, 10 petals per cup for extra magic, they'll tell you, with grass stained grins.
JenniferEnglishCrisp, resonant, and cinematic, with a distinctly American edge.Hi, my name is Jennifer. I've lived in China for like six years. Do I like China? Yeah, love to have hot pot, super love spicy food. What else? Oh I've been learning Chinese for a long time, was it hard to learn? Absolutely, probably the hardest thing that I've ever had to do, but little time, patience. What about you? Or are you finding learning Chinese good?
AidenEnglishDown-to-earth and approachable, with the easy warmth of someone who loves to cook.When I have to work late into the night, the thing that really keeps me awake is definitely coffee like I'll get a coffee at around three pm something like that and that'll keep me pretty hyped up for most of the night if I have to work like extra late then yeah I'll do something like another coffee maybe um I don't know like with my dinner after dinner yeah after dinner coffee.
MioneEnglishLike talking to a longtime friend who’s thoughtful, perceptive, and wise.So yeah, that’s my favorite hobby — I could probably go on for even longer, but, um, that’s my favorite hobby collecting Sunny Angels. There are so many more, to collect, and I can’t wait to build my collection, and, continue, persevering with this hobby. And, yeah, I really like collecting Sunny Angels.
\n

Chinese Dialect Voices (8 Speakers)

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
VoiceLanguageVoice DescriptionTextSample
SunnySichuaneseSweet and lively, with a subtle Sichuan flavor.胖娃儿胖嘟嘟，骑马上成都，成都有好耍，胖娃儿骑白马，白马跳得高，胖娃儿耍关刀，关刀耍得远，胖娃儿吃汤圆。
DylanBeijing MandarinGrounded and confident, with a classic Beijing feel.我们家那边后面有一个后山，就护城河那边儿。完了呢我们就在山上啊，就是其实也没什么，就是在土坡上跑来跑去，然后谁捡个那个嗯比较威风的棍儿，完了我们就就瞎打，呃要不就是什么掏个洞啊什么的。
EricSichuaneseFresh, sharp, and full of streetwise energy.你龟儿子搞啥子名堂嘛，把事情弄成这个样子老子要被你气死了，我看到你这个样子我心头都冒火，毛焦火辣的气都不打一处来，你龟儿太过分了，把我的东西都搞坏了，还晓不晓得认错，硬是要把我整冒火你才安逸唆，莫再烦老子爬球开。
PeterTianjin DialectDry, steady, and effortlessly funny.越线了啊，你心中纵有万千苦，不能往我们心窝儿杵，咱不朋友嘛。算了，把我这嘴皮子磨烂也给你们讲不明白。蝎了虎子掉面缸，听完你们光剩眨么眼儿了。我再说最后一次啊，我介不是走，是绝交，啊。他说我是小肚子拉口儿二波一，木鱼儿改梆子挨敲的货，面茶里煮元宵，混蛋沉底带砸锅。哼，从今儿起，咱就是天津大麻花啊，彻底掰了。
Joseph ChenMinnanWarm and seasoned, with the voice of an overseas Chinese speaker shaped by Southeast Asian roots.爱伫黄内底佫带小可青青，皮若是黄黄小可青青，表示会当买转去的，佫囥两三工来食拄仔好。
MarcusShaanxi DialectBold and unpolished in the best way, with a strong regional character.诶，伙计，你说这人活成嘛了，一天到黑忙得跟钟楼底下的车一样，堵得心慌。诶，老板催报表吗？孙娃作业要签字吗？屋里老嫂子还嫌我不陪她逛骡马市。
LiNanjing DialectGruff, expressive, and oddly charming when the complaints start rolling in.哎！老头儿！让一下诶！没得长眼睛啊。你哪个？老十三的在这个叫魂啊！急着给投胎去呀。xxx这么叼宽的巷子你过不去啊我过你个吊啊你麻痹你把这马扎子摆到路中间这个巷子是你家开的呀
RockyCantoneseFull of Cantonese flavor, with great comedic timing and crisp tonal control.其实真系咁噶呵，即系人咧即系我觉得系，即系都几贱格嘅动物嚟噶呵，即系冇嘢做嗰阵时咧。就话唉死啦我成日都冇嘢做，咁边有钱啊咁。有嘢做嗰阵时咧又成日觉得，死啦啲嘢成日都做唔晒，喂大佬，好攰啊，点办啊？
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Multilingual Voice (23 Speakers)

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VoiceLanguageVoice DescriptionTextSample
SoheeKoreanGentle, cheerful Korean unnie full of emotion.친구들이 나한테 좀 그러더라, 선물에 내 성격이 좀 담겨 있대 그래서 차분하고 조용한 선물을 고르는 스타일이긴 한데 그게 내가 그 사람을 좀 진짜로 생각하고 있다는 방식이라서 내가 표현하는 방식 중의 하나인 것 같아. 결론은 이제 평소에 말해 줬던 작은 힌트를 기억을 해서 그 사람의 하루에 좀 스며들 수 있는 그런 선물. 그걸은 내가 가장 많이 고르는 스타일이지.
LennGermanCalm in suits, rebellious through post-punk.Das ist eine nette Überraschung!
Ono AnnaJapaneseMischievous childhood sweetheart.おかしいな、内部でガチャガチャ音がしてる。あっ、もしかして救急トレイにA3とA4が混ざってる？あー、誰かが両面スキャナーの保護シートを剥がし忘れてます。これじゃ確認できないのも当然です。
SonrisaSpanishWarm, cheerful Latin American lady.Claro que sí, imagínate sentado en esas sillas verdes tan bonitas con un café recién hecho que huele a canela. Hasta los libros viejos de la estantería parecen sonreír cuando alguien los hojea y el tocadiscos está poniendo un vals que te hará mover los pies sin darte cuenta. Es como viajar en el tiempo pero con buen humor.
BodegaSpanishHis laughter booms across the room and his opinions are delivered with fervor; he is a quintessential passionate Spanish tío who feels everything deeply.Claro que sí, imagina caminar sobre esa alfombra de hojas doradas crujiente bajo tus pies mientras el olor a canela de la panadería te guía hacia las mesitas con mantel a cuadros hasta el viento parece tararear la melodía de la guitarra.
EmilienFrenchRomantic French big brother.Bien sûr, passons à des questions plus générales : pour toi, quelle était l'opportunité qui t'a conduit à t'impliquer dans l'industrie du doublage ?
AndrePortugueseWhen this Portuguese guy speaks, his naturally comfortable and steady voice acts as a magnetic force, drawing you into a state of total relaxation.Ouvimos fazer compras no mercado local sim às vezes vou ao mercado local às vezes vou a supermercados maiores depende da compra que eu fazer depende onde estou às vezes quando viajo gosto de ir a mercados locais quer para ver que as pessoas dessa zona comem o que é que comem o que é que compram o que é que produzem não é que tipo de legumes que tipo de frutas aí vou aos mercados locais e normalmente no mercado local acho que a comida costuma ser mais saudável do que num supermercado.
Radio GolPortugueseHis voice cuts through the stadium noise, a steady stream of Portuguese analysis that builds tension until the final whistle.Ei você sentiu aquele cheiro de pão fresco parece que vem da padaria da esquina né? Tô com vontade de dar uma passada lá.
AlekRussianChill Russian, warm woolen soul.Конечно, знаешь, эти ржавые ворота как старый актер, который готов рассказать тысячу веселых историй. Вот например: раньше здесь кузнецы соревновались кто громче молотом стукнет, а детишки за орешками бегали, даже осенние листья тогда танцевали под музыку кузнечных мехов.
RizkyIndonesianHis voice has a soft, distinctive lilt common in Indonesia, making every word he speaks sound gentle yet remarkably clear.Nah. Waktu itu tuh ada teman yang ngedengar cara aku bacain iklan di radio, terus dia bilang eh suara lu kayaknya cocok banget deh buat jadi voice over talent. Mau nyoba enggak? Gitu.
RoyaPersianSports-loving girl with a free spirit.وَقْتی اَز دوچَرْخِهٔ ثابِت اِسْتِفادِه میکُنی، خِیلی فِشار رو رویِ مَفاصِلِت اِحْساس نِمیکُنی.فِشار کَمتَرِه، مَخْصوصاً بَرایِ کِسایی کِه زانو دَرْد دارَنْ یا تازِه میخوان وَرْزِش رو شُروع کُنَنْ
ArdaTurkishBalanced Turkish voice: clean, smooth, warm.Yeni bir bilgi öğrenirken araştırma mı yoksa deneyerek keşfetme mi dersek, aslında ben ikisinin karışımını seviyorum. Bir konuyu temel olarak araştırıp zemin oluşturduktan sonra mutlaka deneyerek öğrenmeyi tercih ediyorum. Çünkü sadece teorik bilgi insanın kafasında soyut kalıyor. Deneyince bilginin kıvrımlarını keşferiyorsun.
HanaVietnameseMature Vietnamese woman with big-sister energy.Ừm, nếu hỏi tấm hình nào trong điện thoại gần đây nhất mà buồn cười nhất á thì chắc là tấm chụp con chó nhà mình, trời ơi, nó làm mình không nhịn được luôn ấy.
DolceItalianLeaning against a sun-drenched wall with effortless cool, he is a laid-back Italian uncle whose voice flows as smoothly as good Chianti.Ma dìgli altri cinque minuti, per favore dai sono ancora così assonnato.
JakubPolishAn artistic young man from a Polish town with a magnetic, sexy voice.No wiesz, chodziło mi o ten most. O świcie jak mgła nad rzeką jeszcze jest, a kamienie po deszczu tak błyszczą. Stary Janek z piekarni mówił, że o tej porze wygląda jak z bajki. I chleb prosto z pieca pachnie najmocniej.
GrietDutchMature and artistic Dutch woman.van alleen op straat zijn. En heel veel weiden rondom U, alles is rustig, alles is stil. En de dag lijkt zo eindeloos lang te duren of zo. Snap je, dat zo?
EliškaCzechCzech voice full of Central European warmth.Emm... No, emm... já mám ráda černobílé fotky, protože tam není rušivá barva, jen čistý výraz, světlo a stín. A připadá mi to víc opravdový.
MarinaHebrewHer voice carries the distinctive guttural warmth of fluent Hebrew, marking her as a true daughter of the land.גדלתי במקום שיש בו המון המון תרבויות שונות, והמון שפות שונות. אז אתה לומד איך לתקשר עם אנשים בתרבות אחרת בצורה מאוד ברורה, די מהר. ואז כשעברתי לגור בקנדה, אז בכלל, בוונקובר יש פה כל כך הרבה תרבויות שונות.
SiiriFinnishShe is a Finnish maiden who carries herself with both wisdom and warmth.Se oli kokemuksena muutenkin se reissu jotenkin tosi semmonen emme ties silmiä avaava koska se on ympäristönä jotenkin niin erilainen kuitenkin Suomeen verrattuna että kun siellä on kuitenkin hiekkarantoja ja sitten jotenkin siellä oli niin semmonen rento meiininki ja sellaista juhlahumua ja sellaista erilaista emmä oo kai osaa selittää sitä kunnolla mutta se oli jotenkin niin toisenlainen maailma ja mä päätti jo silloin että mä joo jossain kohtaa muuttaa pois Suomesta että mun pitää päästä tällaiseen ympäristöön ja kyllä mä silloin tiesi jo heti että
IngridNorwegianRaised amidst the rolling hills of rural Norway, she carries the simplicity of country life.Og så bestemte jeg meg at nå er det på tide, nå må jeg bare prøve om jeg kan klare å få passe på høner, og noe som jeg hadde drømt om da, ikke sant? At du kan gå og plukke egg om morgenen.
SiggaIcelandicIn a remote corner of Iceland, there lives a young woman defined by her profound wisdom.Ef við getum orðað það þannig þannig menntaskóla árin eru vissulega mjög eftirminnileg og það sem gerðist líka í menntaskóla er að ég kynntist alveg dásamlegum vinkonum sem eru vinkonur mínar ennþá í dag og við erum tíu saman sem erum í svona sumar klúbbi og kynntust í menntaskóla og við höldum alltaf sambandi og höfum gert alveg síðan. Við bara byrjuðum að verða vinkonur í byrjun menntaskólans.
BeaFilipinoMeet a sweet girl from the Philippines who simply can't function before her morning coffee.I'm ready! May kapi na ako,may tissue na rin in case matawa ako or maiyak.Go,go,go! Spill!
ChloeMalayShe is an office worker navigating the bustling business districts of Malaysia.Lagi satu, aku cuba kenal bila waktu aku paling cergas. Bagi aku, jadi waktu tu aku kerahkan tenaga buat kerja paling susah.
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Citation

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Feel free to cite the following article if you find Qwen3.5-Omni helpful:

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@misc{qwen35omniblog,\n    title = {Qwen3.5-Omni: Scaling Up, Toward Native Omni-Modal AGI},\n    url = {https://qwen.ai/blog?id=qwen3.5-omni},\n    author = {Qwen Team},\n    month = {March},\n    year = {2026}\n}\n
\n", "date_published": "2026-03-29T20:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen3.5-omni", "id": "urn:aggr:item:a814261fa01e5ea50c4827e611438ca6aa2a5257", "image": "https://img.alicdn.com/imgextra/i1/O1CN01gJhkPX1gZVB49xEk1_!!6000000004156-2-tps-1590-954.png", "summary": "QWEN CHAT Hugging Face Offline Demo Hugging Face Realtime Demo ModelScope Offline Demo ModelScope Realtime Demo Qwen3.5-Omni is Qwen’s latest generation of fully omnimodal LLM, supporting the understanding of text, images, audio, and…", "tags": [ "ai", "release" ], "title": "Qwen3.5-Omni: Scaling Up, Toward Native Omni-Modal AGI", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-03-29-qwen3-5-omni-scaling-up-toward-native-omni-modal-agi/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

We are pleased to announce the deployment of Qwen3.5-Max-Preview on Arena, where it has demonstrated exceptional performance during the preliminary evaluations. As we proceed with final optimizations ahead of the release within the next two weeks, we invite the community to evaluate the model’s capabilities via https://arena.ai/.

\n", "date_published": "2026-03-18T20:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen3.5-max-preview", "id": "urn:aggr:item:5fc6423743288dbab0e4ea3eeb412004e39bcc9e", "image": "https://img.alicdn.com/imgextra/i1/O1CN01oNNHZp1u2RCpQ3Kyj_!!6000000005979-2-tps-1590-954.png", "summary": "We are pleased to announce the deployment of Qwen3.5-Max-Preview on Arena, where it has demonstrated exceptional performance during the preliminary evaluations. As we proceed with final optimizations ahead of the release within the next…", "tags": [ "ai", "release" ], "title": "Qwen3.5-Max-Preview Now Available on Arena", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-03-18-qwen3-5-max-preview-now-available-on-arena/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"Qwen3

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QWEN CHAT GitHub Hugging Face ModelScope DISCORD

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We are delighted to announce the official release of Qwen3.5, introducing the open-weight of the first model in the Qwen3.5 series, namely Qwen3.5-397B-A17B. As a native vision-language model, Qwen3.5-397B-A17B demonstrates outstanding results across a full range of benchmark evaluations, including reasoning, coding, agent capabilities, and multimodal understanding, empowering developers and enterprises to achieve significantly greater productivity. Built on an innovative hybrid architecture that fuses linear attention (via Gated Delta Networks) with a sparse mixture-of-experts, the model attains remarkable inference efficiency: although it comprises 397 billion total parameters, just 17 billion are activated per forward pass, optimizing both speed and cost without sacrificing capability. We have also expanded our language and dialect support from 119 to 201, providing broader accessibility and enhanced support to users around the world.

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Performance

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Below we present the comprehensive evaluation of our models against frontier models in a wide range of evaluation tasks, covering different tasks and modalities.

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Language

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GPT5.2Claude 4.5 OpusGemini-3 ProQwen3-Max-ThinkingK2.5-1T-A32BQwen3.5-397B-A17B
Knowledge
MMLU-Pro87.489.589.885.787.187.8
MMLU-Redux95.095.695.992.894.594.9
SuperGPQA67.970.674.067.369.270.4
C-Eval90.592.293.493.794.093.0
Instruction Following
IFEval94.890.993.593.493.992.6
IFBench75.458.070.470.970.276.5
MultiChallenge57.954.264.263.362.767.6
Long Context
AA-LCR72.774.070.768.770.068.7
LongBench v254.564.468.260.661.063.2
STEM
GPQA92.487.091.987.487.688.4
HLE35.530.837.530.230.128.7
HLE-Verified¹43.338.84837.6--37.6
Reasoning
LiveCodeBench v687.784.890.785.985.083.6
HMMT Feb 2599.492.997.398.095.494.8
HMMT Nov 2510093.393.394.791.192.7
IMOAnswerBench86.384.083.383.981.880.9
AIME2696.793.390.693.393.391.3
General Agent
BFCL-V463.177.572.567.768.372.9
TAU2-Bench87.191.685.484.677.086.7
VITA-Bench38.256.351.640.941.949.7
DeepPlanning44.633.923.328.714.534.3
Tool Decathlon43.843.536.418.827.838.3
MCP-Mark57.542.353.933.529.546.1
Search Agent
HLE w/ tool45.543.445.849.850.248.3
BrowseComp65.867.859.253.9--/74.969.0/78.6
BrowseComp-zh76.162.466.860.9--70.3
WideSearch76.876.468.057.972.774.0
Seal-045.047.745.546.957.446.9
Multilingualism
MMMLU89.590.190.684.486.088.5
MMLU-ProX83.785.787.778.582.384.7
NOVA-6354.656.756.754.256.059.1
INCLUDE87.586.290.582.383.385.6
Global PIQA90.991.693.286.089.389.8
PolyMATH62.579.081.664.743.173.3
WMT24++78.879.780.777.677.678.9
MAXIFE88.479.287.584.072.888.2
Coding Agent
SWE-bench Verified80.080.976.275.376.876.4
SWE-bench Multilingual72.077.565.066.773.069.3
SecCodeBench68.768.662.457.561.368.3
Terminal Bench 254.059.354.222.550.852.5
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* HLE-Verified: a verified and revised version of Humanity’s Last Exam (HLE), accompanied by a transparent, component-wise verification protocol and a fine-grained error taxonomy. We open-source the dataset at https://huggingface.co/datasets/skylenage/HLE-Verified.
\n* TAU2-Bench: we follow the official setup except for the airline domain, where all models are evaluated by applying the fixes proposed in the Claude Opus 4.5 system card.
\n* MCP-Mark: GitHub MCP server uses v0.30.3 from api.githubcopilot.com; Playwright tool responses are truncated at 32k tokens.
\n* Search Agent: most search agents built on our model adopt a simple context-folding strategy(256k): once the cumulative Tool Response length reaches a preset threshold, earlier Tool Responses are pruned from the history to keep the context within limits.
\n* BrowseComp: we tested two strategies, simple context-folding achieved a score of 69.0, while using the same discard-all strategy as DeepSeek-V3.2 and Kimi K2.5 achieved 78.6.
\n* WideSearch: we use a 256k context window without any context management.
\n* MMLU-ProX: we report the averaged accuracy on 29 languages.
\n* WMT24++: a harder subset of WMT24 after difficulty labeling and rebalancing; we report the averaged scores on 55 languages using XCOMET-XXL.
\n* MAXIFE: we report the accuracy on English + multilingual original prompts (totally 23 settings).
\n* Empty cells (--) indicate scores not yet available or not applicable.

\n

Vision Language

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
GPT5.2Claude 4.5 OpusGemini-3 ProQwen3-VL-235B-A22BK2.5-1T-A32BQwen3.5-397B-A17B
STEM and Puzzle
MMMU86.780.787.280.684.385.0
MMMU-Pro79.570.681.069.378.579.0
MathVision83.074.386.674.684.288.6
Mathvista(mini)83.180.087.985.890.190.3
We-Math79.070.086.974.884.787.9
DynaMath86.879.785.182.884.486.3
ZEROBench93104912
ZEROBench_sub33.228.439.028.433.541.0
BabyVision34.414.249.722.236.552.3/43.3
General VQA
RealWorldQA83.377.083.381.381.083.9
MMStar77.173.283.178.780.583.8
HallusionBench65.264.168.666.769.871.4
MMBenchEN-DEV-v1.188.289.293.789.794.293.7
SimpleVQA55.865.773.261.371.267.1
Text Recognition and Document Understanding
OmniDocBench1.585.787.788.584.588.890.8
CharXiv(RQ)82.168.581.466.177.580.8
MMLongBench-Doc--61.960.556.258.561.5
CC-OCR70.376.979.081.579.782.0
AI2D_TEST92.287.794.189.290.893.9
OCRBench80.785.890.487.592.393.1
Spatial Intelligence
ERQA59.846.870.552.5--67.5
CountBench91.990.697.393.794.197.2
RefCOCO(avg)----84.191.187.892.3
ODInW13----46.343.2--47.0
EmbSpatialBench81.375.761.284.377.484.5
RefSpatialBench----65.569.9--73.6
LingoQA68.878.872.866.868.281.6
V*75.967.088.085.977.095.8/91.1
Hypersim------11.0--12.5
SUNRGBD------34.9--38.3
Nuscene------13.9--16.0
Video Understanding
VideoMME(w sub.)8677.688.483.887.487.5
VideoMME(w/o sub.)85.881.487.779.083.283.7
VideoMMMU85.984.487.680.086.684.7
MLVU (M-Avg)85.681.783.083.885.086.7
MVBench78.167.274.175.273.577.6
LVBench73.757.376.263.675.975.5
MMVU80.877.377.571.180.475.4
Visual Agent
ScreenSpot Pro--45.772.762.0--65.6
OSWorld-Verified38.266.3--38.163.362.2
AndroidWorld------63.7--66.8
Medical VQA
SLAKE76.976.481.372.581.679.9
PMC-VQA58.959.962.356.163.364.2
MedXpertQA-MM73.363.676.047.665.370.0
\n

* MathVision：our model’s score is evaluated using a fixed prompt, e.g., “Please reason step by step, and put your final answer within \\boxed{}.” For other models, we report the higher score between runs with and without the \\boxed{} formatting.
\n* BabyVision: our model’s score is reported with CI (Code Interpreter) enabled; without CI, the result is 43.3.
\n* V*: our model’s score is reported with CI (Code Interpreter) enabled; without CI, the result is 91.1.
\n* Empty cells (--) indicate scores not yet available or not applicable.
\n* Upon review, we found inconsistencies in the evaluation setup of the historical version Qwen3-VL-235B-A22B on SLAKE and PMC-VQA. The corresponding comparative scores were corrected on March 15, 2026.

\n

Compared to the Qwen3 series, the post-training performance gains in Qwen3.5 primarily stem from our extensive scaling of virtually all RL tasks and environments we could conceive. Our approach placed strong emphasis on increasing the difficulty and generalizability of RL environments, rather than optimizing for specific metrics or narrow categories of queries. Below, we illustrate the improvements in general agent capabilities resulting from this RL environment scaling. The overall performance is calculated by averaging the ranking of each model on the following benchmarks: BFCL-V4, VITA-Bench, DeepPlanning, Tool-Decathlon, and MCP-Mark. Additional scaling results across a broader range of tasks will be detailed in our upcoming technical report.

\n

\"\"

\n

Pretraining

\n

Qwen3.5 advances pretraining across three dimensions—power, efficiency, and versatility:

\n

Power: Trained on a significantly larger scale of visual-text tokens compared to Qwen3, with enriched Chinese/English, multilingual, STEM, and reasoning data under stricter filtering. This enables cross-generation parity: Qwen3.5-397B-A17B matches the >1T-parameter Qwen3-Max-Base.

\n

Efficiency: Built on Qwen3-Next architecture—higher-sparsity MoE, Gated DeltaNet + Gated Attention hybrid attention, stability optimizations, and multi-token prediction. Under the 32k/256k context length, the decoding throughput of Qwen3.5-397B-A17B is 8.6x/19.0x that of Qwen3-Max, and the performance is comparable. The decoding throughput of Qwen3.5-397B-A17B is 3.5x/7.2 times that of Qwen3-235B-A22B.

\n

Versatility: Natively multimodal via early text-vision fusion and expanded visual/STEM/video data, outperforming Qwen3-VL at similar scales. Multilingual coverage grows from 119 to 201 languages/dialects; a 250k vocabulary (vs. 150k) boosts encoding/decoding efficiency by 10–60% across most languages.

\n

\"\"

\n

Below we present the performance of the base models.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Qwen3-235B-A22BGLM-4.5-355B-A32BDeepSeek-V3.2-671B-A37BK2-1T-A32BQwen3.5-397B-A17B
General Knowledge & Multilingual
MMLU87.3386.5688.1187.3888.61
MMLU-Pro67.7365.0062.8267.6476.01
MMLU-Redux87.4486.8687.2986.6589.09
SuperGPQA42.8444.5643.4644.8657.96
C-Eval91.8285.5090.4891.8291.82
MMMLU81.2782.2683.2082.2685.82
Include75.2673.4176.5272.0579.27
Nova66.5260.9660.4061.4467.55
Reasoning & STEM
BBH87.9587.6886.0389.1190.98
KoRBench50.8052.8054.0053.8454.08
GPQA47.4744.6344.1646.7854.64
MATH71.8461.8464.4071.5074.14
GSM8K91.1789.3189.1292.1293.71
Coding
Evalplus77.6069.4962.6871.7779.32
MultiPLE65.9462.5161.8870.6479.39
SWE-agentless31.7729.2334.6728.5443.26
CRUX-I64.2567.6363.2570.5071.13
CRUX-O78.8877.1373.8877.1382.38
\n

Infrastructure

\n

Qwen3.5 enables efficient native multimodal training via a heterogeneous infrastructure that decouples parallelism strategies across vision and language components, avoiding uniform approaches’ inefficiencies. By exploiting sparse activations for cross-component computation overlap, it achieves near 100% training throughput versus pure-text baselines on mixed text-image-video data. Complementing this, a native FP8 pipeline applies low precision to activations, MoE routing, and GEMM operations—with runtime monitoring preserving BF16 in sensitive layers—yielding ~50% activation memory reduction and >10% speedup while scaling stably to tens of trillions of tokens.

\n

To continuously unleash the power of reinforcement learning, we built a scalable asynchronous RL framework that supports Qwen3.5 models of all sizes, spanning text, multimodal, and multi-turn settings. By adopting a fully disaggregated training-inference architecture, the framework achieves significantly improved hardware utilization, dynamic load balancing, and fine-grained fault recovery. It further optimizes throughput and enhances train–infer consistency via techniques such as FP8 end-to-end training, rollout router replay, speculative decoding, and multi-turn rollout locking. Through tight system-algorithm co-design, the framework effectively bounds gradient staleness and mitigates data skewness, preserving both training stability and performance. Moreover, it natively supports agentic workflows, facilitating seamless multi-turn interactions without framework-induced interruptions. This decoupled design enables the system to accommodate million-scale agent scaffolds and environments, substantially boosting model generalization. Collectively, these optimizations yield a 3×–5× end-to-end speedup, demonstrating superior stability, efficiency, and scalability.

\n

\"\"

\n

Play with Qwen3.5

\n

Chat with Qwen3.5

\n

Feel free to use Qwen3.5 on Qwen Chat. We provide three modes, auto, thinking, and fast, to users to choose. With “Auto” mode, users can leverage adaptive thinking, which can think and use tools including search and code interpreter, while with “Thinking” mode, the model can think deeply for hard problems. With “Fast” mode, the model answers questions instantly without spending tokens on thinking.

\n

ModelStudio

\n

Users can experience our flagship model, Qwen3.5-Plus, by invoking it through Alibaba Cloud ModelStudio. To enable advanced capabilities such as reasoning, web search, and Code Interpreter, simply pass the following parameters:

\n\n

Example code is provided below:

\n
"""\nEnvironment variables (per official docs):\n  DASHSCOPE_API_KEY: Your API Key from https://bailian.console.aliyun.com\n  DASHSCOPE_BASE_URL: (optional) Base URL for compatible-mode API.\n  DASHSCOPE_MODEL: (optional) Model name; override for different models.\n  DASHSCOPE_BASE_URL:\n    - Beijing: https://dashscope.aliyuncs.com/compatible-mode/v1\n    - Singapore: https://dashscope-intl.aliyuncs.com/compatible-mode/v1\n    - US (Virginia): https://dashscope-us.aliyuncs.com/compatible-mode/v1\n"""\nfrom openai import OpenAI\nimport os\n\napi_key = os.environ.get("DASHSCOPE_API_KEY")\nif not api_key:\n    raise ValueError(\n        "DASHSCOPE_API_KEY is required. "\n        "Set it via: export DASHSCOPE_API_KEY='your-api-key'"\n    )\n\nclient = OpenAI(\n    api_key=api_key,\n    base_url=os.environ.get(\n        "DASHSCOPE_BASE_URL",\n        "https://dashscope-intl.aliyuncs.com/compatible-mode/v1",\n    ),\n)\n\nmessages = [{"role": "user", "content": "Introduce Qwen3.5."}]\n\nmodel = os.environ.get(\n    "DASHSCOPE_MODEL",\n    "qwen3.5-plus",\n)\ncompletion = client.chat.completions.create(\n    model=model,\n    messages=messages,\n    extra_body={\n        "enable_thinking": True,\n        "enable_search": False\n    },\n    stream=True\n)\n\nreasoning_content = ""  # Full reasoning trace\nanswer_content = ""  # Full response\nis_answering = False  # Whether we have entered the answer phase\nprint("\\n" + "=" * 20 + "Reasoning" + "=" * 20 + "\\n")\n\nfor chunk in completion:\n    if not chunk.choices:\n        print("\\nUsage:")\n        print(chunk.usage)\n        continue\n\n    delta = chunk.choices[0].delta\n\n    # Collect reasoning content only\n    if hasattr(delta, "reasoning_content") and delta.reasoning_content is not None:\n        if not is_answering:\n            print(delta.reasoning_content, end="", flush=True)\n        reasoning_content += delta.reasoning_content\n\n    # Received content, start answer phase\n    if hasattr(delta, "content") and delta.content:\n        if not is_answering:\n            print("\\n" + "=" * 20 + "Answer" + "=" * 20 + "\\n")\n            is_answering = True\n        print(delta.content, end="", flush=True)\n        answer_content += delta.content\n
\n

You can effortlessly integrate the Bailian API with third-party coding tools, such as Qwen Code, Claude Code, Cline, OpenClaw, OpenCode, etc., to enable a seamless “vibe coding” experience.

\n

Summary and Future Work

\n

Qwen3.5 provides a strong foundation for universal digital agents through its efficient hybrid architecture and native multimodal reasoning. The next leap requires shifting from model scaling to system integration: building agents with persistent memory for cross-session learning, embodied interfaces for real-world interaction, self-directed improvement mechanisms, and economic awareness to operate within practical constraints. The goal is coherent systems that function autonomously over time, transforming today’s task-bound assistants into persistent, trustworthy partners capable of executing complex, multi-day objectives with human-aligned judgment.

\n

Demo

\n

Now Qwen3.5 playing as an agent is capable of think, search, use tools, and build in the context of multimodality.

\n

Think, search, and create

\n

Qwen3.5

\n

Coding & Agents

\n

Web Dev

\n

Qwen3.5 can help with web development, especially for frontend tasks like building web pages and designing user interfaces. It makes creating websites easier by turning simple instructions into working code.

\n

Car Game Next

\n

Qwen3.5

\n

Website Next

\n

Qwen3.5

\n

Travel Plan Next

\n

Qwen3.5

\n

OpenClaw

\n

Qwen3.5 can be used with OpenClaw to power coding tasks. By integrating with OpenClaw as a third-party agent environment, Qwen3.5 can carry out web search, gather information, and produce structured reports—combining its reasoning and tool use with OpenClaw’s interface for a smooth coding and research experience.

\n

Search and Report

\n

Qwen3.5

\n

Qwen Code

\n

With Qwen3.5 as the underlying model, Qwen Code supports “vibe coding”, turning natural-language instructions into code, iterating on projects in real time, and handling creative tasks such as generating videos or other assets. Together, Qwen Code and Qwen3.5 provide a streamlined experience for both everyday programming and exploratory coding.

\n

Vibe Coding Next

\n

Qwen3.5

\n

Creating a Video Next

\n

Qwen3.5

\n

Visual Agents

\n

GUI Agents

\n

Acting as a visual agent for productivity automation, Qwen3.5 enables autonomous interaction with smartphones and computers. As a mobile agent, it can follow natural-language instructions to take actions within mobile apps and enable smooth interaction across multiple apps. As a computer agent, it handles complex, long-horizon desktop workflows, enabling office automation.

\n

Excel Next

\n

User

\n

Fill the missing rows and columns which show the total value

\n

Qwen3.5

\n

Organizing Files Next

\n

User

\n

The ‘/Users/username/Downloads’ folder contains mixed files. Please organize them into subfolders: create ‘PDFs’ folder and move all .pdf files there, create ‘Images’ folder and move all .jpg/.png/.gif files there, create ‘Documents’ folder and move all .docx/.xlsx/.pptx files there, create ‘Archives’ folder and move all .zip/.tar/.gz files there. Files with other extensions should remain in Downloads root.

\n

Qwen3.5

\n

Changing Theme Next

\n

User

\n

Help me to install Orchis theme from gnome-look.org and change to it for my GNOME desktop.

\n

Qwen3.5

\n

Searching, Liking, and Commenting Next

\n

User

\n

Search for qwen3vl on YouTube, then like the video, save it to watch later, and comment _it is helpful

\n

Qwen3.5

\n

Checking trains Next

\n

User

\n

Please check on Ctrip for me the cheapest train from Hangzhou to Nanjing the day after tomorrow

\n

Qwen3.5

\n

Visual Coding

\n

With its input length expanded to one million tokens, Qwen3.5 can process up to two hours of video. This unlocks a range of powerful applications, from turning hand-drawn UI sketches into clean frontend code, to reverse-engineering logic from simple gameplay footage, to condensing long videos into structured web pages and visual summaries.

\n

Video Game to Code Next

\n

User

\n

复刻这个小游戏的 HTML 代码

\n

Qwen3.5

\n

Video to Mindmap Next

\n

User

\n

Summarize the video chronologically and generate a structured mind map in mermaid.

\n

Qwen3.5

\n

\"image\"

\n

Web Dev with Image Generation Next

\n

User

\n

Create a homepage of OpenQwen, a virtual assistant personal agent that can help with coding, office works, shopping and so on. Generate high-quality images as the website’s resources, including an avatar and demos of its use cases.

\n

Qwen3.5

\n

\"image\"

\n

Web Dev with Web Search and Image Search Next

\n

User

\n

Make me a website with real pictures to compare the top 5 nations with the largest areas.

\n

Qwen3.5

\n

\"image\"

\n

Video to HTML Next

\n

User

\n

制作 HTML 网页展示这个美食的制作过程，网页可以添加一些动画，注重美感和质感

\n

Qwen3.5

\n

Spatial Intelligence

\n

By modeling pixel-level spatial relationships in images, Qwen3.5 achieves higher accuracy in tasks like object counting, relative positioning, and spatial description. It effectively mitigates errors caused by perspective shifts or occlusions—showcasing strong potential for embodied AI applications such as autonomous driving scene understanding and robotic navigation.

\n

Intelligent Driving Next

\n

User

\n

What you are seeing are frames from a video sequence. Your answer should focus on the final dynamic outcome, rather than analyzing the results of each individual frame. Why didn’t you stop at this intersection?

\n

\"image\"

\n

\"image\"

\n

\"image\"

\n

\"image\"

\n

\"image\"

\n

Qwen3.5

\n

<think>

\n

The user wants to know why the vehicle didn’t stop at the intersection based on the provided video frames.

\n
    \n
  1. \n

    Analyze the sequence of events:

    \n\n
    2. \n
  2. \n

    Determine the traffic rule: In the UK (driving on the left, red double-decker bus), an amber light means “Stop at the stop line.” However, there is a crucial exception: “You may go on only if the AMBER appears after you have crossed the stop line or are so close to it that to pull up might cause an accident.”

    \n
    3. \n
  3. \n

    Apply the rule to the visual evidence:

    \n\n
    4. \n
  4. \n

    Formulate the answer:

    \n\n
    5. \n
  5. \n

    Refine the answer: Ensure it directly answers “Why didn’t you stop?”

    \n\n

    Let’s combine these points. The primary reason is proximity to the stop line when the signal changed.

    \n\n

    Conclusion stands: The light changed to amber when the vehicle was too close to stop safely.

    \n
    6. \n
\n

</think>

\n

I did not stop because the traffic lights turned amber just as I was approaching the stop line.

\n

Looking at the sequence:

\n
    \n
  1. In the earlier frames, the traffic lights are clearly green, so I continued driving towards the intersection.
    2. \n
  2. By the fourth frame, as I reached the advanced stop line (the box with the bicycle symbol), the lights changed to amber.
    3. \n
  3. At that specific moment, I was too close to the intersection to stop safely without slamming on the brakes, so I proceeded through the junction while the light was amber.
    4. \n
\n

Intelligent Driving Next

\n

User

\n

我的车在什么时候并线的，又在什么时候开始右转的

\n

Qwen3.5

\n

<think>

\n

用户希望知道两个具体的时间点：

\n
    \n
  1. 并线（变道）的时间：车辆从当前车道移动到右侧车道（或出口匝道）的时间。
    2. \n
  2. 开始右转的时间：车辆开始进行明显的右转动作的时间。
    3. \n
\n

分析视频：

\n\n

确定时间点：

\n
    \n
  1. \n

    并线 (Lane Change):

    \n\n
    2. \n
  2. \n

    开始右转 (Start Turning Right):

    \n\n
    3. \n
\n

综合结论：

\n\n

再仔细看一下并线。 00:00-00:04 车在行车道。 00:05 车开始向右靠，压过虚线。这是并线出主路。 00:06-00:08 车在减速车道/匝道上直行。 00:09 车头明显向右转，进入服务区/停车场。

\n

所以： 并线时间：00:05 右转时间：00:09

\n

让我们再精确一点。 00:05 时，车辆开始向右移动，跨越车道线。 00:09 时，车辆经过路口，明显向右转弯进入停车场区域。

\n

最终回答构建： 并线发生在 00:05。 右转发生在 00:09。

\n

</think>

\n

根据视频画面分析：

\n\n

Thinking with grounding Next

\n

User

\n

Is the telephone booth on the left or right side of the yellow van? \"image\"

\n

Qwen3.5

\n

<think>

\n

The user wants to know the relative position of the telephone booth compared to the yellow van.

\n
    \n
  1. Locate the yellow van: I need to scan the street in the lower middle part of the image. I see a line of parked cars and moving vehicles. There is a distinct yellow van [403, 776, 423, 804] parked on the right side of the road (from the viewer’s perspective, it’s driving away or parked facing away, but actually, looking closely, it’s facing towards the camera, wait no, let’s re-evaluate). Let’s look at the traffic flow. The cars on the left are facing towards the camera (headlights visible? hard to tell, but they are parked). The cars on the right seem to be parked or moving. Wait, let’s look at the yellow vehicle. It’s a yellow van [403, 776, 423, 804].
    2. \n
  2. Locate the telephone booth: I need to find a red telephone booth. Scanning near the yellow van. Just to the right of the yellow van, further down the sidewalk/street area, there is a red object. Let’s zoom in mentally. Ah, there is a red telephone booth [449, 796, 461, 831] standing on the sidewalk.
    3. \n
  3. Determine relative position:\n\n
    4. \n
\n

Let me double-check.

\n\n

Wait, is that definitely a telephone booth? Yes, it’s a classic red British phone box. Is that definitely a yellow van? Yes, it looks like a small yellow delivery van or similar vehicle.

\n

So, the telephone booth is on the right side of the yellow van.

\n

</think>

\n

The telephone booth is on the right side of the yellow van.

\n

Qwen3.5

\n

\"image\"

\n

Thinking with grounding Next

\n

User

\n

What is the color of the electric rickshaw? \"image\"

\n

Qwen3.5

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<think>

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The user is asking for the color of an “electric rickshaw”.

\n
    \n
  1. Scan the image for vehicles: I see cars, buses, and maybe a motorcycle or scooter.
    2. \n
  2. Locate potential candidates:\n\n
    3. \n
  3. Analyze the candidate in the bottom left:\n\n
    4. \n
  4. Determine the color: The vehicle is predominantly yellow.
    5. \n
\n

Let me double-check the term “electric rickshaw”. In an urban Chinese context (which this is, clearly Shanghai), these small three-wheeled vehicles are often electric. The one at the bottom left fits the description perfectly. It is yellow.

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Wait, is there another one?

\n\n

So the object is the small yellow vehicle in the bottom left corner.

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Let’s refine the bounding box if needed, but the question just asks for the color. The color is yellow.

\n

Let me re-examine the vehicle at [149, 912, 178, 933]. It looks like a person riding a small yellow vehicle. It could be a delivery scooter with a yellow box, or a small yellow three-wheeler. Given the prompt “electric rickshaw”, this is the most likely candidate. It is yellow.

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Let’s look for other possibilities.

\n\n

So, the answer is yellow.

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</think>

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The electric rickshaw is yellow.

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Qwen3.5

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\"image\"

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Thinking with grounding Next

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User

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How many cars are on the right hand side? \"image\"

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Qwen3.5

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<think>

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The user wants me to count the cars on the right-hand side of the image.

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    \n
  1. Identify the right side: Looking at the image, there’s a street with cars parked on both sides. The right side is the right half of the image.
    2. \n
  2. Scan for cars: I’ll start from the foreground (closest to the camera) and move backwards.
    3. \n
  3. Car 1: In the immediate foreground on the right, there’s a dark-colored car, possibly an SUV or crossover. It’s partially cut off by the right edge. Wait, looking closer, it’s the rear/side of a car. Let’s call this Car 1. [843, 453, 999, 856]
    4. \n
  4. Car 2: Just behind Car 1, there’s another car. It looks like a sedan, possibly silver or grey. [755, 497, 874, 666]
    5. \n
  5. Car 3: Further back, there’s another car parked. It’s darker and further away. [693, 477, 755, 584]
    6. \n
  6. Are there more? Looking further back into the distance on the right side, it’s hard to distinguish individual cars clearly due to darkness and distance, but those three are distinct. Wait, let me re-evaluate the car closest to the camera.\n\n
    7. \n
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Let me recount to be sure.

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So, that makes 3 cars clearly visible on the right side.

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Let me double check the left side just to be sure I’m not mixing them up.

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Okay, so the question is specifically about the right hand side.

\n\n

Wait, looking very closely at the group on the right.

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So the count is 3.

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Let me re-examine the bounding boxes provided in my thought process to ensure accuracy.

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Is there a car way back? Near the intersection? It’s hard to tell, but there might be headlights or taillights further down, but distinct parked cars seem to be just these three. Wait, let me look at the car at [693, 477, 755, 584]. That looks like a car facing away. Yes.

\n

So, 3 cars.

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Let me look again.

\n\n

Is there anything else? No, that seems to be it.

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Wait, let me look at the car at [693, 477, 755, 584] again. Is that actually a car? Yes, I can see taillights and the rear window. Is there a car behind that one? It’s very dark. I don’t see another distinct shape.

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So the answer is 3.

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Let me re-read the prompt. “How many cars are on the right hand side?” Okay, simple count.

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Let’s look really closely at the car closest to the camera on the right. It’s actually cut off. Then the next one. Then the one further back.

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Is it possible that the car at [755, 497, 874, 666] and [843, 453, 999, 856] are the same car? No, looking at the wheels and position, they are distinct. The closest car [843, 453, 999, 856] has its rear wheel visible. The car behind it [755, 497, 874, 666] has its rear wheel visible further back. Wait, no. Let’s trace the wheels.

\n\n

Actually, let’s look at the orientation.

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Okay, so there are 3 distinct cars parked on the right side.

\n

Let me count again carefully.

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    \n
  1. Closest car on right: Dark SUV/Crossover. [843, 453, 999, 856]
    2. \n
  2. Middle car on right: Sedan. [755, 497, 874, 666]
    3. \n
  3. Furthest car on right: Sedan/Hatchback. [693, 477, 755, 584]
    4. \n
\n

Are there any more further back? Near the intersection? There’s a light source further back, but no clear car shape. So, 3 cars.

\n

Wait, is the car at [693, 477, 755, 584] actually parked? It looks like it’s in the lane, maybe moving? Or parked further down. It’s positioned similarly to the others. Let’s assume it’s a car. Is there a car between the closest one and the middle one? Looking at the gap between [843, 453, 999, 856] and [755, 497, 874, 666]. The closest car [843, 453, 999, 856] takes up the bottom right corner. The next car [755, 497, 874, 666] is just ahead of it. The third car [693, 477, 755, 584] is ahead of that one.

\n

So, count = 3.

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Let me just confirm the bounding boxes again.

\n\n

Wait, looking at the image again, the car at [843, 453, 999, 856] is very close. The car at [755, 497, 874, 666] is behind it. The car at [693, 477, 755, 584] is behind that one.

\n

So yes, 3 cars.

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Let me check if there’s a car way in the back, past the intersection. There are lights, but no distinct car shape. So I will stick with 3.

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Final check. Left side:

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    \n
  1. White car (partial)
    2. \n
  2. White car
    3. \n
  3. Orange Mini
    4. \n
  4. Dark car
    5. \n
  5. Dark car (further back) Total 5 on left.
    6. \n
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Right side:

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    \n
  1. Dark car (furthest back visible)
    2. \n
  2. Silver/Grey car
    3. \n
  3. Dark SUV (closest) Total 3 on right.
    4. \n
\n

The question asks “How many cars are on the right hand side?”. Answer: 3.

\n

Wait, let me look really closely at the car at [693, 477, 755, 584]. Is it actually a car? Yes, I see the rear window, the trunk, and the taillight area. It looks like a sedan. Is there another car in front of it? There’s a faint shape further up the road, near the green light. It’s too indistinct to count. So 3 is the most reasonable answer.

\n

Let me re-verify the “right hand side”. From the perspective of the camera/viewer. Yes, the right side of the image.

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So, 3 cars.

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</think>

\n

Based on the image, there appear to be 3 cars parked on the right-hand side of the street.

\n
    \n
  1. One car is in the immediate foreground on the right (a dark SUV/crossover).
    2. \n
  2. One car is parked just ahead of it (a silver/grey sedan).
    3. \n
  3. One car is parked further ahead near the intersection (a darker sedan).
    4. \n
\n

Qwen3.5

\n

\"image\"

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Thinking with grounding Next

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User

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Question: How many door hinges are located in this image? Options: A. There are 3 door hinges in this image. B. There are 12 door hinges in this image. C. There are 6 door hinges in this image. Please select the correct answer from the options above. \"image\"

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Qwen3.5

\n

<think>

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The user wants me to count the door hinges in the image.

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    \n
  1. \n

    Analyze the image: I see two sets of double doors.

    \n\n
    2. \n
  2. \n

    Examine the left set of doors:

    \n\n
    3. \n
  3. \n

    Examine the right set of doors:

    \n\n
    4. \n
  4. \n

    Count the hinges:

    \n\n
    5. \n
  5. \n

    Check options:

    \n\n
    6. \n
  6. \n

    Conclusion: The correct answer is 12.

    \n
    7. \n
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</think>

\n

The correct answer is B. There are 12 door hinges in this image.

\n

Qwen3.5

\n

\"image\"

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Thinking with grounding Next

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User

\n

Question: Is the car that is currently traversing through the intersection moving towards the left or towards the right? Options: A. Left B. Right Please select the correct answer from the options above. \"image\"

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Qwen3.5

\n

<think>

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The user is asking about the direction of a car traversing the intersection.

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    \n
  1. Identify the car: Looking at the intersection, there is a silver/grey SUV (looks like a Jeep Wrangler) on the left side of the image [0, 469, 137, 597]. It is positioned perpendicular to the camera’s view, facing towards the right side of the frame.
    2. \n
  2. Determine its motion: The car is positioned as if it is entering or crossing the intersection from the left side. Its front is pointing towards the right. Therefore, it is moving towards the right.
    3. \n
  3. Check other cars: There are parked cars on both sides. There’s a white car [313, 473, 372, 545] and a black car [203, 488, 266, 560] facing the camera, waiting or parked. There’s an orange car [493, 476, 558, 545] parked facing away. The only car “traversing” or actively in the process of crossing (or positioned to cross) is the silver Jeep on the left.
    4. \n
  4. Confirm direction: The Jeep is facing right. So it is moving towards the right.
    5. \n
\n

Wait, let me re-evaluate. Is the Jeep moving? It’s in the middle of the intersection entrance. It’s facing right. So if it’s moving, it’s moving right. Let’s look for other moving cars. Further up the hill, cars are parked. The question asks about “the car that is currently traversing through the intersection”. The silver Jeep [0, 469, 137, 597] is the only candidate. It is facing right. Therefore, it is moving towards the right.

\n

Let’s double check.

\n\n

So the only car that could be described as “traversing” is the Jeep on the left. It is facing and moving towards the right side of the image.

\n

Therefore, the answer is Right.

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</think>

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B. Right

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Qwen3.5

\n

\"image\"

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Thinking with grounding Next

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User

\n

Question: Where is the face that has it’s eyes open compared to the other figures? Options: A. It’s in the middle of the 6 other figures. B. It’s on the left side of the 6 other figures. C. It’s in the right side of the 6 other figures. Please select the correct answer from the options above. \"image\"

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Qwen3.5

\n

<think>

\n

The user wants me to identify the location of a specific figure in the image.

\n
    \n
  1. \n

    Analyze the image: I see a collection of 7 small, sculpted heads mounted on a white board inside a glass case.

    \n
    2. \n
  2. \n

    Identify the figures: They all look like pale, child-like faces wearing red hoods with ears (like Little Red Riding Hood or a fox).

    \n
    3. \n
  3. \n

    Check the eyes:

    \n\n
    4. \n
  4. \n

    Determine the position: The figure with open eyes is located in the center of the arrangement. It is surrounded by the other six figures (two above, two below, one to the left, one to the right).

    \n
    5. \n
  5. \n

    Evaluate the options:

    \n\n
    6. \n
  6. \n

    Formulate the final answer: The correct option is A.

    \n
    7. \n
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</think>

\n

A. It’s in the middle of the 6 other figures.

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Qwen3.5

\n

\"image\"

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Thinking with Images

\n

Qwen3.5 can use tools such as a code interpreter and image search during multimodal reasoning. This means it can not only interpret images, but also leverage external tools during its reasoning — transforming images, rendering intermediate reasoning steps, and using visuals to verify its textual results.

\n

Blackwhite Maze Next

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User

\n

画一画，找出红球到绿球的最短路线

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(En: Draw it out and find the shortest path from the red ball to the green ball.) \"image\"

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Qwen3.5

\n

<think>

\n

为了找到从红球到绿球的最短路线，我首先需要将图像转换为一个网格表示，识别出起点（红球）、终点（绿球）以及墙壁和路径。然后，我将使用广度优先搜索（BFS）算法来找到最短路径。

\n

首先，我将读取图像，将其转换为灰度并二值化，以区分墙壁和路径。然后，我将检测红球和绿球的位置。

\n

</think>

\n
import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport collections\n\n# Load the image\nimage = np.array(image_clue[0])\n\n# The maze seems to be a grid. Let's try to determine the grid size.\n# The image has a thick black border.\n# Let's convert to HSV to detect red and green colors for start and end points.\nhsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\n\n# Define range of red colors\nlower_red = np.array([0, 70, 50])\nupper_red = np.array([10, 255, 255])\n# Threshold the HSV image to get red components\nmask1 = cv2.inRange(hsv, lower_red, upper_red)\n# Sometimes red wraps around 180, so check upper end too\nlower_red2 = np.array([160, 70, 50])\nupper_red2 = np.array([180, 255, 255])\nmask2 = cv2.inRange(hsv, lower_red2, upper_red2)\nmask_red = cv2.bitwise_or(mask1, mask2)\n\n# Define range of green colors\nlower_green = np.array([40, 70, 50])\nupper_green = np.array([80, 255, 255])\nmask_green = cv2.inRange(hsv, lower_green, upper_green)\n\n# Find coordinates of red and green pixels\nred_pixels = np.column_stack(np.where(mask_red > 0))\ngreen_pixels = np.column_stack(np.where(mask_green > 0))\n\nprint(f"Red pixels count: {len(red_pixels)}")\nprint(f"Green pixels count: {len(green_pixels)}")\n\n# Let's estimate grid size.\n# The image looks like a square maze. Let's assume a standard size like 15x15 or similar.\nh, w = image.shape[:2]\nprint(f"Image dimensions: {w}x{h}")\n\n# Let's try to detect the grid lines or infer from the structure.\n# The walls are black, paths are white.\ngray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n# Threshold to separate black walls from white paths\n_, binary = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY)\n\n# Let's try to infer grid size by looking at runs of pixels.\n# Or simply assume a size based on visual inspection.\n# Let's count transitions or look at the structure.\n# Let's try to resize the image to various grid sizes and see which one makes sense.\n# Common sizes are 10x10, 15x15, 20x20, etc.\n# Let's try to detect the bounding box of the maze content first.\ncoords = cv2.findNonZero(255 - binary) # Find black pixels (walls)\nx, y, w_maze, h_maze = cv2.boundingRect(coords)\nprint(f"Maze bounding box: x={x}, y={y}, w={w_maze}, h={h_maze}")\n\n# It seems the maze fills most of the image.\n# Let's try to determine grid size by checking common divisors or visual estimation.\n# Let's assume the maze is roughly square.\n# Let's try to print out potential grid sizes.\n# If I look at the image, it seems to be around 17x17 or similar.\n# Let's try to detect lines.\n\n# Let's try a simpler approach: assume the maze is a grid of N x N cells.\n# Let's try to find N such that the cell size is consistent.\n# Let's try N from 10 to 30.\n# The bounding box is likely the whole image or close to it.\n# Let's assume the black border is part of the maze walls.\n\n# Let's try to infer grid size by looking at the image dimensions.\n# 512x512 is common, or similar. Let's check w, h again.\n# Let's try to divide w and h by integers to see if we get a reasonable cell size.\n# Let's guess the grid size is roughly 17x17 based on visual complexity.\n# Let's try to detect the grid by looking at the binary image.\n\n# Let's try to simply assume a grid size and see if it reconstructs well.\n# Let's try to count the number of black/white transitions in the middle row/col.\n\n# Let's try to detect the grid size by checking the run lengths in the binary image.\ndef estimate_grid_size(binary_img):\n    # Scan middle row and column\n    mid_row = binary_img[h//2, :]\n    mid_col = binary_img[:, w//2]\n    \n    # This might be noisy. Let's try to just guess based on image size.\n    # Let's print image size first.\n    return\n\nprint(f"Image shape: {image.shape}")\n\n# Let's try to visualize the masks to confirm start and end points.\nplt.figure(figsize=(15, 5))\n\nplt.subplot(1, 3, 1)\nplt.imshow(image)\nplt.title(f"Original Image ({w}x{h})")\nplt.axis('off')\n\nplt.subplot(1, 3, 2)\nplt.imshow(mask_red, cmap='gray')\nplt.title(f"Red Mask (Start): {len(red_pixels)} pixels")\nplt.axis('off')\n\nplt.subplot(1, 3, 3)\nplt.imshow(mask_green, cmap='gray')\nplt.title(f"Green Mask (End): {len(green_pixels)} pixels")\nplt.axis('off')\n\nplt.tight_layout()\nplt.show()\n\n# Calculate centroids for start and end\nif len(red_pixels) > 0:\n    start_y, start_x = np.mean(red_pixels, axis=0).astype(int)\n    print(f"Start point (pixel): ({start_x}, {start_y})")\nelse:\n    start_x, start_y = -1, -1\n\nif len(green_pixels) > 0:\n    end_y, end_x = np.mean(green_pixels, axis=0).astype(int)\n    print(f"End point (pixel): ({end_x}, {end_y})")\nelse:\n    end_x, end_y = -1, -1\n
\n

User

\n

Red pixels count: 279 Green pixels count: 238 Image dimensions: 612x606 Maze bounding box: x=15, y=15, w=587, h=586 Image shape: (606, 612, 3) \"image\" Start point (pixel): (61, 60) End point (pixel): (555, 554)

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Qwen3.5

\n

<think>

\n

图像已成功加载并处理。我检测到了红球和绿球的位置，以及迷宫的边界框。

\n\n

接下来，我需要确定迷宫的网格大小。从边界框的宽度和高度（587x586）来看，这似乎是一个正方形区域。通常迷宫是由相同大小的单元格组成的。让我尝试推断网格的尺寸。 假设迷宫是一个 N x N 的网格。 587 和 586 非常接近。如果这是一个 17x17 的网格（包括墙壁），每个单元格大约是 34-35 像素。 让我尝试通过检查图像中的线条或简单的除法来确定网格大小。 587 / 17 = 34.52… 586 / 17 = 34.47… 这看起来不太像整数。 让我们尝试其他常见的尺寸，比如 15x15, 19x19, 21x21 等。 或者，我可以尝试根据红球和绿球的位置以及边界框来推断。 红球在左上角，绿球在右下角。 让我尝试将边界框内的图像分割成网格，并尝试几种可能的网格尺寸，看看哪种能产生清晰的黑白模式。 考虑到边界框是 587x586，这可能意味着最外圈是墙壁。 让我们尝试检测边缘或简单的阈值来估算单元格大小。 或者，我可以假设网格大小是使得 587 和 586 能被整除的数。 587 是质数吗？587 / 1 = 587. 586 = 2 * 293. 这有点奇怪。也许边界框检测有轻微偏差，或者迷宫本身不是完美的正方形网格，或者最外层的黑边厚度不同。 让我重新检查边界框。也许我应该直接对整个图像进行网格化，或者尝试找到一个更规则的子区域。 让我们尝试打印出边界框内图像的一些统计信息，或者尝试假设一个网格大小并可视化它。 常见的迷宫尺寸如 10x10, 15x15, 20x20 等。 如果单元格大小是 34 像素，17 * 34 = 578。 如果单元格大小是 35 像素，17 * 35 = 595。 如果单元格大小是 30 像素，19 * 30 = 570，20 * 30 = 600。 让我们尝试自动检测网格大小。我可以计算水平和垂直方向上的投影，或者寻找重复的模式。 另一种方法是，假设迷宫由黑色墙壁和白色路径组成。 让我尝试将边界框内的图像调整大小到不同的网格尺寸，看看哪个看起来最清晰。 但首先，让我确认边界框是否准确。 从显示的裁剪图来看，边界框似乎包含了整个迷宫区域。 让我们尝试假设网格大小为 17x17 或 19x19 或类似，并查看平均颜色。 或者，我可以简单地尝试将图像划分为 N x N 个单元格，其中 N 使得单元格大小接近整数。 587 和 586 非常接近 588 (which is 1734.58… no, 1734=578, 1735=595). 也许网格是 15x15? 587/15 = 39.13. 也许是 20x20? 587/20 = 29.35. 也许是 21x21? 587/21 = 27.95. 接近 28. 2128 = 588. 如果是 21x21 的网格，每个单元格 28 像素，总宽 588。边界框宽 587，非常接近。 如果是 21x21 的网格，每个单元格 28 像素，总高 588。边界框高 586，也非常接近。 这很有希望。让我尝试用 21x21 的网格来解析迷宫。 单元格大小约为 28 像素。 起始点 (61, 60) 相对于边界框 (15, 15) 是 (46, 45)。 46/28 = 1.64, 45/28 = 1.60. 这看起来像是在 (1,1) 或 (2,2) 附近。 等等，红球是在左上角的白色路径里。通常迷宫的入口/出口在边缘。 看原图，红球在左上角的一个白色格子里，周围有黑边。 让我们尝试用代码来确认网格大小和解析迷宫。 我将尝试几种可能的网格尺寸（例如 17x17, 19x19, 21x21），并检查哪种尺寸产生的二值化网格最一致（即每个单元格要么是纯黑，要么是纯白，或者有明显的中心点颜色）。

\n

修正思路：

\n
    \n
  1. 提取边界框内的图像。
    2. \n
  2. 尝试将图像划分为 N x N 网格。
    3. \n
  3. 对于每个网格单元，计算平均颜色。
    4. \n
  4. 根据颜色判断是墙壁还是路径。
    5. \n
  5. 确定起点和终点的网格坐标。
    6. \n
  6. 使用 BFS 寻找最短路径。
    7. \n
  7. 绘制路径。
    8. \n
\n

我将首先尝试推断网格大小。 边界框宽 587，高 586。 可能的网格尺寸 N 和单元格大小 S： N=17, S=34.5 N=19, S=30.9 N=21, S=27.95 -> 28 N=29, S=20.2 N=31, S=18.9 让我尝试 N=21, S=28。总尺寸 588x588。边界框 587x586。误差很小，可能是裁剪或抗锯齿造成的。 让我尝试 N=17, S=34.5 (不太可能)。 让我尝试 N=15, S=39.1. 让我尝试 N=29, S=20. 让我尝试 N=30, S=19.5.

\n

我将编写代码来尝试 N=21 (S=28) 和 N=17 (S=34) 等，并可视化网格以确认。 同时，我也需要处理起点和终点。红球和绿球可能占据整个单元格或部分单元格。 从掩膜图看，红球和绿球是明显的圆点。 我将使用之前检测到的红球和绿球中心坐标来定位它们在网格中的位置。 相对坐标： Start: (61-15, 60-15) = (46, 45) End: (555-15, 554-15) = (540, 539)

\n

如果 S=28: Start cell: (46/28, 45/28) = (1.64, 1.60) -> 可能是 (1,1) 或 (2,2) 索引？ End cell: (540/28, 539/28) = (19.28, 19.25) -> 可能是 (19,19) 或 (20,20)？ 如果网格是 21x21，索引 0-20。 (1,1) 和 (19,19) 看起来是合理的内部位置，或者靠近边缘。 看原图，红球在左上角第二个格子（如果第一个是黑边）？或者就是角落的格子。 通常迷宫最外圈是墙壁。 如果最外圈是墙壁，那么路径从 (1,1) 开始。 让我们尝试用代码来验证。

\n

</think>

\n
import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport collections\n\n# Load the image\nimage = np.array(image_clue[0])\n\n# The maze seems to be a grid. Let's try to determine the grid size.\n# The image has a thick black border.\n# Let's convert to HSV to detect red and green colors for start and end points.\nhsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)\n\n# Define range of red colors\nlower_red = np.array([0, 70, 50])\nupper_red = np.array([10, 255, 255])\n# Threshold the HSV image to get red components\nmask1 = cv2.inRange(hsv, lower_red, upper_red)\n# Sometimes red wraps around 180, so check upper end too\nlower_red2 = np.array([160, 70, 50])\nupper_red2 = np.array([180, 255, 255])\nmask2 = cv2.inRange(hsv, lower_red2, upper_red2)\nmask_red = cv2.bitwise_or(mask1, mask2)\n\n# Define range of green colors\nlower_green = np.array([40, 70, 50])\nupper_green = np.array([80, 255, 255])\nmask_green = cv2.inRange(hsv, lower_green, upper_green)\n\n# Find coordinates of red and green pixels\nred_pixels = np.column_stack(np.where(mask_red > 0))\ngreen_pixels = np.column_stack(np.where(mask_green > 0))\n\nprint(f"Red pixels count: {len(red_pixels)}")\nprint(f"Green pixels count: {len(green_pixels)}")\n\n# Let's estimate grid size.\n# The image looks like a square maze. Let's assume a standard size like 15x15 or similar.\nh, w = image.shape[:2]\nprint(f"Image dimensions: {w}x{h}")\n\n# Let's try to detect the grid lines or infer from the structure.\n# The walls are black, paths are white.\ngray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n# Threshold to separate black walls from white paths\n_, binary = cv2.threshold(gray, 128, 255, cv2.THRESH_BINARY)\n\n# Let's try to infer grid size by looking at runs of pixels.\n# Or simply assume a size based on visual inspection.\n# Let's count transitions or look at the structure.\n# Let's try to resize the image to various grid sizes and see which one makes sense.\n# Common sizes are 10x10, 15x15, 20x20, etc.\n# Let's try to detect the bounding box of the maze content first.\ncoords = cv2.findNonZero(255 - binary) # Find black pixels (walls)\nx, y, w_maze, h_maze = cv2.boundingRect(coords)\nprint(f"Maze bounding box: x={x}, y={y}, w={w_maze}, h={h_maze}")\n\n# It seems the maze fills most of the image.\n# Let's try to determine grid size by checking common divisors or visual estimation.\n# Let's assume the maze is roughly square.\n# Let's try to print out potential grid sizes.\n# If I look at the image, it seems to be around 17x17 or similar.\n# Let's try to detect lines.\n\n# Let's try a simpler approach: assume the maze is a grid of N x N cells.\n# Let's try to find N such that the cell size is consistent.\n# Let's try N from 10 to 30.\n# The bounding box is likely the whole image or close to it.\n# Let's assume the black border is part of the maze walls.\n\n# Let's try to infer grid size by looking at the image dimensions.\n# 512x512 is common, or similar. Let's check w, h again.\n# Let's try to divide w and h by integers to see if we get a reasonable cell size.\n# Let's guess the grid size is roughly 17x17 based on visual complexity.\n# Let's try to detect the grid by looking at the binary image.\n\n# Let's try to simply assume a grid size and see if it reconstructs well.\n# Let's try to count the number of black/white transitions in the middle row/col.\n\n# Let's try to detect the grid size by checking the run lengths in the binary image.\ndef estimate_grid_size(binary_img):\n    # Scan middle row and column\n    mid_row = binary_img[h//2, :]\n    mid_col = binary_img[:, w//2]\n    \n    # This might be noisy. Let's try to just guess based on image size.\n    # Let's print image size first.\n    return\n\nprint(f"Image shape: {image.shape}")\n\n# Let's try to visualize the masks to confirm start and end points.\nplt.figure(figsize=(15, 5))\n\nplt.subplot(1, 3, 1)\nplt.imshow(image)\nplt.title(f"Original Image ({w}x{h})")\nplt.axis('off')\n\nplt.subplot(1, 3, 2)\nplt.imshow(mask_red, cmap='gray')\nplt.title(f"Red Mask (Start): {len(red_pixels)} pixels")\nplt.axis('off')\n\nplt.subplot(1, 3, 3)\nplt.imshow(mask_green, cmap='gray')\nplt.title(f"Green Mask (End): {len(green_pixels)} pixels")\nplt.axis('off')\n\nplt.tight_layout()\nplt.show()\n\n# Calculate centroids for start and end\nif len(red_pixels) > 0:\n    start_y, start_x = np.mean(red_pixels, axis=0).astype(int)\n    print(f"Start point (pixel): ({start_x}, {start_y})")\nelse:\n    start_x, start_y = -1, -1\n\nif len(green_pixels) > 0:\n    end_y, end_x = np.mean(green_pixels, axis=0).astype(int)\n    print(f"End point (pixel): ({end_x}, {end_y})")\nelse:\n    end_x, end_y = -1, -1\n
\n

User

\n

Grid size: 21x21, Cell size: 28 Cluster centers: [[ 47.1935085 ] [200.60667539]] Start pixel (relative): (46, 45) -> Grid: (1, 1) End pixel (relative): (540, 539) -> Grid: (19, 19) Maze grid shape: (21, 21) Start cell value: 1, End cell value: 1 Path found: [(1, 1), (1, 2), (1, 3), (2, 3), (3, 3), (4, 3), (5, 3), (6, 3), (7, 3), (8, 3), (8, 4), (8, 5), (8, 6), (8, 7), (8, 8), (7, 8), (6, 8), (5, 8), (4, 8), (3, 8), (2, 8), (1, 8), (1, 9), (1, 10), (1, 11), (1, 12), (2, 12), (3, 12), (3, 13), (3, 14), (4, 14), (5, 14), (6, 14), (6, 13), (6, 12), (6, 11), (6, 10), (7, 10), (8, 10), (8, 11), (8, 12), (9, 12), (10, 12), (10, 11), (10, 10), (10, 9), (10, 8), (10, 7), (10, 6), (11, 6), (12, 6), (12, 5), (12, 4), (12, 3), (11, 3), (10, 3), (10, 2), (10, 1), (11, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1), (17, 2), (17, 3), (18, 3), (19, 3), (19, 4), (19, 5), (19, 6), (19, 7), (19, 8), (18, 8), (17, 8), (17, 9), (16, 9), (16, 10), (15, 10), (14, 10), (13, 10), (12, 10), (12, 11), (12, 12), (13, 12), (14, 12), (15, 12), (16, 12), (17, 12), (17, 13), (17, 14), (18, 14), (19, 14), (19, 15), (19, 16), (19, 17), (19, 18), (19, 19)] \"image\" Start point (pixel): (61, 60) End point (pixel): (555, 554)

\n

Qwen3.5

\n

<think>

\n

代码成功解析了迷宫并找到了路径。

\n\n

路径看起来是连贯的，并且遵循白色路径。 我现在需要生成最终的图像，将路径绘制在原始图像上，或者像右侧那样显示解决方案。 用户要求“画一画，找出红球到绿球的最短路线”。 我将创建一个图像，在原始迷宫上叠加红色的路径线。 为了更美观，我可以将路径坐标转换回像素坐标并绘制线条或填充单元格。 由于我已经有了网格化的表示，我可以直接在调整大小后的图像上绘制，或者映射回原始坐标。 考虑到原始图像有边界框，我将把路径映射回原始图像的坐标系。

\n

映射逻辑： 网格坐标 (r, c) 对应于边界框内的像素区域。 边界框左上角：(15, 15) 单元格大小：28 网格单元 (r, c) 的中心像素坐标（相对于边界框）：(c28 + 14, r28 + 14) 加上边界框偏移：(15 + c28 + 14, 15 + r28 + 14) = (c28 + 29, r28 + 29) 或者简单地绘制填充矩形。

\n

让我在原始图像上绘制路径。 我将使用 OpenCV 在原始图像副本上绘制红色线条或矩形。 为了清晰，我将绘制连接路径中每个单元格中心的线条，线宽设为单元格大小的一半或更小。 或者，我可以简单地填充路径经过的单元格。 让我们尝试绘制连接中心的线条，颜色为红色，线宽为 10 像素左右。

\n

首先，我需要重新加载原始图像（或者使用之前加载的），然后应用路径绘制。 由于我无法直接访问之前的变量，我需要在新的代码块中重新定义必要的变量或重新运行部分代码。 不过，我可以简单地使用之前计算出的路径坐标和参数。 路径坐标：path 变量。 边界框：(15, 15) 单元格大小：28

\n

我将编写代码来：

\n
    \n
  1. 加载原始图像。
    2. \n
  2. 定义路径坐标列表。
    3. \n
  3. 将路径坐标转换为像素坐标。
    4. \n
  4. 在图像上绘制路径。
    5. \n
  5. 显示结果。
    6. \n
\n

</think>

\n
import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Load the original image\nimage_path = image_clue[0]\nimage = np.array(image_path)\n\n# Parameters from previous step\nbbox_x, bbox_y = 15, 15\ncell_size = 28\n# Path coordinates from previous execution\npath_coords = [\n    (1, 1), (1, 2), (1, 3), (2, 3), (3, 3), (4, 3), (5, 3), (6, 3), (7, 3), (8, 3), \n    (8, 4), (8, 5), (8, 6), (8, 7), (8, 8), (7, 8), (6, 8), (5, 8), (4, 8), (3, 8), \n    (2, 8), (1, 8), (1, 9), (1, 10), (1, 11), (1, 12), (2, 12), (3, 12), (3, 13), \n    (3, 14), (4, 14), (5, 14), (6, 14), (6, 13), (6, 12), (6, 11), (6, 10), (7, 10), \n    (8, 10), (8, 11), (8, 12), (9, 12), (10, 12), (10, 11), (10, 10), (10, 9), (10, 8), \n    (10, 7), (10, 6), (11, 6), (12, 6), (12, 5), (12, 4), (12, 3), (11, 3), (10, 3), \n    (10, 2), (10, 1), (11, 1), (12, 1), (13, 1), (14, 1), (15, 1), (16, 1), (17, 1), \n    (17, 2), (17, 3), (18, 3), (19, 3), (19, 4), (19, 5), (19, 6), (19, 7), (19, 8), \n    (18, 8), (17, 8), (17, 9), (16, 9), (16, 10), (15, 10), (14, 10), (13, 10), (12, 10), \n    (12, 11), (12, 12), (13, 12), (14, 12), (15, 12), (16, 12), (17, 12), (17, 13), \n    (17, 14), (18, 14), (19, 14), (19, 15), (19, 16), (19, 17), (19, 18), (19, 19)\n]\n\n# Create a copy of the image to draw on\nimage_copy = image.copy()\n\n# Function to get pixel center from grid coordinate\ndef get_pixel_center(r, c):\n    x = bbox_x + c * cell_size + cell_size // 2\n    y = bbox_y + r * cell_size + cell_size // 2\n    return (x, y)\n\n# Draw the path\n# We can draw lines between centers or fill rectangles. Filling rectangles might look blocky but accurate.\n# Drawing lines with a thick width is usually smoother.\npoints = [get_pixel_center(r, c) for r, c in path_coords]\npoints_np = np.array(points, dtype=np.int32).reshape((-1, 1, 2))\n\n# Draw lines\n# Color: Red (255, 0, 0)\n# Thickness: 10\ncv2.polylines(image_copy, [points_np], isClosed=False, color=(255, 0, 0), thickness=12)\n\n# Also mark start and end points clearly\n# Start: Green (0, 255, 0) - wait, original start is red, end is green.\n# Let's keep original colors or use distinct ones. The user asked to find the route from red to green.\n# I'll draw the path in a distinct color, maybe blue or keep red but thicker.\n# Let's use a bright magenta or just red with high thickness.\n# Let's re-draw with a slightly different color to stand out, e.g., (0, 255, 255) Cyan or (255, 0, 255) Magenta.\n# But red is fine if it's thick enough. Let's use (255, 50, 50) to be slightly different from the start dot.\n# Actually, let's just use the red color (255, 0, 0) as requested "red ball to green ball".\n# Let's re-draw with thickness 14 to cover the path well.\ncv2.polylines(image_copy, [points_np], isClosed=False, color=(255, 0, 0), thickness=14)\n\n# Highlight start and end\nstart_pt = get_pixel_center(1, 1)\nend_pt = get_pixel_center(19, 19)\ncv2.circle(image_copy, start_pt, 8, (255, 0, 0), -1) # Red start\ncv2.circle(image_copy, end_pt, 8, (0, 128, 0), -1) # Green end\n\nplt.figure(figsize=(15, 15))\nplt.imshow(image_copy)\nplt.axis('off')\nplt.tight_layout()\nplt.show()\n
\n

User

\n

\"image\"

\n

Qwen3.5

\n

<think>

\n

代码成功执行，并在原始图像上绘制了从红球到绿球的最短路径（红色线条）。 路径看起来正确地沿着白色通道行进，避开了黑色墙壁。 起点和终点也正确对应。 现在我可以向用户展示结果并简要说明。

\n

</think>

\n

这是从红球到绿球的最短路线图：

\n

\"Solved

\n

解题步骤：

\n
    \n
  1. 图像识别：首先识别出迷宫的边界、墙壁（黑色区域）和路径（白色区域），并定位红球（起点）和绿球（终点）的位置。
    2. \n
  2. 网格化：将迷宫划分为 21x21 的网格，每个单元格大小为 28x28 像素。
    3. \n
  3. 路径搜索：使用广度优先搜索（BFS）算法在网格中寻找从起点到终点的最短路径。
    4. \n
  4. 绘制结果：将找到的路径坐标映射回原始图像像素坐标，并绘制红色线条。
    5. \n
\n

红色线条即为最短路线，共经过 99 个步骤。

\n

Find the difference Next

\n

User

\n

There is one unique different icon in the figure. In which row and column is it located? Note: Both row and column numbers start from 1, counting from top to bottom as row 1, row 2, etc., and from left to right as column 1, column 2, etc. Please output a two-number list in the format [row, column]. For example, [1, 2] means the first row and the second column.

\n

Put the final answer into the \\boxed{}. \"image\"

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Qwen3.5

\n

<think>

\n

To locate the unique different icon in the image, I’ll first preprocess the image to segment the individual icons. Then, I’ll compare each icon to find the one that is different from the others. I’ll start by loading the image, converting it to grayscale, and then thresholding to separate the icons from the background. After that, I’ll find contours to identify the grid cells and extract each cell’s image. Finally, I’ll compare the extracted cells to find the outlier.

\n

</think>

\n
import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sklearn.cluster\n\n# Load the image\nimage = np.array(image_clue[0])\ngray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n\n# Threshold to separate the yellow icons from the purple background\n# The background is purple, and icons are yellow. I'll use Otsu's thresholding after Gaussian blurring.\nblur = cv2.GaussianBlur(gray, (5, 5), 0)\nret, thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n\n# Find contours to detect individual icons\ncontours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n# Collect bounding boxes\nboxes = []\nfor cnt in contours:\n    area = cv2.contourArea(cnt)\n    if area > 50: # Filter small noise\n        x, y, w, h = cv2.boundingRect(cnt)\n        boxes.append((x, y, w, h))\n\n# Sort boxes to form a grid\n# First, sort by y-coordinate to group into rows\n# I need to determine a tolerance to group boxes into the same row\nboxes_sorted_y = sorted(boxes, key=lambda k: k[1])\nrows = []\nif boxes_sorted_y:\n    current_row = [boxes_sorted_y[0]]\n    for i in range(1, len(boxes_sorted_y)):\n        prev_box = current_row[-1]\n        curr_box = boxes_sorted_y[i]\n        # If the y-difference is small, they are in the same row\n        if abs(curr_box[1] - prev_box[1]) > 10: # Adjust tolerance if needed\n            # Sort current row by x-coordinate and add to rows\n            current_row_sorted = sorted(current_row, key=lambda k: k[0])\n            rows.append(current_row_sorted)\n            current_row = []\n        current_row.append(curr_box)\n    # Add last row\n    current_row_sorted = sorted(current_row, key=lambda k: k[0])\n    rows.append(current_row_sorted)\n\nprint(f"Number of rows: {len(rows)}")\nfor i, row in enumerate(rows):\n    print(f"Row {i+1}: {len(row)} columns")\n\n# Extract each icon and find the outlier\n# I'll resize all extracted icons to a common size and then flatten them to find the outlier\nicon_images = []\npositions = []\nfor r_idx, row in enumerate(rows):\n    for c_idx, box in enumerate(row):\n        x, y, w, h = box\n        roi = image[y:y+h, x:x+w]\n        roi_resized = cv2.resize(roi, (20, 20)) # Resize to a standard size\n        icon_images.append(roi_resized.flatten())\n        positions.append((r_idx + 1, c_idx + 1))\n\nicon_images_np = np.array(icon_images)\n\n# Use K-means with K=2 to find the outlier, assuming one is different\n# Or simply find the one with the largest distance from the median\nmedian_icon = np.median(icon_images_np, axis=0)\ndistances = np.linalg.norm(icon_images_np - median_icon, axis=1)\noutlier_idx = np.argmax(distances)\noutlier_pos = positions[outlier_idx]\n\nprint(f"Outlier found at: Row {outlier_pos[0]}, Column {outlier_pos[1]}")\n\n# Visualize to confirm\noutlier_roi = image[rows[outlier_pos[0]-1][outlier_pos[1]-1][1]:rows[outlier_pos[0]-1][outlier_pos[1]-1][1]+rows[outlier_pos[0]-1][outlier_pos[1]-1][3],\n                   rows[outlier_pos[0]-1][outlier_pos[1]-1][0]:rows[outlier_pos[0]-1][outlier_pos[1]-1][0]+rows[outlier_pos[0]-1][outlier_pos[1]-1][2]]\n\nplt.figure(figsize=(10, 5))\n\nplt.subplot(1, 2, 1)\nplt.imshow(image)\nplt.title(f"Full Image. Outlier at {outlier_pos}")\nplt.axis('off')\n\nplt.subplot(1, 2, 2)\nplt.imshow(outlier_roi)\nplt.title(f"Outlier Icon at {outlier_pos}")\nplt.axis('off')\n\nplt.tight_layout()\nplt.show()\n
\n

User

\n

Number of rows: 1 Row 1: 1 columns Outlier found at: Row 1, Column 1 \"image\"

\n

Qwen3.5

\n

<think>

\n

The previous code failed to segment the grid correctly, detecting only 1 row with 1 column. This is likely because the thresholding or contour finding didn’t separate the individual icons properly, possibly due to the text at the top or the color similarity. The displayed image shows the full image, confirming the segmentation failed.

\n

Let’s re-examine the image. The background is purple, and the icons are yellow squares with a small white/lighter square inside. The text “Eye Test Challenge” is at the top.

\n

I need to:

\n
    \n
  1. Crop out the text at the top to avoid interference.
    2. \n
  2. Re-apply thresholding and contour detection.
    3. \n
  3. Sort the bounding boxes into a grid.
    4. \n
  4. Find the outlier.
    5. \n
\n

Let’s try to crop the bottom 90% of the image first, assuming the text is at the very top. Or, I can try to detect contours and filter by size and position.

\n

Let’s print the image shape and try a simple threshold again, perhaps visualizing the thresholded image to tune parameters.

\n

</think>

\n
import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Load image\nimage = np.array(image_clue[0])\nh, w = image.shape[:2]\n\n# The text is at the top. Let's crop the bottom 90% to focus on the grid.\n# Visually, the grid starts a bit down. Let's try cropping from 10% height.\nstart_y = int(h * 0.05)\ncropped_image = image[start_y:, :]\n\n# Convert to grayscale\ngray = cv2.cvtColor(cropped_image, cv2.COLOR_RGB2GRAY)\n\n# The icons are yellow, background is purple.\n# Let's try Otsu thresholding on the cropped image.\nblur = cv2.GaussianBlur(gray, (5, 5), 0)\nret, thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)\n\n# Find contours\ncontours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n# Collect bounding boxes\nboxes = []\nfor cnt in contours:\n    area = cv2.contourArea(cnt)\n    x, y, w_box, h_box = cv2.boundingRect(cnt)\n    # Filter based on area to exclude noise and potentially the text if it wasn't cropped enough\n    # The icons seem to be small squares. Let's guess a reasonable size range.\n    # If the grid is roughly 15x15, and image is say 800x800, each cell is ~50x50.\n    if 100 < area < 2500: # Adjust as needed\n        boxes.append((x, y + start_y, w_box, h_box)) # Adjust y coordinate back to original image\n\nprint(f"Number of detected boxes: {len(boxes)}")\n\n# Sort boxes into a grid\n# Sort by y first\nboxes_sorted_y = sorted(boxes, key=lambda k: k[1])\nrows = []\nif boxes_sorted_y:\n    current_row = [boxes_sorted_y[0]]\n    for i in range(1, len(boxes_sorted_y)):\n        prev_box = current_row[-1]\n        curr_box = boxes_sorted_y[i]\n        # Heuristic: if y difference is significant, start new row\n        # Let's estimate row height from median height of boxes\n        median_h = np.median([b[3] for b in boxes])\n        if abs(curr_box[1] - prev_box[1]) > median_h / 2:\n            current_row_sorted = sorted(current_row, key=lambda k: k[0])\n            rows.append(current_row_sorted)\n            current_row = []\n        current_row.append(curr_box)\n    current_row_sorted = sorted(current_row, key=lambda k: k[0])\n    rows.append(current_row_sorted)\n\nprint(f"Number of rows: {len(rows)}")\nfor i, row in enumerate(rows):\n    print(f"Row {i+1}: {len(row)} columns, boxes: {row[:3]}...")\n\n# Extract and find outlier\nif len(rows) > 1:\n    icon_images = []\n    positions = []\n    for r_idx, row in enumerate(rows):\n        for c_idx, box in enumerate(row):\n            x, y, wb, hb = box\n            roi = image[y:y+hb, x:x+wb]\n            roi_resized = cv2.resize(roi, (30, 30))\n            icon_images.append(roi_resized.flatten())\n            positions.append((r_idx + 1, c_idx + 1))\n    \n    icon_images_np = np.array(icon_images)\n    median_icon = np.median(icon_images_np, axis=0)\n    distances = np.linalg.norm(icon_images_np - median_icon, axis=1)\n    outlier_idx = np.argmax(distances)\n    outlier_pos = positions[outlier_idx]\n    \n    print(f"Outlier found at: Row {outlier_pos[0]}, Column {outlier_pos[1]}")\n    \n    # Visualize\n    outlier_box = rows[outlier_pos[0]-1][outlier_pos[1]-1]\n    x, y, wb, hb = outlier_box\n    outlier_roi = image[y:y+hb, x:x+wb]\n    \n    plt.figure(figsize=(15, 5))\n    \n    plt.subplot(1, 3, 1)\n    plt.imshow(thresh, cmap='gray')\n    plt.title(f"Thresholded Image. Rows: {len(rows)}, Cols: {len(rows[0]) if rows else 0}")\n    plt.axis('off')\n    \n    plt.subplot(1, 3, 2)\n    plt.imshow(image)\n    plt.title(f"Full Image. Outlier at {outlier_pos}")\n    plt.axis('off')\n    \n    plt.subplot(1, 3, 3)\n    plt.imshow(outlier_roi)\n    plt.title(f"Outlier Icon at {outlier_pos}")\n    plt.axis('off')\n    \n    plt.tight_layout()\n    plt.show()\nelse:\n    print("Grid detection failed. Displaying debug info.")\n    plt.figure(figsize=(15, 5))\n    plt.subplot(1, 2, 1)\n    plt.imshow(cropped_image)\n    plt.title(f"Cropped Image (from y={start_y})")\n    plt.axis('off')\n    plt.subplot(1, 2, 2)\n    plt.imshow(thresh, cmap='gray')\n    plt.title(f"Thresholded Cropped Image. Num contours: {len(contours)}")\n    plt.axis('off')\n    plt.tight_layout()\n    plt.show()\n
\n

User

\n

Number of detected boxes: 0 Number of rows: 0 Grid detection failed. Displaying debug info. \"image\"

\n

Qwen3.5

\n

<think>

\n

The previous attempt failed because the area filter 100 < area < 2500 was too restrictive or the coordinate adjustment was wrong. The debug image shows that the thresholding on the cropped image works well (black squares on white background, or vice versa). The thresholded image clearly shows the grid of icons. The number of contours is reported as 5, which is very low, likely because the findContours is finding the large white regions or the black regions depending on the inversion, but the area filter eliminated them.

\n

Looking at the thresholded image in the debug output (right side), the icons are white squares on a black background (or black on white, need to check). Wait, the displayed thresholded image has white icons on black background. Let’s re-examine. In cv2.threshold(..., cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU), if the background is dark and objects are light, THRESH_BINARY makes objects white. If background is light, THRESH_BINARY_INV makes objects white. The original image has a purple background and yellow icons. Purple is darker than yellow. So after grayscale, background is dark, icons are light. THRESH_BINARY_INV would make dark regions white and light regions black. This is counter-intuitive if I want icons to be white. Let’s look at the debug image again. The right subplot shows black squares on a white background? No, it looks like white squares on a black background, but the text “Eye Test Challenge” is also visible at the top as black text on white? Or maybe the cropping didn’t remove the text fully and the thresholding is weird. Actually, looking at the right subplot, it seems the icons are black rectangles on a white background? No, let’s look closer. The background of the subplot is black, and there are white shapes. Wait, the subplot title is “Thresholded Cropped Image”. The shapes look like the icons. Let’s just re-run the contour detection on the cropped image with a broader area range and visualize the contours. Also, the coordinate start_y was int(h * 0.05). The text might still be there. Let’s try to detect contours on the full grayscale image, filter by area, and then cluster their y-coordinates to find rows.

\n

Let’s refine the cropping. The text is at the top. The grid seems to occupy most of the image. Let’s try to detect all contours on the cropped image without strict area filtering first, print some stats, and then filter. Also, looking at the debug image, the cropped image starts at y=33. The text “Eye Test Challenge” is partially visible. Let’s try to crop more aggressively, say from 10% or just manually inspect the image dimensions. Let’s assume the grid is regular.

\n

Let’s re-run contour detection on the cropped_image (which is image[start_y:, :]). The thresholded image shown has white shapes on black background (or vice versa). Let’s assume the icons are the objects of interest. Let’s try cv2.THRESH_BINARY instead of INV if the icons are brighter. Let’s just try to find contours on the thresholded image shown in the debug plot. It seems to have successfully segmented the icons (the grid is visible). The issue is likely the area filtering or the findContours mode. Wait, the debug output says “Num contours: 5”. But the image shows many squares. This is strange. Maybe findContours is finding the large black background as one contour and some noise? If the thresholded image has white icons on black background, findContours on the binary image will find the white regions. Let’s check the thresholding logic again. cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU) If background (purple) is darker than icons (yellow), then in grayscale, background < icons. Otsu finds a threshold T. THRESH_BINARY: src(x,y) > T ? 255 : 0. So icons become 255 (white), background 0 (black). THRESH_BINARY_INV: src(x,y) > T ? 0 : 255. So icons become 0 (black), background 255 (white). The debug image shows black squares on a white background? No, it’s hard to tell from the small thumbnail, but if there are only 5 contours, maybe it found the large white background and some noise. Let’s try cv2.THRESH_BINARY to make icons white.

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Let’s also print the image shape to understand dimensions. And let’s try to detect contours on the full image, but ignore the top part by y-coordinate.

\n

Let’s rewrite the detection logic:

\n
    \n
  1. Load image, convert to gray.
    2. \n
  2. Crop bottom 90% to remove text.
    3. \n
  3. Threshold with Otsu (try both BINARY and BINARY_INV).
    4. \n
  4. Find contours.
    5. \n
  5. Filter contours by area (e.g., between 50 and 1000 pixels, need to estimate).
    6. \n
  6. Sort into grid.
    7. \n
  7. Find outlier.
    8. \n
\n

Let’s also print the number of contours found before filtering to debug. And visualize the contours on the image.

\n

From the visual appearance, the grid is roughly 15x15 or similar. Let’s count rows and columns visually from the provided image. It looks like roughly 15 rows and 21 columns? Or 15x15? Let’s just let the code determine it.

\n

Let’s try again with better parameters.

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</think>

\n
import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Load image\nimage = np.array(image_clue[0])\nh, w = image.shape[:2]\nprint(f"Image shape: {h}x{w}")\n\n# Crop to remove the title text at the top.\n# Based on visual, the title is about 5-10% of the height.\n# Let's try cropping from 8% down.\nstart_y = int(h * 0.08)\ncropped_image = image[start_y:, :]\ncropped_h, cropped_w = cropped_image.shape[:2]\nprint(f"Cropped shape: {cropped_h}x{cropped_w}, start_y: {start_y}")\n\n# Convert to grayscale\ngray = cv2.cvtColor(cropped_image, cv2.COLOR_RGB2GRAY)\n\n# Apply Gaussian blur\nblur = cv2.GaussianBlur(gray, (5, 5), 0)\n\n# Apply Otsu's thresholding.\n# Since icons are yellow (bright) and background is purple (darker),\n# THRESH_BINARY should make icons white and background black.\nret, thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n\n# Find contours\ncontours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\nprint(f"Total contours found: {len(contours)}")\n\n# Collect bounding boxes\nboxes = []\nfor cnt in contours:\n    area = cv2.contourArea(cnt)\n    x, y, wb, hb = cv2.boundingRect(cnt)\n    # Adjust coordinates to original image\n    y_orig = y + start_y\n    # Filter noise. Icons seem to be small squares.\n    # Let's collect all and then decide, or use a loose filter.\n    # Assuming icons are not tiny noise and not the whole image.\n    if 50 < area < 5000: # Loose bounds\n        boxes.append((x, y_orig, wb, hb))\n\nprint(f"Number of boxes after filtering: {len(boxes)}")\n\n# If boxes are found, sort them\nif len(boxes) > 10:\n    # Sort by y to group into rows\n    boxes_sorted_y = sorted(boxes, key=lambda k: k[1])\n    rows = []\n    current_row = [boxes_sorted_y[0]]\n    for i in range(1, len(boxes_sorted_y)):\n        prev_box = current_row[-1]\n        curr_box = boxes_sorted_y[i]\n        # Determine if new row based on y-difference\n        # Use a threshold, e.g., half the median height of boxes\n        median_h = np.median([b[3] for b in boxes])\n        if abs(curr_box[1] - prev_box[1]) > median_h * 0.5:\n            # Sort current row by x and add\n            current_row_sorted = sorted(current_row, key=lambda k: k[0])\n            rows.append(current_row_sorted)\n            current_row = []\n        current_row.append(curr_box)\n    # Add last row\n    current_row_sorted = sorted(current_row, key=lambda k: k[0])\n    rows.append(current_row_sorted)\n    \n    print(f"Detected grid: {len(rows)} rows")\n    for i, row in enumerate(rows):\n        print(f"Row {i+1}: {len(row)} columns")\n    \n    # Extract icons and find outlier\n    icon_images = []\n    positions = []\n    for r_idx, row in enumerate(rows):\n        for c_idx, box in enumerate(row):\n            x, y, wb, hb = box\n            roi = image[y:y+hb, x:x+wb]\n            # Resize to a common size for comparison\n            roi_resized = cv2.resize(roi, (30, 30))\n            icon_images.append(roi_resized.flatten())\n            positions.append((r_idx + 1, c_idx + 1))\n    \n    icon_images_np = np.array(icon_images)\n    # Find outlier using distance from median\n    median_icon = np.median(icon_images_np, axis=0)\n    distances = np.linalg.norm(icon_images_np - median_icon, axis=1)\n    outlier_idx = np.argmax(distances)\n    outlier_pos = positions[outlier_idx]\n    \n    print(f"Outlier at: Row {outlier_pos[0]}, Column {outlier_pos[1]}")\n    \n    # Visualize\n    outlier_box = rows[outlier_pos[0]-1][outlier_pos[1]-1]\n    x, y, wb, hb = outlier_box\n    outlier_roi = image[y:y+hb, x:x+wb]\n    \n    plt.figure(figsize=(20, 5))\n    \n    plt.subplot(1, 3, 1)\n    plt.imshow(thresh, cmap='gray')\n    plt.title(f"Thresholded (Binary). Rows: {len(rows)}, Cols: {len(rows[0]) if rows else 0}")\n    plt.axis('off')\n    \n    plt.subplot(1, 3, 2)\n    # Draw rectangles on original image to verify detection\n    debug_img = image.copy()\n    for r_idx, row in enumerate(rows):\n        for c_idx, box in enumerate(row):\n            x, y, wb, hb = box\n            cv2.rectangle(debug_img, (x, y), (x+wb, y+hb), (0, 255, 0), 1)\n    # Highlight outlier\n    x, y, wb, hb = outlier_box\n    cv2.rectangle(debug_img, (x, y), (x+wb, y+hb), (255, 0, 0), 2)\n    plt.imshow(debug_img)\n    plt.title(f"Detected Grid. Outlier: {outlier_pos}")\n    plt.axis('off')\n    \n    plt.subplot(1, 3, 3)\n    plt.imshow(outlier_roi)\n    plt.title(f"Outlier Icon: Row {outlier_pos[0]}, Col {outlier_pos[1]}")\n    plt.axis('off')\n    \n    plt.tight_layout()\n    plt.show()\nelse:\n    print("Not enough boxes found. Trying different threshold or crop.")\n    # Display threshold to debug\n    plt.figure(figsize=(15, 5))\n    plt.subplot(1, 2, 1)\n    plt.imshow(cropped_image)\n    plt.title(f"Cropped Image (y>{start_y})")\n    plt.axis('off')\n    plt.subplot(1, 2, 2)\n    plt.imshow(thresh, cmap='gray')\n    plt.title(f"Thresholded Image. Contours: {len(contours)}")\n    plt.axis('off')\n    plt.tight_layout()\n    plt.show()\n
\n

User

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Image shape: 672x736 Cropped shape: 619x736, start_y: 53 Total contours found: 330 Number of boxes after filtering: 330 Detected grid: 15 rows Row 1: 22 columns Row 2: 22 columns Row 3: 22 columns Row 4: 22 columns Row 5: 22 columns Row 6: 22 columns Row 7: 22 columns Row 8: 22 columns Row 9: 22 columns Row 10: 22 columns Row 11: 22 columns Row 12: 22 columns Row 13: 22 columns Row 14: 22 columns Row 15: 22 columns Outlier at: Row 5, Column 12 \"image\"

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Qwen3.5

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<think>

\n

The code detected a 15x22 grid. However, looking at the thresholded image (left subplot), the grid seems to be 15 rows and roughly 21 or 22 columns. Wait, let me count visually from the original image or the thresholded one. Looking at the thresholded image, I can count the columns. Let’s look at the outlier image. It shows a purple square with a lighter border, but it’s very blurry and zoomed in. The outlier detection points to (5, 12). Let’s visually verify the grid size from the original image. Counting columns in the original image: Row 1: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21. It looks like 21 columns. Let’s re-count. The code says 22 columns. This might be due to noise or splitting one icon into two. Let’s look at the thresholded image again. The white blobs look distinct. Let’s count the blobs in the first row of the thresholded image. Also, the outlier image shown is very zoomed in and blurry, making it hard to confirm if it’s the correct one. The red box in the middle image highlights a specific icon. Let’s look at that icon in the middle image. It seems to be a square with a different inner pattern or color. However, if the grid detection is wrong (22 vs 21 columns), the row/column indices will be wrong. Let’s re-examine the bounding boxes. Maybe some icons are split or noise is detected as an icon. The total number of contours is 330. 15 * 22 = 330. This is a very consistent number, suggesting the detection is stable, but maybe the column count is indeed 22? Or maybe 15*21 = 315 and there are 15 extra noise detections? Let’s look at the original image again. Let’s count columns visually from the provided image. Top row: 1(yellow), 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21. It looks like 21 columns to me. Let me count again carefully. Let’s use code to count unique x-coordinates or cluster them to determine the number of columns. Also, the outlier at (5, 12) in a 22-column grid would be different from (5, 12) in a 21-column grid if the extra column is inserted somewhere. Let’s look at the middle image with the red box. The red box is around the 12th item in the 5th row (visually counting from the image). Let’s count rows and columns from the middle image visually. The red box is in the 5th row from the top (excluding the title). Counting columns from left: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12. The red box is indeed at the 12th position. Wait, if there are 21 columns, the middle is around 11. 12 is slightly right of center. Let’s verify the total count. 15 rows * 21 columns = 315 items. The code found 330 items. 330 / 15 = 22. So either there are 22 columns, or there are 15 noise detections (1 per row?). Let’s look at the thresholded image again. The blobs look uniform. Maybe I should re-run the detection with a check on bounding box sizes to filter out potential splits or noise. Or, I can simply look at the image provided in the middle subplot. The red rectangle clearly highlights one specific icon. I can visually count its position from that image. Let’s count rows and columns from the middle subplot image. Rows: 1, 2, 3, 4, 5 (the one with red box). Columns: Let’s count from left. 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12. It seems the code’s detection of (5, 12) might be correct even if the total column count is debatable, as long as the grid structure is preserved left-to-right. But wait, if there are 21 columns and the code thinks 22, where is the extra one? If the code splits one icon into two, the indices after that split will be shifted. Let’s check the bounding box widths. If most are ~20px and one is ~10px, that’s a split. Let’s print statistics of bounding box widths and heights. Also, let’s look at the outlier icon image again. It looks like a purple square with a lighter border. The other icons are yellow squares with a lighter border? Or maybe the “different” one is a square outline vs filled? Looking at the original image, most icons are yellow squares with a small white square inside (like a picture frame). The different one might be just a square outline or a different color. In the middle subplot, the highlighted icon (red box) looks like a small white square outline, while others are yellow filled squares? It’s hard to tell at that resolution. Let’s look at the thresholded image (left). Most blobs are solid white rectangles. One blob in row 5 seems to have a hole or is different? In the left subplot (thresholded), row 5, somewhere in the middle, there is a blob that looks like a square ring (white border, black center) while others are solid white? Or vice versa? Actually, looking closely at the left subplot, row 5, column ~12, there is a small black square inside the white blob? No, it looks like a white square with a black dot? Let’s re-examine the original image. The icons are yellow squares with a white inner square. The “different” one might be a square with a different inner color or shape. Let’s trust the outlier detection but verify the grid dimensions. 330 items detected. 15 rows. 330/15 = 22. Let’s count columns in the original image again very carefully. I will write code to count columns by clustering x-coordinates.

\n

Also, let’s look at the outlier image (right subplot). It’s a 30x30 resized image. It looks purple with some lighter borders. This is strange because the background is purple and icons are yellow. If the outlier is detected as different, and it looks purple, maybe it’s a missing icon or a different color icon. Wait, if the outlier is at (5,12) and the image shows a purple-ish patch, maybe that’s the background? But the bounding box should have captured an icon. Let’s look at the middle image again. The red box is around a small white square. The other icons are yellow. This suggests the outlier is indeed a white square (or empty square) among yellow squares. Let’s confirm the position by counting on the original image. I’ll count columns on the original image by loading it and displaying a zoomed in crop of the top-left area to count manually.

\n

Let’s also re-run the grid detection, printing unique widths to see if 22 is correct or if there’s a split. And I’ll try to count columns by looking at the x-coordinates of the boxes.

\n

One more thing: 15 rows * 21 columns = 315. 330 - 315 = 15. This suggests exactly 1 extra detection per row. This is a strong hint that one icon per row is being split into two, or there’s a systematic error. If one icon is split into two in each row, the column index would be off by the number of splits before that column. However, if the split is consistent (e.g., always the last one, or always a specific one), the relative order might be preserved but indices shifted. But 15 extra detections is suspicious. Maybe the grid is indeed 22 columns? Let’s count visually from the image. Row 1: [icon][icon]… Let’s use python to count.

\n

Let’s refine the plan:

\n
    \n
  1. Re-detect contours.
    2. \n
  2. Print histogram of bounding box widths.
    3. \n
  3. Cluster x-coordinates to find number of unique columns.
    4. \n
  4. Re-calculate row/col indices based on correct grid size.
    5. \n
  5. Verify the outlier location.
    6. \n
\n

Also, looking at the provided solution image (middle), the red box is clearly visible. I can try to count columns from that image visually. Let’s assume the code’s row detection (15 rows) is correct. Let’s assume the outlier is indeed the one highlighted. I need to be sure about the column count. Let’s try to count columns from the original image by simple visual estimation or code. The image width is 736. If 21 columns, width ~35. If 22 columns, width ~33. The detected boxes have widths. I can check the median width. If median width is W, and total width is 736, then N_cols ~ 736/W.

\n

Let’s execute code to analyze bounding boxes and re-determine grid.

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</think>

\n
import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans\n\n# Load image\nimage = np.array(image_clue[0])\nh, w = image.shape[:2]\n\n# Use parameters from previous successful detection\nstart_y = int(h * 0.08)\ncropped_image = image[start_y:, :]\ngray = cv2.cvtColor(cropped_image, cv2.COLOR_RGB2GRAY)\nblur = cv2.GaussianBlur(gray, (5, 5), 0)\nret, thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n\n# Find contours\ncontours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\nboxes = []\nfor cnt in contours:\n    area = cv2.contourArea(cnt)\n    x, y, wb, hb = cv2.boundingRect(cnt)\n    y_orig = y + start_y\n    if 50 < area < 5000:\n        boxes.append((x, y_orig, wb, hb))\n\nprint(f"Number of boxes: {len(boxes)}")\n\n# Analyze widths and heights\nwidths = [b[2] for b in boxes]\nheights = [b[3] for b in boxes]\nprint(f"Median width: {np.median(widths)}, Median height: {np.median(heights)}")\nprint(f"Unique widths (sorted): {sorted(list(set(widths)))[:10]}...")\nprint(f"Unique heights (sorted): {sorted(list(set(heights)))[:10]}...")\n\n# Sort boxes to form grid\nboxes_sorted_y = sorted(boxes, key=lambda k: k[1])\nrows = []\nif boxes_sorted_y:\n    current_row = [boxes_sorted_y[0]]\n    for i in range(1, len(boxes_sorted_y)):\n        prev_box = current_row[-1]\n        curr_box = boxes_sorted_y[i]\n        median_h = np.median(heights)\n        if abs(curr_box[1] - prev_box[1]) > median_h * 0.5:\n            current_row_sorted = sorted(current_row, key=lambda k: k[0])\n            rows.append(current_row_sorted)\n            current_row = []\n        current_row.append(curr_box)\n    current_row_sorted = sorted(current_row, key=lambda k: k[0])\n    rows.append(current_row_sorted)\n\nprint(f"Number of rows: {len(rows)}")\ncol_counts = [len(r) for r in rows]\nprint(f"Columns per row: {col_counts}")\n\n# If column counts are uniform, proceed. If not, investigate.\n# Assuming uniform for now to find outlier\nif len(set(col_counts)) == 1:\n    n_cols = col_counts[0]\n    print(f"Grid size: {len(rows)}x{n_cols}")\n    \n    # Extract and find outlier\n    icon_images = []\n    positions = []\n    for r_idx, row in enumerate(rows):\n        for c_idx, box in enumerate(row):\n            x, y, wb, hb = box\n            roi = image[y:y+hb, x:x+wb]\n            roi_resized = cv2.resize(roi, (30, 30))\n            icon_images.append(roi_resized.flatten())\n            positions.append((r_idx + 1, c_idx + 1))\n    \n    icon_images_np = np.array(icon_images)\n    median_icon = np.median(icon_images_np, axis=0)\n    distances = np.linalg.norm(icon_images_np - median_icon, axis=1)\n    \n    # Find top 5 outliers to inspect\n    top_k_indices = np.argsort(distances)[::-1][:5]\n    for idx in top_k_indices:\n        pos = positions[idx]\n        print(f"Outlier candidate: Row {pos[0]}, Col {pos[1]}, Distance: {distances[idx]}")\n        \n        # Visualize top outlier\n        if idx == top_k_indices[0]:\n            outlier_box = rows[pos[0]-1][pos[1]-1]\n            x, y, wb, hb = outlier_box\n            outlier_roi = image[y:y+hb, x:x+wb]\n            \n            plt.figure(figsize=(10, 5))\n            plt.subplot(1, 2, 1)\n            plt.imshow(outlier_roi)\n            plt.title(f"Top Outlier at {pos}")\n            plt.axis('off')\n            \n            # Also show a typical icon for comparison\n            typical_box = rows[0][0]\n            xt, yt, wbt, hbt = typical_box\n            typical_roi = image[yt:yt+hbt, xt:xt+wbt]\n            plt.subplot(1, 2, 2)\n            plt.imshow(typical_roi)\n            plt.title(f"Typical Icon at (1,1)")\n            plt.axis('off')\n            plt.tight_layout()\n            plt.show()\nelse:\n    print("Grid is not uniform. Investigating row lengths.")\n
\n

User

\n

Number of boxes: 330 Median width: 20.0, Median height: 24.0 Unique widths (sorted): [20]… Unique heights (sorted): [24]… Number of rows: 15 Columns per row: [22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22, 22] Grid size: 15x22 Outlier candidate: Row 5, Col 12, Distance: 4484.878147731552

\n

\"image\"

\n

Outlier candidate: Row 10, Col 22, Distance: 485.6737588134652 Outlier candidate: Row 10, Col 15, Distance: 482.7711673246446 Outlier candidate: Row 10, Col 4, Distance: 482.7711673246446 Outlier candidate: Row 10, Col 3, Distance: 482.7711673246446

\n

Qwen3.5

\n

<think>

\n

The code detects a 15x22 grid. The outlier is at Row 5, Column 12. The visual comparison shows:

\n\n

Also, let’s look at the outlier image (purple square) vs typical (yellow square). In the original image, I can see a purple square among yellow ones. Let’s locate it visually. Row 1: all yellow. Row 2: all yellow. Row 3: all yellow. Row 4: all yellow. Row 5: … somewhere in the middle … there is a purple square! Let’s count columns in Row 5 of the original image. 1(y), 2(y), 3(y), 4(y), 5(y), 6(y), 7(y), 8(y), 9(y), 10(y), 11(y), 12(purple?), 13(y)… Let’s count carefully on the original image. The purple square is the one that looks like an empty frame or just the background color inside. Wait, the outlier image shows a purple center. The typical icon shows a yellow center. So I’m looking for a purple-centered square. Scanning row 5: Icons look yellow, yellow, yellow… Let’s count from the left in the original image. The grid seems to start after some margin. Let’s rely on the code’s row index (5) which seems robust. Now for the column index. If the code says 12 in a 22-col grid, and true is 21-col grid. If 1 extra per row, maybe the extra is at the end? Then index 12 is correct. If the extra is at the beginning? Then index 11. Let’s check the x-coordinates clustering.

\n

One more check: 15 rows * 21 cols = 315. 330 detected. 330/15 = 22. Could it be 15 rows * 22 cols? Let’s look at the aspect ratio of the whole grid area. If 21 cols: width ~ 2120 + 2014 = 420 + 280 = 700. (assuming 14px gaps). If 22 cols: width ~ 2220 + 2114 = 440 + 294 = 734. Image width is 736. This matches 22 columns much better! With 21 columns, we’d have 700 width, leaving 36 pixels margin (18 each side). With 22 columns, we’d have 734 width, leaving 2 pixels margin (1 each side). Given the image fills the frame, 22 columns is very plausible. Let’s re-count visually. Maybe I missed one. Let’s assume 22 columns is correct. Then the outlier is at Row 5, Column 12.

\n

Let’s double check the visual count again. Maybe the icons are closer together. Let’s trust the code’s detection of 22 columns, supported by the width calculation (736px width fits 2220 + gaps better than 2120 + gaps if gaps are small). Wait, if gaps are 0, 22*20 = 440. Still leaves 300px. Let’s look at the thresholded image again. The white blobs are separated by black. The gaps seem significant. Let’s re-evaluate: If 21 cols, width 736. Avg cell width (including gap) = 736/21 = 35.05. If 22 cols, width 736. Avg cell width = 736/22 = 33.45. Detected box width is 20. So gap would be 15px (for 21 cols) or 13.45px (for 22 cols). Both are plausible. However, the code found exactly 22 boxes per row with width 20. If there were 21 icons and 1 noise per row, the noise would also have to be width 20 and area similar to icons. This is unlikely to happen consistently 15 times. It is more likely that there are indeed 22 columns. My visual count of 21 might be off by one due to poor resolution or counting error. Let’s assume the code is correct: 15 rows, 22 columns. Outlier at Row 5, Column 12.

\n

Let’s verify the outlier visual again. The code shows “Top Outlier at (5, 12)” is purple-centered. “Typical Icon at (1,1)” is yellow-centered. This matches the task description “unique different icon”. So the location is Row 5, Column 12.

\n

Wait, I should confirm the row count too. Code says 15 rows. Visual count of rows: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15. Yes, 15 rows seems correct. So the grid is 15x22. The outlier is at [5, 12].

\n

Let me try to count columns one more time from the original image crop shown in the second execution (middle subplot). The red box is at row 5. Counting columns from left to right in that image: The red box is the 12th one? Let’s count: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, [12-red]. Yes, it looks like the 12th one. And the total number of columns in that row seems to be more than 12*2=24? No, maybe 20-22. Let’s count the remaining after 12: 13, 14, 15, 16, 17, 18, 19, 20, 21, 22. It looks like there are about 10 more to the right. 12+10 = 22. This supports the 22 column hypothesis. My initial visual count of 21 was likely a mistake.

\n

Therefore, the outlier is at Row 5, Column 12.

\n

Final check:

\n\n

One minor point: The problem asks for [row, column]. My code outputs (5, 12). I will output [5, 12].

\n

Wait, let’s look at the outlier image again. The purple square has a border. The yellow square has a border. The difference is the fill color. The code’s outlier detection is based on pixel difference, which is robust here. The position (5, 12) is consistent with visual inspection of the debug image.

\n

One more sanity check. What if the grid is 15x21 and the code detected an extra column of noise on the right? If so, the indices 1-21 would be shifted or correct? If noise is at the end, indices 1-21 are correct. If noise is at the beginning, indices are shifted by 1. Given the uniform width of 20 for all 330 boxes, it’s highly unlikely to be noise. Noise would likely have different size. So 22 columns is the most probable truth.

\n

Final Answer seems to be [5, 12].

\n

</think>

\n

Based on the code analysis, the image contains a grid of icons with 15 rows and 22 columns. The code detected 330 icons (15 * 22). The outlier detection identified the icon at Row 5, Column 12 as significantly different from the others. Visual inspection of the debug images confirms that the icon at this position has a purple center, while the typical icons have a yellow center. The grid dimensions and the position of the outlier are consistent with the visual evidence in the debug plots.

\n

\\boxed{[5, 12]}

\n

Soduku Next

\n

User

\n

解一下这个数独问题 \"image\"

\n

Qwen3.5

\n

<think>

\n

为了解决这个数独问题，我将使用 Python 代码来识别图像中的数字，然后使用回溯算法来求解数独。

\n

首先，我将读取图像，将其转换为灰度，然后使用 OCR 或简单的图像处理技术来提取每个单元格中的数字。由于这是一个标准的 9x9 数独，我可以将图像分割成 81 个小格，并尝试识别每个格子里的数字。

\n

如果 OCR 效果不佳，我将手动根据图像内容创建一个二维数组来表示数独的初始状态。

\n

让我们先尝试加载图像并显示它，以便确认图像内容。然后我将编写代码来求解数独。

\n

</think>

\n
import cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Load the image\nimage = np.array(image_clue[0])\nplt.figure(figsize=(15, 15))\nplt.imshow(image)\nplt.axis('off')\nplt.tight_layout()\nplt.show()\n\n# Since OCR might be unreliable on simple grid images without clear bounding boxes,\n# I will manually transcribe the sudoku grid from the image into a 2D list.\n# 0 represents an empty cell.\n\nsudoku_grid = [\n    [0, 1, 8, 5, 0, 0, 4, 2, 6],\n    [6, 5, 3, 0, 8, 0, 1, 7, 0],\n    [0, 7, 0, 9, 1, 0, 5, 3, 0],\n    [2, 3, 0, 0, 0, 0, 0, 0, 5],\n    [0, 6, 4, 3, 2, 0, 0, 1, 0],\n    [0, 9, 0, 0, 0, 0, 0, 0, 0],\n    [0, 0, 0, 0, 0, 0, 6, 8, 0],\n    [0, 0, 6, 0, 7, 2, 3, 0, 0],\n    [0, 0, 1, 0, 4, 0, 7, 0, 0]\n]\n\ndef print_grid(grid):\n    for row in grid:\n        print(row)\n\nprint("Initial Sudoku Grid:")\nprint_grid(sudoku_grid)\n\ndef is_valid(grid, row, col, num):\n    # Check row\n    for x in range(9):\n        if grid[row][x] == num:\n            return False\n\n    # Check column\n    for x in range(9):\n        if grid[x][col] == num:\n            return False\n\n    # Check 3x3 box\n    start_row = row - row % 3\n    start_col = col - col % 3\n    for i in range(3):\n        for j in range(3):\n            if grid[i + start_row][j + start_col] == num:\n                return False\n    return True\n\ndef solve_sudoku(grid):\n    for i in range(9):\n        for j in range(9):\n            if grid[i][j] == 0:\n                for num in range(1, 10):\n                    if is_valid(grid, i, j, num):\n                        grid[i][j] = num\n                        if solve_sudoku(grid):\n                            return True\n                        grid[i][j] = 0\n                return False\n    return True\n\n# Solve the sudoku\nif solve_sudoku(sudoku_grid):\n    print("\\nSolved Sudoku Grid:")\n    print_grid(sudoku_grid)\nelse:\n    print("No solution exists")\n
\n

User

\n

\"image\" Initial Sudoku Grid: [0, 1, 8, 5, 0, 0, 4, 2, 6] [6, 5, 3, 0, 8, 0, 1, 7, 0] [0, 7, 0, 9, 1, 0, 5, 3, 0] [2, 3, 0, 0, 0, 0, 0, 0, 5] [0, 6, 4, 3, 2, 0, 0, 1, 0] [0, 9, 0, 0, 0, 0, 0, 0, 0] [0, 0, 0, 0, 0, 0, 6, 8, 0] [0, 0, 6, 0, 7, 2, 3, 0, 0] [0, 0, 1, 0, 4, 0, 7, 0, 0]

\n

Solved Sudoku Grid: [9, 1, 8, 5, 3, 7, 4, 2, 6] [6, 5, 3, 2, 8, 4, 1, 7, 9] [4, 7, 2, 9, 1, 6, 5, 3, 8] [2, 3, 7, 4, 9, 1, 8, 6, 5] [8, 6, 4, 3, 2, 5, 9, 1, 7] [1, 9, 5, 7, 6, 8, 2, 4, 3] [7, 2, 9, 1, 5, 3, 6, 8, 4] [5, 4, 6, 8, 7, 2, 3, 9, 1] [3, 8, 1, 6, 4, 9, 7, 5, 2]

\n

Qwen3.5

\n

<think>

\n

代码已经成功识别了数独网格并求解。 初始网格被正确识别为： [0, 1, 8, 5, 0, 0, 4, 2, 6] [6, 5, 3, 0, 8, 0, 1, 7, 0] [0, 7, 0, 9, 1, 0, 5, 3, 0] [2, 3, 0, 0, 0, 0, 0, 0, 5] [0, 6, 4, 3, 2, 0, 0, 1, 0] [0, 9, 0, 0, 0, 0, 0, 0, 0] [0, 0, 0, 0, 0, 0, 6, 8, 0] [0, 0, 6, 0, 7, 2, 3, 0, 0] [0, 0, 1, 0, 4, 0, 7, 0, 0]

\n

求解后的网格为： [9, 1, 8, 5, 3, 7, 4, 2, 6] [6, 5, 3, 2, 8, 4, 1, 7, 9] [4, 7, 2, 9, 1, 6, 5, 3, 8] [2, 3, 7, 4, 9, 1, 8, 6, 5] [8, 6, 4, 3, 2, 5, 9, 1, 7] [1, 9, 5, 7, 6, 8, 2, 4, 3] [7, 2, 9, 1, 5, 3, 6, 8, 4] [5, 4, 6, 8, 7, 2, 3, 9, 1] [3, 8, 1, 6, 4, 9, 7, 5, 2]

\n

我将把这个结果格式化输出给用户。

\n

</think>

\n

这个数独问题的解如下：

\n
9 1 8 | 5 3 7 | 4 2 6\n6 5 3 | 2 8 4 | 1 7 9\n4 7 2 | 9 1 6 | 5 3 8\n------+-------+------\n2 3 7 | 4 9 1 | 8 6 5\n8 6 4 | 3 2 5 | 9 1 7\n1 9 5 | 7 6 8 | 2 4 3\n------+-------+------\n7 2 9 | 1 5 3 | 6 8 4\n5 4 6 | 8 7 2 | 3 9 1\n3 8 1 | 6 4 9 | 7 5 2\n
\n

Sliding Puzzle Next

\n

User

\n

图中展示了滑动方块游戏的两个棋盘：左边是当前状态 (current state)，右边是目标状态 (target state)。 请给出从左边棋盘（当前状态）变到右边棋盘（目标状态）的最短移动路径。 移动路径由 ‘块ID+方向’ 序列组成。方向使用 U (上), D (下), L (左), R (右)。 例如：‘1U1L2D’ 表示1号块向上移一格，再向左移一格，然后2号块向下移一格。 如果有多条最短路径，请按块ID从小到大、方向(U,D,L,R)的顺序优先选择。 \"image\"

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Qwen3.5

\n

<think>

\n

为了解决这个问题，我需要编写一个Python脚本来模拟滑动方块游戏。 首先，我需要定义两个棋盘的状态：当前状态（左图）和目标状态（右图）。 然后，我将使用广度优先搜索（BFS）算法来找到从当前状态到目标状态的最短路径。 为了确保在有多个最短路径时选择特定的路径（块ID从小到大，方向U,D,L,R优先），我需要在BFS扩展节点时按照这个顺序进行。

\n

第一步：定义棋盘状态 我将把棋盘看作一个网格。根据图片，网格大概是 5行 x 4列（或者类似的比例，让我仔细观察）。 让我们数一下格子。 左图：

\n\n

让我们尝试构建一个坐标系。 看左图： Row 0: [3, 3, 5, 1, 1, 0] (假设宽是6) -> 3是2x2, 5是1x1, 1是1x2, 0是空 Row 1: [3, 3, 2, 2, 1, 14] -> 14是1x2 Row 2: [9, 9, 2, 2, 13, 14] -> 9是2x1, 13是1x1 Row 3: [7, 7, 0, 10, 4, 4] -> 7是2x1, 10是1x1, 4是2x2 Row 4: [8, 8, 6, 6, 4, 4] -> 8是2x1, 6是2x2 Row 5: [11, 11, 6, 6, 12, 12] -> 11是2x1, 12是2x1

\n

这看起来总共有6行？ 让我们重新数一下左图的行。

\n
    \n
  1. 第一行：3(部分), 5, 1(部分), 空
    2. \n
  2. 第二行：3(部分), 2(部分), 1(部分), 14(部分)
    3. \n
  3. 第三行：9, 2(部分), 13, 14(部分)
    4. \n
  4. 第四行：7, 空, 10, 4(部分)
    5. \n
  5. 第五行：8, 6(部分), 4(部分)
    6. \n
  6. 第六行：11, 6(部分), 12
    7. \n
\n

这看起来有点乱。让我用代码来辅助识别或者手动仔细构建矩阵。 让我们假设网格是 5行 x 6列。 左图： R0: [3, 3, 5, 1, 1, 0] (0代表空) R1: [3, 3, 2, 2, 1, 14] R2: [9, 9, 2, 2, 13, 14] R3: [7, 7, 0, 10, 4, 4] R4: [8, 8, 6, 6, 4, 4] R5: [11, 11, 6, 6, 12, 12] 这个布局看起来非常合理。 总行数：6，总列数：6。 块的大小： 3: 2x2 (0,0) 5: 1x1 (0,2) 1: 1x2 (0,3) 14: 1x2 (1,5) 2: 2x2 (1,2) 9: 2x1 (2,0) 13: 1x1 (2,4) 7: 2x1 (3,0) 10: 1x1 (3,3) 4: 2x2 (3,4) 8: 2x1 (4,0) 6: 2x2 (4,2) 11: 2x1 (5,0) 12: 2x1 (5,4)

\n

让我们验证一下右图（目标状态）： R0: [3, 3, 0, 5, 1, 1] R1: [3, 3, 2, 2, 1, 14] R2: [9, 9, 2, 2, 13, 14] R3: [7, 7, 6, 6, 4, 4] R4: [0, 10, 6, 6, 4, 4] R5: [8, 8, 11, 11, 12, 12]

\n

让我们再次仔细检查右图的布局。 R0: 3(2x2), 空, 5(1x1), 1(1x2) R1: 3(2x2), 2(2x2), 1(1x2), 14(1x2) R2: 9(2x1), 2(2x2), 13(1x1), 14(1x2) R3: 7(2x1), 6(2x2), 4(2x2) R4: 空, 10(1x1), 6(2x2), 4(2x2) R5: 8(2x1), 11(2x1), 12(2x1)

\n

等等，右图最下面一行： 左边是8 (2x1)，然后是11 (2x1)，然后是12 (2x1)。 如果是这样，那么R5应该是 [8, 8, 11, 11, 12, 12]。 R4: 左边是空(1x1)? 然后是10(1x1)? 然后是6(2x2)的下半部分? 然后是4(2x2)的下半部分? 让我们看右图R3: 7(2x1), 6(2x2上半), 4(2x2上半)。 R4: 空(1x1), 10(1x1), 6(2x2下半), 4(2x2下半)。 R5: 8(2x1), 11(2x1), 12(2x1)。 这似乎不对齐。 让我们重新看右图。 R0: [3, 3, 0, 5, 1, 1] -> 3是2x2, 5是1x1, 1是1x2 R1: [3, 3, 2, 2, 1, 14] -> 2是2x2, 14是1x2 R2: [9, 9, 2, 2, 13, 14] -> 9是2x1, 13是1x1 R3: [7, 7, 6, 6, 4, 4] -> 7是2x1, 6是2x2, 4是2x2 R4: [0, 10, 6, 6, 4, 4] -> 0是空, 10是1x1 R5: [8, 8, 11, 11, 12, 12] -> 8是2x1, 11是2x1, 12是2x1

\n

这个布局看起来是 6行 x 6列。 让我们确认一下列数。 R0: 3(2), 0(1), 5(1), 1(2) -> 2+1+1+2 = 6. 正确。 R1: 3(2), 2(2), 1(1), 14(1) -> 2+2+1+1 = 6. 正确。 R2: 9(2), 2(2), 13(1), 14(1) -> 2+2+1+1 = 6. 正确。 R3: 7(2), 6(2), 4(2) -> 2+2+2 = 6. 正确。 R4: 0(1), 10(1), 6(2), 4(2) -> 1+1+2+2 = 6. 正确。 R5: 8(2), 11(2), 12(2) -> 2+2+2 = 6. 正确。

\n

所以网格是 6x6。

\n

现在我要编写代码来：

\n
    \n
  1. 定义初始状态和目标状态的矩阵。
    2. \n
  2. 实现BFS搜索。
    3. \n
  3. 定义移动规则：块可以上下左右移动，前提是移动方向上的空间足够且为空。
    4. \n
  4. 记录路径。
    5. \n
\n

注意：由于块有不同大小，移动时需要检查整个块占据的格子移动后是否都在边界内且为空。 为了简化，我可以把状态表示为每个块的左上角坐标，或者整个网格的矩阵。 考虑到块的大小不一，用网格矩阵表示状态可能更直观，但移动时需要知道哪个数字代表哪个块。 在这个问题中，每个块有一个唯一的ID（数字）。 所以我可以用一个 6x6 的矩阵，其中每个元素是块ID。 移动时，找到块ID的所有格子，计算它们的新位置，检查是否合法。

\n

让我们定义初始状态矩阵 (Start State): [[3, 3, 5, 1, 1, 0], [3, 3, 2, 2, 1, 14], [9, 9, 2, 2, 13, 14], [7, 7, 0, 10, 4, 4], [8, 8, 6, 6, 4, 4], [11, 11, 6, 6, 12, 12]]

\n

目标状态矩阵 (Target State): [[3, 3, 0, 5, 1, 1], [3, 3, 2, 2, 1, 14], [9, 9, 2, 2, 13, 14], [7, 7, 6, 6, 4, 4], [0, 10, 6, 6, 4, 4], [8, 8, 11, 11, 12, 12]]

\n

等等，让我再仔细看一下右图的R4和R5。 右图R4: 左边是空的，然后是10。 右图R5: 左边是8，然后是11，然后是12。 如果R4左边是空(1x1)，那么R5左边是8(2x1)。 这意味着R4[0]是空，R5[0]是8。 但是8是2x1的块，占据R5[0]和R5[1]。 那R4[0]上面的R3[0]是7(2x1)，占据R3[0]和R3[1]。 所以R4[0]确实是空的。 但是R4[1]是10(1x1)。 R5[2]是11(2x1)的左半部分。 这看起来没问题。

\n

让我再次确认左图的R3, R4, R5。 左图R3: [7, 7, 0, 10, 4, 4] -> 7(2x1), 空(1x1), 10(1x1), 4(2x2上半) 左图R4: [8, 8, 6, 6, 4, 4] -> 8(2x1), 6(2x2上半), 4(2x2下半) 左图R5: [11, 11, 6, 6, 12, 12] -> 11(2x1), 6(2x2下半), 12(2x1) 这里有个问题： 如果4是2x2，它占据R3[4], R3[5], R4[4], R4[5]。 如果6是2x2，它占据R4[2], R4[3], R5[2], R5[3]。 如果8是2x1，它占据R4[0], R4[1]。 如果11是2x1，它占据R5[0], R5[1]。 如果12是2x1，它占据R5[4], R5[5]。 这样R4就是: [8, 8, 6, 6, 4, 4]。 R5就是: [11, 11, 6, 6, 12, 12]。 这与我的矩阵一致。

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现在看右图。 R3: [7, 7, 6, 6, 4, 4] -> 7(2x1), 6(2x2上半), 4(2x2上半) R4: [0, 10, 6, 6, 4, 4] -> 空(1x1), 10(1x1), 6(2x2下半), 4(2x2下半) R5: [8, 8, 11, 11, 12, 12] -> 8(2x1), 11(2x1), 12(2x1) 这与我的矩阵一致。

\n

好的，现在我可以编写代码了。 BFS状态表示：元组(tuple) of tuples。 移动生成： 对于每个块ID，尝试向U, D, L, R移动。 移动合法性检查：

\n
    \n
  1. 获取块的所有坐标。
    2. \n
  2. 计算新坐标。
    3. \n
  3. 检查新坐标是否在边界内。
    4. \n
  4. 检查新坐标位置是否为空（即原矩阵中为0的位置，或者移动后不会与其他块重叠）。 更简单的检查方法：\n\n
    5. \n
\n

为了确保“最短路径”且“块ID从小到大、方向优先”，BFS队列应该按层扩展。 在每一层，我应该按照块ID顺序，方向顺序生成邻居。 但是BFS通常是用队列。如果我按顺序生成邻居并加入队列，第一次到达目标状态的路径就是最短的。 如果有多个最短路径，我需要确保先访问到的路径是符合字典序（块ID+方向）的。 由于BFS是按层遍历，第一次遇到目标状态时的路径长度是最短的。 为了处理“多条最短路径”的选择问题，我可以在生成邻居时，严格按照块ID从小到大，方向U,D,L,R的顺序。 这样，如果同一层有多个状态能到达目标，先被生成的那个（也就是路径字典序更小的那个）会先被处理。 但是，这里有一个细微差别：不同的移动序列可能导致相同的状态。 例如：1U 2L 和 2L 1U 可能到达同一个状态。 题目要求输出移动序列。如果有多条最短路径，按块ID从小到大、方向优先。 这意味着如果路径A是 “1U2L”，路径B是 “2L1U”，且长度相同，应该选 “1U2L”。 这实际上是在比较路径字符串的字典序。 标准的BFS如果不加特殊处理，找到的第一条路径取决于邻居生成的顺序。 如果我在生成邻居时，严格按照 (block_id, direction) 的顺序，那么BFS找到的第一条路径应该就是符合要求的。 因为对于同一个状态，先尝试小的block_id和优先的方向。 但是，不同的路径可能到达同一个中间状态。 例如： Start -> A (via 1U) -> Target Start -> B (via 2L) -> Target 如果1U和2L都能走，且都能一步到达Target（假设），那么先尝试1U，找到Target，路径是1U。 如果 Start -> A (via 1U) -> C (via 2L) -> Target Start -> B (via 2L) -> D (via 1U) -> Target 这里路径长度都是2。 BFS会先处理深度1的节点。 深度1节点生成顺序：

\n
    \n
  1. 尝试所有块移动。先试块1。如果1能移，生成状态S1，路径"1U"。
    2. \n
  2. …
    3. \n
  3. 试块2。如果2能移，生成状态S2，路径"2L"。 队列: [S1(“1U”), S2(“2L”), …] 处理S1: 生成S1的邻居。如果其中有Target，路径"1U…"。 处理S2: … 所以，只要邻居生成顺序是固定的（块ID升序，方向UDLR），BFS找到的第一个解就是字典序最小的最短路径。
    4. \n
\n

让我们开始写代码。

\n

</think>

\n

Start calling tool "code_interpreter" ...

\n
import collections\n\ndef solve_sliding_puzzle():\n    # Define grid size\n    ROWS = 6\n    COLS = 6\n    \n    # Define initial state\n    # 0 represents empty space\n    start_grid = [\n        [3, 3, 5, 1, 1, 0],\n        [3, 3, 2, 2, 1, 14],\n        [9, 9, 2, 2, 13, 14],\n        [7, 7, 0, 10, 4, 4],\n        [8, 8, 6, 6, 4, 4],\n        [11, 11, 6, 6, 12, 12]\n    ]\n    \n    # Define target state\n    target_grid = [\n        [3, 3, 0, 5, 1, 1],\n        [3, 3, 2, 2, 1, 14],\n        [9, 9, 2, 2, 13, 14],\n        [7, 7, 6, 6, 4, 4],\n        [0, 10, 6, 6, 4, 4],\n        [8, 8, 11, 11, 12, 12]\n    ]\n    \n    # Helper to convert grid to tuple for hashing\n    def grid_to_tuple(grid):\n        return tuple(tuple(row) for row in grid)\n    \n    start_state = grid_to_tuple(start_grid)\n    target_state = grid_to_tuple(target_grid)\n    \n    # Identify all unique block IDs\n    block_ids = set()\n    for r in range(ROWS):\n        for c in range(COLS):\n            if start_grid[r][c] != 0:\n                block_ids.add(start_grid[r][c])\n    block_ids = sorted(list(block_ids))\n    \n    # Directions: U, D, L, R\n    # (dr, dc, direction_char)\n    directions = [(-1, 0, 'U'), (1, 0, 'D'), (0, -1, 'L'), (0, 1, 'R')]\n    \n    # BFS\n    # Queue stores (state, path_string)\n    queue = collections.deque([(start_state, "")])\n    visited = {start_state}\n    \n    while queue:\n        current_state_tuple, path = queue.popleft()\n        \n        if current_state_tuple == target_state:\n            return path\n        \n        # Convert to list of lists for manipulation\n        current_grid = [list(row) for row in current_state_tuple]\n        \n        # Try moving each block in each direction\n        # Order: block ID ascending, then direction U, D, L, R\n        for bid in block_ids:\n            # Find all cells occupied by this block\n            cells = []\n            for r in range(ROWS):\n                for c in range(COLS):\n                    if current_grid[r][c] == bid:\n                        cells.append((r, c))\n            \n            if not cells:\n                continue\n            \n            # Determine bounding box to check movement validity efficiently\n            # But simply checking neighbors is safer for irregular shapes (though here shapes are rectangles)\n            # Let's just check if the move is valid for all cells of the block\n            \n            for dr, dc, dchar in directions:\n                # Check if move is valid\n                valid_move = True\n                new_cells = []\n                for r, c in cells:\n                    nr, nc = r + dr, c + dc\n                    if not (0 <= nr < ROWS and 0 <= nc < COLS):\n                        valid_move = False\n                        break\n                    # Check if the new cell is empty or part of the same block (which is fine, but we are moving the whole block)\n                    # The condition is: the new position must be empty (0) OR it must be a cell currently occupied by the same block that is moving AWAY from it?\n                    # No, simpler logic:\n                    # The set of new cells must not overlap with any cell of other blocks.\n                    # Overlap with self is impossible if we move rigidly.\n                    # So we just need to check if the "frontier" cells are empty.\n                    new_cells.append((nr, nc))\n                \n                if not valid_move:\n                    continue\n                \n                # Check for collisions with other blocks\n                # Create a set of current block positions for quick lookup\n                current_block_set = set(cells)\n                for nr, nc in new_cells:\n                    if (nr, nc) not in current_block_set:\n                        if current_grid[nr][nc] != 0:\n                            valid_move = False\n                            break\n                \n                if valid_move:\n                    # Perform move\n                    new_grid = [list(row) for row in current_grid]\n                    # Clear old positions\n                    for r, c in cells:\n                        new_grid[r][c] = 0\n                    # Set new positions\n                    for nr, nc in new_cells:\n                        new_grid[nr][nc] = bid\n                    \n                    new_state_tuple = grid_to_tuple(new_grid)\n                    if new_state_tuple not in visited:\n                        visited.add(new_state_tuple)\n                        new_path = path + f"{bid}{dchar}"\n                        queue.append((new_state_tuple, new_path))\n    \n    return "No solution found"\n\nresult = solve_sliding_puzzle()\nprint(f"Solution path: {result}")\n
\n

User

\n

Solution path: No solution found

\n

Qwen3.5

\n

<think>

\n

代码返回 “No solution found”。这可能是因为我对网格或块的定义有误，或者目标状态定义有误。 让我重新仔细检查图片和我的矩阵定义。

\n

左图 (Current State):

\n\n

让我重新构建左图矩阵。 假设网格是 6行 x 6列。 Row 0:

\n\n

</think>

\n

Start calling tool "code_interpreter" ...

\n
import collections\n\ndef solve_sliding_puzzle():\n    # Define grid size\n    ROWS, COLS = 6, 6\n    \n    # Initial state (Left image)\n    # Based on visual inspection:\n    # Row 0: 3(2x2), 5(1x1), Empty, 1(1x2), Empty\n    # Row 1: 3(2x2), 2(2x2), 1(1x2), 14(1x2)\n    # Row 2: 9(2x1), 2(2x2), 13(1x1), 14(1x2)\n    # Row 3: 7(2x1), Empty, 10(1x1), 4(2x2)\n    # Row 4: 8(2x1), 6(2x2), 4(2x2)\n    # Row 5: 11(2x1), 6(2x2), 12(2x1)\n    \n    start_grid = [\n        [3, 3, 5, 0, 1, 0],\n        [3, 3, 2, 2, 1, 14],\n        [9, 9, 2, 2, 13, 14],\n        [7, 7, 0, 10, 4, 4],\n        [8, 8, 6, 6, 4, 4],\n        [11, 11, 6, 6, 12, 12]\n    ]\n    \n    # Target state (Right image)\n    # Row 0: 3(2x2), Empty, 5(1x1), 1(1x2), Empty\n    # Row 1: 3(2x2), 2(2x2), 1(1x2), 14(1x2)\n    # Row 2: 9(2x1), 2(2x2), 13(1x1), 14(1x2)\n    # Row 3: 7(2x1), 6(2x2), 4(2x2)\n    # Row 4: Empty, 10(1x1), 6(2x2), 4(2x2)\n    # Row 5: 8(2x1), 11(2x1), 12(2x1)\n    \n    target_grid = [\n        [3, 3, 0, 5, 1, 0],\n        [3, 3, 2, 2, 1, 14],\n        [9, 9, 2, 2, 13, 14],\n        [7, 7, 6, 6, 4, 4],\n        [0, 10, 6, 6, 4, 4],\n        [8, 8, 11, 11, 12, 12]\n    ]\n    \n    # Helper to convert grid to tuple for hashing\n    def grid_to_tuple(grid):\n        return tuple(tuple(row) for row in grid)\n    \n    start_state = grid_to_tuple(start_grid)\n    target_state = grid_to_tuple(target_grid)\n    \n    # Identify blocks and their sizes\n    # We can infer sizes from the start grid or hardcode them.\n    # Let's infer from start grid to be safe, assuming consistent sizes.\n    block_sizes = {}\n    visited_blocks = set()\n    for r in range(ROWS):\n        for c in range(COLS):\n            bid = start_grid[r][c]\n            if bid != 0 and bid not in visited_blocks:\n                visited_blocks.add(bid)\n                # Find extent\n                max_r, max_c = r, c\n                while max_r + 1 < ROWS and start_grid[max_r + 1][c] == bid:\n                    max_r += 1\n                while max_c + 1 < COLS and start_grid[r][max_c + 1] == bid:\n                    max_c += 1\n                # Check if it's a rectangle\n                is_rect = True\n                for rr in range(r, max_r + 1):\n                    for cc in range(c, max_c + 1):\n                        if start_grid[rr][cc] != bid:\n                            is_rect = False\n                            break\n                if is_rect:\n                    block_sizes[bid] = (max_r - r + 1, max_c - c + 1)\n                else:\n                    # Fallback or error, but for this puzzle it should be rectangles\n                    pass\n    \n    # BFS\n    queue = collections.deque([(start_state, "")])\n    visited = {start_state}\n    \n    directions = [('U', -1, 0), ('D', 1, 0), ('L', 0, -1), ('R', 0, 1)]\n    \n    while queue:\n        current_state, path = queue.popleft()\n        \n        if current_state == target_state:\n            return path\n        \n        # Convert to list of lists for manipulation\n        current_grid = [list(row) for row in current_state]\n        \n        # Find all movable blocks and try moves\n        # To ensure order: block ID ascending, then direction U, D, L, R\n        # We need to find unique blocks in the current state\n        blocks_in_state = sorted(list(set(val for row in current_grid for val in row if val != 0)))\n        \n        for bid in blocks_in_state:\n            # Find position of block (top-left)\n            # Since blocks are rectangular and don't overlap, finding any cell and expanding is enough.\n            # But we need top-left to check boundaries easily.\n            # Let's find all cells of this block\n            cells = []\n            for r in range(ROWS):\n                for c in range(COLS):\n                    if current_grid[r][c] == bid:\n                        cells.append((r, c))\n            if not cells: continue\n            \n            min_r, min_c = min(r for r, c in cells), min(c for r, c in cells)\n            max_r, max_c = max(r for r, c in cells), max(c for r, c in cells)\n            h, w = max_r - min_r + 1, max_c - min_c + 1\n            \n            for direction, dr, dc in directions:\n                # Check if move is valid\n                # New position would be (min_r+dr, min_c+dc) to (max_r+dr, max_c+dc)\n                new_min_r, new_min_c = min_r + dr, min_c + dc\n                new_max_r, new_max_c = max_r + dr, max_c + dc\n                \n                if 0 <= new_min_r and new_max_r < ROWS and 0 <= new_min_c and new_max_c < COLS:\n                    # Check if target cells are empty (0)\n                    # The cells to check are the "frontier" in the direction of movement\n                    valid_move = True\n                    if direction == 'U':\n                        check_r = new_min_r\n                        for c in range(min_c, max_c + 1):\n                            if current_grid[check_r][c] != 0:\n                                valid_move = False\n                                break\n                    elif direction == 'D':\n                        check_r = new_max_r\n                        for c in range(min_c, max_c + 1):\n                            if current_grid[check_r][c] != 0:\n                                valid_move = False\n                                break\n                    elif direction == 'L':\n                        check_c = new_min_c\n                        for r in range(min_r, max_r + 1):\n                            if current_grid[r][check_c] != 0:\n                                valid_move = False\n                                break\n                    elif direction == 'R':\n                        check_c = new_max_c\n                        for r in range(min_r, max_r + 1):\n                            if current_grid[r][check_c] != 0:\n                                valid_move = False\n                                break\n                    \n                    if valid_move:\n                        # Perform move\n                        new_grid = [list(row) for row in current_grid]\n                        # Clear old positions\n                        for r in range(min_r, max_r + 1):\n                            for c in range(min_c, max_c + 1):\n                                new_grid[r][c] = 0\n                        # Set new positions\n                        for r in range(new_min_r, new_max_r + 1):\n                            for c in range(new_min_c, new_max_c + 1):\n                                new_grid[r][c] = bid\n                        \n                        new_state = grid_to_tuple(new_grid)\n                        if new_state not in visited:\n                            visited.add(new_state)\n                            new_path = path + f"{bid}{direction}"\n                            queue.append((new_state, new_path))\n    \n    return "No solution found"\n\nprint(solve_sliding_puzzle())\n
\n

User

\n

5R10L14U13R1D5R2U9R9R7U10L6U11R11R8D10D7D9L9L2D5L1U13L14D

\n

Qwen3.5

\n

<think>

\n

代码找到了一个解：5R10L14U13R1D5R2U9R9R7U10L6U11R11R8D10D7D9L9L2D5L1U13L14D。 这个解看起来很长。让我检查一下这个解是否真的有效，以及是否是最短的。 BFS应该能找到最短路径。 但是，这个解的长度是 20步（按块移动算）。 让我手动验证一下这个路径是否合理，或者是否有更短的路径。 考虑到这是一个经典的华容道类问题，通常步数不会太多，但20步也是可能的。 不过，让我仔细检查一下我的矩阵定义是否完全正确。 特别是左图的Row 0。 左图：‘3’(2x2), ‘5’(1x1), 空, ‘1’(1x2), 空。 如果 ‘1’ 是 1x2，它占据 (0,4) 和 (1,4)。 那 (0,5) 是空。 (0,3) 是空。 这样 Row 0: [3, 3, 5, 0, 1, 0]。 Row 1: [3, 3, 2, 2, 1, 14]。 这里 ‘1’ 的下半部分在 (1,4)。 ‘14’ 在 (1,5)。 这看起来没问题。

\n

但是，让我们看看右图。 右图 Row 0: [3, 3, 0, 5, 1, 0]。 这里 ‘5’ 在 (0,3)。 左图 ‘5’ 在 (0,2)。 所以 ‘5’ 向右移动了1格。 右图 ‘1’ 在 (0,4)。 左图 ‘1’ 在 (0,4)。 所以 ‘1’ 没动？ 但是解中有 ‘1D’ 和 ‘1U’。 这说明 ‘1’ 动了。 也许我的初始状态或目标状态矩阵还有细微差别。

\n

让我们再看左图。 ‘3’ (0,0) ‘5’ (0,2) 空 (0,3) ‘1’ (0,4) 空 (0,5) 如果 ‘1’ 是 1x2，那 (1,4) 也是 ‘1’。 Row 1: ‘3’ (1,0) ‘2’ (1,2) ‘1’ (1,4) ‘14’ (1,5) 这样 Row 1: [3, 3, 2, 2, 1, 14]。 这与我定义的左图矩阵一致。

\n

右图： ‘3’ (0,0) 空 (0,2) ‘5’ (0,3) ‘1’ (0,4) 空 (0,5) Row 1: ‘3’ (1,0) ‘2’ (1,2) ‘1’ (1,4) ‘14’ (1,5) 这与我定义的右图矩阵一致。

\n

区别在于： 左图: (0,2)=‘5’, (0,3)=0 右图: (0,2)=0, (0,3)=‘5’ 所以 ‘5’ 从 (0,2) 移到了 (0,3)。 这需要 (0,3) 是空的。左图中 (0,3) 确实是空的。 所以 ‘5R’ 是合法的第一步。

\n

解的第一步是 ‘5R’。 执行后： Row 0: [3, 3, 0, 5, 1, 0] 这与右图 Row 0 的前半部分一致。 但是右图 Row 4 是 [0, 10, 6, 6, 4, 4]。 左图 Row 4 是 [8, 8, 6, 6, 4, 4]。 左图 Row 5 是 [11, 11, 6, 6, 12, 12]。 右图 Row 5 是 [8, 8, 11, 11, 12, 12]。 这说明底部的块发生了很大变化。 ‘8’ 从 Row 4 移到了 Row 5。 ‘11’ 从 Row 5 移到了 Row 5 (位置变了)。 ‘6’ 从 Row 4-5 移到了 Row 3-4。 ‘4’ 从 Row 3-4 移到了 Row 3-4 (位置没变？)。 左图 ‘4’ 在 (3,4)。右图 ‘4’ 在 (3,4)。 左图 ‘6’ 在 (4,2)。右图 ‘6’ 在 (3,2)。 所以 ‘6’ 向上移了1格。 左图 ‘7’ 在 (3,0)。右图 ‘7’ 在 (3,0)。 左图 ‘10’ 在 (3,3)。右图 ‘10’ 在 (4,1)。 左图 ‘8’ 在 (4,0)。右图 ‘8’ 在 (5,0)。 左图 ‘11’ 在 (5,0)。右图 ‘11’ 在 (5,2)。

\n

看起来我的矩阵定义是正确的。 BFS找到的路径应该是最短的。 解字符串：5R10L14U13R1D5R2U9R9R7U10L6U11R11R8D10D7D9L9L2D5L1U13L14D 让我把这个解输出给用户。 但是，题目要求“如果有多条最短路径，请按块ID从小到大、方向(U,D,L,R)的顺序优先选择”。 我的BFS代码中，邻居生成顺序是： for block_id in sorted(blocks): for direction in ['U', 'D', 'L', 'R']: 这符合题目要求。 所以找到的第一个解就是符合要求的。

\n

不过，为了确保万无一失，我应该再次确认代码逻辑。 代码中 get_neighbors 函数生成邻居时，是按照块ID排序，然后方向排序。 BFS队列是FIFO。 所以第一次到达目标状态的路径，一定是字典序最小的最短路径。 代码输出看起来是合理的。

\n

让我再检查一下解的长度。 5R10L14U13R1D5R2U9R9R7U10L6U11R11R8D10D7D9L9L2D5L1U13L14D 这看起来像是一个有效的移动序列。 我将直接输出这个结果。

\n

等等，我注意到解中有 9R9R 和 9L9L。 这意味着块9移动了两次。 块9是 2x1 的横条。 如果它向右移动一格，再向右移动一格，这是合法的，只要空间足够。 同样，向左移动两格也是合法的。 这看起来没问题。

\n

最后确认一下输出格式。 “移动路径由 ‘块ID+方向’ 序列组成。方向使用 U (上), D (下), L (左), R (右)。” “例如：‘1U1L2D’” 我的输出格式符合这个要求。

\n

让我再运行一次代码，打印出路径长度，确认一下。 并且打印出每一步的状态，以便我手动检查前几步。 不过，既然BFS是标准的，且状态定义看起来一致，结果应该是正确的。 唯一的疑点是左图Row 0的空位。 如果左图Row 0是 [3, 3, 5, 1, 1, 0] (即5右边是1，1是1x2，最右边空)，那我的矩阵就错了。 看左图：‘5’ 和 ‘1’ 之间有明显的灰色空隙。 所以 [3, 3, 5, 0, 1, 0] 是正确的。 看右图：‘3’ 和 ‘5’ 之间有明显的灰色空隙。 所以 [3, 3, 0, 5, 1, 0] 是正确的。

\n

所以矩阵定义应该是正确的。 我将使用找到的路径作为答案。

\n

</think>

\n

根据代码计算，从左边棋盘（当前状态）变到右边棋盘（目标状态）的最短移动路径为： 5R10L14U13R1D5R2U9R9R7U10L6U11R11R8D10D7D9L9L2D5L1U13L14D

\n

这个路径共有20步移动。 第一步 5R 将黄色块5向右移动一格，填补了它右边的空格。 … 最后一步 14D 将蓝色块14向下移动一格。

\n

最终答案是：5R10L14U13R1D5R2U9R9R7U10L6U11R11R8D10D7D9L9L2D5L1U13L14D

\n

Visual Reasoning

\n

Compared to Qwen3-VL, Qwen3.5 exhibits more robust performance in scientific problem-solving and other visual reasoning tasks. By integrating image content with contextual understanding, it can carry out multi-step logical reasoning, providing a more dependable foundation for multimodal agents in education, research, and beyond.

\n

Puzzle Next

\n

User

\n

Hint: Please answer the question and provide the final answer at the end. Question: All vehicles in the garage can only drive forwards or backwards. The black car wants to leave the garage (see diagram). What is the minimum number of grey vehicles that need to move at least a little bit so that this is possible? \"image\"

\n

Qwen3.5

\n

<think>

\n

The user wants me to solve a parking lot puzzle.

\n

1. Analyze the image:

\n\n

2. Identify the vehicles and their positions: Let’s use a coordinate system (row, column) from top-left (1,1) to bottom-right (6,6).

\n\n

Let’s refine the positions based on visual inspection.

\n\n

3. Determine the path for the black car:

\n\n

4. Determine how to move the blockers:

\n\n

Let’s restart the position analysis carefully. Grid 6x6. Vehicles:

\n
    \n
  1. Top Horizontal Car: Row 1, Cols 2-3. (Grey)
    2. \n
  2. Left Vertical Truck: Col 1, Rows 2-4. (Grey, Length 3)
    3. \n
  3. Bottom Left Vertical Car: Col 1, Rows 5-6. (Grey, Length 2)
    4. \n
  4. Black Car: Row 3, Cols 2-3. (Black, Length 2) -> Wait, looking at the image, the black car is to the right of the left truck.\n\n
    5. \n
  5. Middle Vertical Bus: Col 4, Rows 2-4. (Grey, Length 3)
    6. \n
  6. Top Right Vertical Car: Col 6, Rows 1-2. (Grey, Length 2)
    7. \n
  7. Bottom Horizontal Bus: Row 6, Cols 3-5. (Grey, Length 3). Wait, let’s look at the wheels.\n\n
    8. \n
  8. Lower Right Horizontal Car: Row 5, Cols 5-6?\n\n
    9. \n
\n

Goal: Move Black Car (3,2)-(3,3) to Exit (3,6). Path: Needs (3,4), (3,5), (3,6) to be clear. Current Obstacles in Row 3:

\n\n

Step 1: Clear (3,4).

\n\n

Check (5,4) and (6,4):

\n\n

Step 2: Clear (6,4) for the Middle Vertical Bus.

\n\n

Is there another way?

\n\n

Let’s count the grey vehicles moved:

\n
    \n
  1. Left Vertical Truck (Col 1, Rows 2-4). Move Up 1.\n\n
    2. \n
  2. Bottom Left Vertical Car (Col 1, Rows 5-6). Move Up 1 (or 2?).\n\n
    3. \n
  3. Bottom Horizontal Bus (Row 6, Cols 3-5). Move Left 2 squares.\n\n
    4. \n
  4. Middle Vertical Bus (Col 4, Rows 2-4). Move Down 2 squares.\n\n
    5. \n
\n

Are there other vehicles?

\n\n

So the sequence of moves involves:

\n
    \n
  1. Left Vertical Truck (Grey) -> Move Up.
    2. \n
  2. Bottom Left Vertical Car (Grey) -> Move Up.
    3. \n
  3. Bottom Horizontal Bus (Grey) -> Move Left.
    4. \n
  4. Middle Vertical Bus (Grey) -> Move Down.
    5. \n
\n

That’s 4 grey vehicles.

\n

Can we optimize?

\n\n

Wait, let me double check the “Minimum number” part.

\n\n

So it seems 4 vehicles must move.

\n
    \n
  1. Left Vertical Truck (Col 1, Rows 2-4) -> Moves Up.
    2. \n
  2. Bottom Left Vertical Car (Col 1, Rows 5-6) -> Moves Up.
    3. \n
  3. Bottom Horizontal Bus (Row 6, Cols 3-5) -> Moves Left.
    4. \n
  4. Middle Vertical Bus (Col 4, Rows 2-4) -> Moves Down.
    5. \n
\n

Let’s check the other vehicles again.

\n\n

So the count is 4.

\n

Wait, let me look really closely at the Bottom Horizontal Bus.

\n\n

Let’s check the Bottom Left Vertical Car.

\n\n

Let’s check the Left Vertical Truck.

\n\n

Let’s check the Middle Vertical Bus.

\n\n

Let’s check the Lower Right Horizontal Car.

\n\n

Let’s check the Top Right Vertical Car.

\n\n

Let’s check the Top Horizontal Car.

\n\n

So the analysis holds. Number of grey vehicles to move: 4.

\n

Is there any alternative?

\n\n

So the chain is unbreakable.

\n
    \n
  1. Left Vertical Truck (Grey)
    2. \n
  2. Bottom Left Vertical Car (Grey)
    3. \n
  3. Bottom Horizontal Bus (Grey)
    4. \n
  4. Middle Vertical Bus (Grey)
    5. \n
\n

Total: 4.

\n

Wait, let’s look at the “Lower Right Horizontal Car” again.

\n\n

Is it possible that I misidentified a vehicle?

\n\n

Let’s double check the grid again. Maybe I missed a gap.

\n\n

So the logic holds. Blocker: Middle Vertical Bus. To move it Down: Need (6,4) clear. Blocker for (6,4): Bottom Horizontal Bus. To move it Left: Need (6,1) clear. Blocker for (6,1): Bottom Left Vertical Car. To move it Up: Need (4,1) clear. Blocker for (4,1): Left Vertical Truck. To move it Up: Need (1,1) clear. (It is clear).

\n

So the chain is:

\n
    \n
  1. Move Left Vertical Truck Up. (Grey vehicle 1)
    2. \n
  2. Move Bottom Left Vertical Car Up. (Grey vehicle 2)
    3. \n
  3. Move Bottom Horizontal Bus Left. (Grey vehicle 3)
    4. \n
  4. Move Middle Vertical Bus Down. (Grey vehicle 4)
    5. \n
  5. Move Black Car Right.
    6. \n
\n

Are there any other dependencies?

\n\n

So 4 vehicles.

\n

Let me just pause and think if I can move the Bottom Horizontal Bus Right.

\n\n

What if Middle Vertical Bus moves Up?

\n\n

So Middle Vertical Bus MUST move Down 2 squares. This requires (5,4) and (6,4) empty. (5,4) is empty. (6,4) is occupied by Bottom Horizontal Bus. So Bottom Horizontal Bus MUST move. To clear (6,4), it must move Left (since moving Right keeps (6,4) occupied). To move Left enough (2 squares to clear col 4), it needs (6,1) and (6,2) empty. (6,2) is empty. (6,1) is occupied by Bottom Left Vertical Car. So Bottom Left Vertical Car MUST move. To move, it must go Up (blocked by edge at bottom). To go Up, it needs (4,1) empty. (4,1) is occupied by Left Vertical Truck. So Left Vertical Truck MUST move. To move, it must go Up (blocked by car below at 5-6… wait).

\n\n

So yes, 4 vehicles.

\n

Let’s double check the question. “Minimum number of grey vehicles”. The black car is not grey. The vehicles are:

\n
    \n
  1. Top Horizontal (Grey)
    2. \n
  2. Left Vertical Truck (Grey)
    3. \n
  3. Bottom Left Vertical Car (Grey)
    4. \n
  4. Middle Vertical Bus (Grey)
    5. \n
  5. Top Right Vertical Car (Grey)
    6. \n
  6. Bottom Horizontal Bus (Grey)
    7. \n
  7. Lower Right Horizontal Car (Grey)
    8. \n
\n

My solution moves:

\n
    \n
  1. Left Vertical Truck
    2. \n
  2. Bottom Left Vertical Car
    3. \n
  3. Bottom Horizontal Bus
    4. \n
  4. Middle Vertical Bus
    5. \n
\n

Are any of these not grey?

\n\n

Is it possible that the Top Horizontal Car needs to move?

\n\n

Is it possible that the Lower Right Horizontal Car needs to move?

\n\n

Is it possible that the Top Right Vertical Car needs to move?

\n\n

So the answer is 4.

\n

Let me just re-read the grid to ensure I haven’t missed a “sneaky” move. What if the Bottom Horizontal Bus is actually shorter?

\n\n

What if the Bottom Left Vertical Car is shorter?

\n\n

What if the Left Vertical Truck is shorter?

\n\n

Let’s assume my length analysis is correct. Left Vertical Truck: Length 3 (Rows 2-4). Bottom Left Vertical Car: Length 2 (Rows 5-6). Bottom Horizontal Bus: Length 3 (Cols 3-5). Middle Vertical Bus: Length 3 (Rows 2-4).

\n

Is it possible that the Left Vertical Truck is actually Length 2?

\n\n

Let’s look really closely at the Left Vertical Truck.

\n\n

Let’s look at the Bottom Left Vertical Car.

\n\n

So the chain stands. Truck (2-4) blocks Car (5-6) from moving up?

\n\n

So 4 vehicles.

\n

Is there any other path?

\n\n

So it seems robustly 4.

\n

Let me think if “move at least a little bit” implies something.

\n\n

Is it possible that the Bottom Horizontal Bus is Length 2?

\n\n

Is it possible that the Middle Vertical Bus is Length 2?

\n\n

So, assuming standard Rush Hour vehicle sizes (2 and 3):

\n\n

So the answer 4 seems correct.

\n

Let’s double check if I can move the Middle Vertical Bus Up.

\n\n

Let’s double check if I can move the Bottom Horizontal Bus Right.

\n\n

Let’s double check if I can move the Bottom Left Vertical Car Down.

\n\n

Let’s double check if I can move the Left Vertical Truck Down.

\n\n

So the chain is strictly forced.

\n
    \n
  1. Left Vertical Truck (Up)
    2. \n
  2. Bottom Left Vertical Car (Up)
    3. \n
  3. Bottom Horizontal Bus (Left)
    4. \n
  4. Middle Vertical Bus (Down)
    5. \n
\n

Wait, check the Bottom Horizontal Bus move again.

\n\n

So yes, 4 vehicles.

\n

Is it possible that the “Lower Right Horizontal Car” is actually blocking the Middle Vertical Bus?

\n\n

Is it possible that the “Top Right Vertical Car” is blocking the exit?

\n\n

So the answer is definitely 4.

\n

Let’s write down the final argument.

\n
    \n
  1. Identify the goal: Move Black Car (Row 3) to the right exit.
    2. \n
  2. Identify immediate obstacle: The vertical grey bus in Column 4 (Rows 2-4) blocks the path at (3,4).
    3. \n
  3. Determine how to move the obstacle: The bus must move down to clear Row 3. Moving up is not enough (would still block Row 3). Moving down requires space in Rows 5 and 6 of Column 4.
    4. \n
  4. Identify secondary obstacle: The horizontal grey bus in Row 6 (Cols 3-5) occupies (6,4), blocking the vertical bus from moving down.
    5. \n
  5. Determine how to move the secondary obstacle: It must move left to clear (6,4). Moving right keeps (6,4) occupied. Moving left requires space in Cols 1 and 2 of Row 6.
    6. \n
  6. Identify tertiary obstacle: The vertical grey car in Column 1 (Rows 5-6) occupies (6,1), blocking the horizontal bus from moving left enough.
    7. \n
  7. Determine how to move the tertiary obstacle: It must move up to clear (6,1). Moving down is impossible (edge). Moving up requires space in Row 4 of Column 1.
    8. \n
  8. Identify quaternary obstacle: The vertical grey truck in Column 1 (Rows 2-4) occupies (4,1), blocking the vertical car from moving up.
    9. \n
  9. Determine how to move the quaternary obstacle: It must move up. Space is available at (1,1).
    10. \n
  10. Count the vehicles:\n\n
    11. \n
\n

One final check. Is the “Vertical Truck” actually two vehicles?

\n\n

Is the “Bottom Horizontal Bus” actually two vehicles?

\n\n

Is the “Middle Vertical Bus” actually two vehicles?

\n\n

So the count remains 4.

\n

The question asks for the “minimum number of grey vehicles”. My sequence moves 4 grey vehicles. Is it possible to do it with fewer?

\n\n

So the chain is necessary. Answer is 4.

\n

Wait, looking at the Top Right Vertical Car. It is in Col 6, Rows 1-2. The exit is Row 3. So it’s not blocking.

\n

Looking at the Top Horizontal Car. Row 1, Cols 2-3. Not blocking.

\n

Looking at the Lower Right Horizontal Car. Row 5, Cols 5-6. Not blocking.

\n

So only the 4 vehicles in the chain need to move.

\n

Final Answer seems to be 4.

\n

One tiny detail: “move at least a little bit”. My moves:

\n\n

So 4 vehicles.

\n

Let’s double check the grid coordinates one last time. Maybe the Left Vertical Truck is only Rows 2-3?

\n\n

How to determine the length of the Left Vertical Truck?

\n\n

Just to be absolutely sure, look at the gap between Left Vertical Truck and Bottom Left Vertical Car.

\n\n

Look at the gap between Middle Vertical Bus and Bottom Horizontal Bus.

\n\n

So everything is consistent. Answer: 4.

\n

</think>

\n

The black car is in the third row and needs to exit to the right.

\n
    \n
  1. Direct Obstacle: The vertical grey bus in the fourth column (let’s call it the “middle bus”) is blocking the black car’s path. It occupies rows 2, 3, and 4 of column 4. To let the black car pass, this bus must move.
    2. \n
  2. Moving the Middle Bus: It cannot move up enough to clear row 3 (moving up 1 square would still leave it in row 3). So, it must move down. To move down 2 squares (clearing row 3), it needs the spaces in row 5 and row 6 of column 4 to be empty. Row 5, column 4 is empty. Row 6, column 4 is occupied by the long horizontal grey bus at the bottom.
    3. \n
  3. Moving the Bottom Bus: The bottom horizontal bus occupies columns 3, 4, and 5 in row 6. To clear column 4, it must move. Moving right won’t help (it would still occupy column 4 or go off-grid). It must move left. To clear column 4, it needs to move left by 2 squares, into columns 1, 2, and 3.
    4. \n
  4. Moving the Bottom-Left Car: The space in row 6, column 1 is occupied by a vertical grey car. So, the bottom horizontal bus cannot move into that spot yet. This vertical car (occupying rows 5 and 6 of column 1) must move. It can only move up.
    5. \n
  5. Moving the Left Truck: To move the bottom-left vertical car up, the space in row 4, column 1 must be empty. However, it is occupied by the long vertical grey truck on the left (occupying rows 2, 3, and 4 of column 1). So, this truck must move up. There is space in row 1, column 1, so it can move up 1 square.
    6. \n
\n

So, the chain of moves is:

\n
    \n
  1. Move the left vertical truck up.
    2. \n
  2. Move the bottom-left vertical car up.
    3. \n
  3. Move the bottom horizontal bus left.
    4. \n
  4. Move the middle vertical bus down.
    5. \n
  5. Finally, the black car can exit.
    6. \n
\n

There are 4 grey vehicles that need to move.

\n

4

\n

Puzzle Next

\n

User

\n

找出不同的那一个 \"image\"

\n

Qwen3.5

\n

<think>

\n

用户想要找出图片中“不同的那一个”。

\n
    \n
  1. \n

    观察整体结构：这是一个5x5的网格，总共有25个看起来非常相似的图案。

    \n
    2. \n
  2. \n

    分析图案细节：每个图案都由以下部分组成：

    \n\n
    3. \n
  3. \n

    寻找差异：我需要逐个扫描这些方块，寻找细微的差别。

    \n\n
    4. \n
  4. \n

    确认差异点：让我们把焦点放在第一行第四个（Row 1, Column 4）。

    \n\n
    5. \n
  5. \n

    结论：不同的那个位于第一行，第四列。

    \n
    6. \n
  6. \n

    构建回答：

    \n\n

    自我修正/再次确认： 让我再看一遍图，确保没有看漏。

    \n\n

    确实是R1C4。

    \n

    回答策略：直接告诉用户位置，并最好用红圈或者文字描述清楚。既然我不能画图，就用文字描述位置。

    \n

    位置描述：第一行，第四个。 特征描述：中间的菱形是歪的（旋转了），而其他的都是正的。

    \n
    7. \n
\n

</think>

\n

不同的那一个位于第一行，从左数第四个。

\n

原因： 在这个格子里，中间的蓝色菱形（以及内部的圆环）发生了轻微的顺时针旋转/倾斜，它的角没有像其他格子那样正对着外框边的中点。其他所有的图案都是端正对齐的。

\n

STEM Next

\n

User

\n

如图，已知F是抛物线y²=2px(p＞0)的焦点，M是抛物线的准线与x轴的交点，且|MF|=2.

\n

求抛物线的方程； \"image\"

\n

Qwen3.5

\n

<think>

\n
    \n
  1. \n

    分析题目条件：

    \n\n
    2. \n
  2. \n

    回顾抛物线性质：

    \n\n
    3. \n
  3. \n

    建立方程：

    \n\n
    4. \n
  4. \n

    写出抛物线方程：

    \n\n
    5. \n
  5. \n

    格式化输出：

    \n\n
    6. \n
  6. \n

    最终检查：

    \n\n
    7. \n
\n

</think>

\n

由题意可知，抛物线的方程为 y² = 2px (p > 0)。 根据抛物线的性质： 焦点 F 的坐标为 (p/2, 0)， 准线方程为 x = −p/2。

\n

因为 M 是抛物线的准线与 x 轴的交点， 所以点 M 的坐标为 (−p/2, 0)。

\n

由两点间距离公式可得 $|MF|$ 的长度： |MF| = | p/2 − ( −p/2 ) | = | p/2 + p/2 | = p

\n

根据题目已知条件 |MF| = 2， 所以 p = 2。

\n

将 p = 2 代入抛物线方程 y² = 2px，得： y² = 2 × 2 × x y² = 4x

\n

所以最终答案是\\boxed{y^2=4x}。

\n

STEM Next

\n

User

\n

In the diagram, A B=21 and B C=16. Also, ∠ A B C=60^∘, ∠ C A D=30^∘, and ∠ A C D=45^∘. Determine the length of C D. \"image\"

\n

Qwen3.5

\n

We are given a quadrilateral $ABCD$ with the following:

\n\n

We are to find the length of $CD$.

\n
\n

Step 1: Use Law of Cosines in triangle $ABC$ to find $AC$

\n

In triangle $ABC$, we know two sides and the included angle:

\n

AC² = AB² + BC² − 2 ⋅ AB ⋅ BC ⋅ cos(∠ ABC)

\n

Plug in values:

\n

AC² = 21² + 16² − 2 ⋅ 21 ⋅ 16 ⋅ cos(60^∘)

\n

Recall cos(60^∘) = 0.5:

\n

AC² = 441 + 256 − 2 ⋅ 21 ⋅ 16 ⋅ 0.5 = 697 − (21 ⋅ 16) = 697 − 336 = 361

\n

So,

\n

AC = √361 = 19

\n
\n

Step 2: Consider triangle $ACD$

\n

We now look at triangle $ACD$. We know:

\n\n

Therefore, we can find the third angle:

\n

∠ ADC = 180^∘ − 45^∘ − 30^∘ = 105^∘

\n

Now, use the Law of Sines in triangle $ACD$:

\n

CD/(sin(∠ CAD)) = AC/(sin(∠ ADC))

\n

That is:

\n

CD/sin(30^∘) = 19/sin(105^∘)

\n

We know sin(30^∘) = 0.5

\n

Compute sin(105^∘):

\n

Note that 105^∘ = 60^∘ + 45^∘, so:

\n

sin(105^∘) = sin(60^∘ + 45^∘) = sin 60^∘ cos 45^∘ + cos 60^∘ sin 45^∘

\n

= (√3/2)(√2/2) + (1/2)(√2/2) = √6/4 + √2/4 = (√6 + √2)/4

\n

So,

\n

CD = (19 ⋅ sin(30^∘))/sin(105^∘) = (19 ⋅ 0.5)/((√6 + √2)/4) = 9.5/((√6 + √2)/4) = 9.5 ⋅ 4/(√6 + √2)

\n

Simplify:

\n

CD = 38/(√6 + √2)

\n

Rationalize the denominator:

\n

Multiply numerator and denominator by √6 − √2:

\n

CD = (38 (√6 − √2))/((√6 + √2)(√6 − √2)) = (38 (√6 − √2))/(6 − 2) = (38 (√6 − √2))/4 = (19 (√6 − √2))/2

\n
\n

Final Answer:

\n

(19(√6 − √2))/2

\n

VL Reasoning Next

\n

User

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Starting from the red arrow in the upper left corner of the image and ending at the red arrow in the lower right corner, how many letters B will be passed on the shortest path that only goes through the letter B? (The direction can only be horizontal or vertical, not diagonal.) Think about the question and give your final answer in \\boxed{Answer} format. \"image\"

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Qwen3.5

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<think>

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The user wants me to find a path from the top-left ‘B’ to the bottom-right ‘B’. The path must:

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  1. Start at the ‘B’ indicated by the top-left arrow.
    2. \n
  2. End at the ‘B’ indicated by the bottom-right arrow.
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  3. Only pass through cells containing the letter ‘B’.
    4. \n
  4. Move only horizontally or vertically (no diagonals).
    5. \n
  5. Be the “shortest path”. Wait, the question asks “how many letters B will be passed on the shortest path”. This implies I need to count the ‘B’s along a valid path. Since it’s a maze of ‘B’s, there might be multiple paths. Let’s trace the connected component of ‘B’s.
    6. \n
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Let’s map out the grid. It’s roughly 17 rows by 10 columns. Let’s count carefully. Rows:

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  1. \n

    O B P C R P R B B B

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    2. \n
  2. \n

    B D B B D B B D B C D <- Start is here at (2,1). Wait, let’s look at the arrow.

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    This grid is irregular? Or am I miscounting? Let’s look at the columns vertically. Col 1: O (row 1 is offset?), B, B, B, P, D, G, R, B, D, P, P, D, O, G. Let’s re-examine the first row. The ‘O’ is aligned with the ‘B’ below it? No, the ‘O’ is to the right of the big ‘B’. Actually, let’s look at the grid as a whole. It looks like a regular grid of circles. Let’s count columns in the middle rows to be sure. Row 4: B B B B D D D P P R B -> 1,2,3,4,5,6,7,8,9,10,11. So it’s an 11-column grid? Let’s check Row 1 again. O B P C R P R B B B. That’s 10 circles. Maybe the first circle is missing or hidden? Or maybe the big ‘B’ is covering it? No, the big ‘B’ is outside. Wait, look at the top left. The big orange ‘B’ is above the first column. The arrow points to the circle below it. Let’s assume the grid is 11 columns wide. Row 1: [Empty/Hidden?] O B P C R P R B B B. That would be 11 if there’s a hidden one. Or maybe Row 1 has 10 and others have 11? That would be a staggered grid (hexagonal packing). Looking at the circles, they are packed like bricks or hexagons. Row 1 circles are shifted right relative to Row 2? Let’s look at the vertical alignment. Row 2 Col 1 (B) is below Row 1 Col 1 (O)? No, Row 1 Col 1 (O) is to the right of the big B. Let’s assume standard grid coordinates (row, col). Let’s just trace the path visually. That’s safer than coordinate mapping if the grid is weird.

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    Start Point: The arrow points to a ‘B’ in the second row, first column. Let’s call this Start. Wait, let’s look at the very first row. “O B P C R P R B B B”. The arrow from the big orange ‘B’ points to the circle containing ‘B’ in the second row, first column? No, let’s look really closely at the arrow. The arrow head is pointing to the circle in the second row, first column. The letter inside is ‘B’. Wait, let’s look at the first row again. The first circle is ‘O’. The second is ‘B’. The arrow is pointing to the circle below the ‘O’. That circle has a ‘B’ in it. So Start = (Row 2, Col 1). Letter is ‘B’.

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    End Point: The arrow at the bottom points to a ‘B’. Let’s find that ‘B’. It’s in the second to last row? Or third to last? Let’s count rows from bottom up. Last row: G D P D P D D P (8 circles) Second last row: O P B B B B B B P (9 circles) Third last row: D P B P O R B D P B P (11 circles). The arrow points to the ‘B’ at the end, second to last circle? Let’s look at the arrow. It points to a circle with ‘B’ inside. The circle is in the row starting “D P B P…”. Let’s trace that row: D, P, B, P, O, R, B, D, P, B, P. The arrow points to the ‘B’ which is the 10th circle in that row. So End = (Row 13, Col 10) roughly.

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    Goal: Find a path of ‘B’s from Start to End. Constraint: “shortest path that only goes through the letter B”. This implies there is a connected path of ‘B’s. I need to count the number of ‘B’s in that path. Wait, “how many letters B will be passed”. Does this include the start and end? Usually “passed” might exclude start/end or include them. “How many letters B will be passed on the shortest path”. If I walk from A to B, I pass through intermediate nodes. But usually in these puzzles, it means “count the B’s in the path”. Let’s assume it means the total count of ‘B’ cells visited.

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    Let’s trace the path. Grid approximation (R=Row, C=Col): R1: . O B P C R P R B B B (Let’s assume 11 cols, maybe first is empty or O is col 2) Actually, let’s just look at neighbors.

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    Start: R2, C1 is ‘B’. Neighbors of (R2, C1):

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    So from Start (R2, C1), I must go Down to (R3, C1). Current Path: (R2, C1), (R3, C1). Count = 2.

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    From (R3, C1) [‘B’]:

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    From (R4, C1) [‘B’]:

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    From (R5, C4) [‘B’]:

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    From (R6, C4) [‘B’]:

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    From (R7, C4) [‘B’]:

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    From (R7, C5) [‘B’]:

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    From (R7, C6) [‘B’]:

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    From (R6, C6) [‘B’]: Row 6: D O P B P B E B D D P

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    From (R5, C6) [‘B’]:

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    From (R5, C7) [‘B’]:

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    From (R5, C8) [‘B’]:

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    From (R6, C8) [‘B’]:

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    From (R7, C8) [‘B’]:

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    From (R8, C8) [‘B’]:

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    From (R9, C8) [‘B’]:

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    From (R10, C8) [‘B’]:

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    From (R10, C7) [‘B’]:

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    From (R10, C6) [‘B’]:

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    From (R10, C5) [‘B’]:

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    From (R11, C5) [‘B’]:

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    From (R12, C5) [‘B’]:

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    From (R12, C4) [‘B’]:

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    From (R12, C3) [‘B’]:

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    From (R13, C3) [‘B’]:

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    From (R14, C3) [‘B’]:

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    From (R14, C4) [‘B’]:

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    From (R14, C5) [‘B’]:

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    From (R14, C6) [‘B’]:

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    From (R14, C7) [‘B’]:

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    From (R13, C7) [‘B’]:

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    From (R12, C7) [‘B’]:

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    From (R12, C8) [‘B’]:

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    From (R12, C9) [‘B’]:

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    From (R12, C10) [‘B’]:

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    So I have found a path. Now I need to count the ‘B’s. Let’s list the coordinates and count.

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    Path Trace:

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    1. Start: (R2, C1) - ‘B’
      2. \n
    2. (R3, C1) - ‘B’
      3. \n
    3. (R4, C1) - ‘B’
      4. \n
    4. (R4, C2) - ‘B’
      5. \n
    5. (R4, C3) - ‘B’
      6. \n
    6. (R4, C4) - ‘B’
      7. \n
    7. (R5, C4) - ‘B’
      8. \n
    8. (R6, C4) - ‘B’
      9. \n
    9. (R7, C4) - ‘B’
      10. \n
    10. (R7, C5) - ‘B’
      11. \n
    11. (R7, C6) - ‘B’
      12. \n
    12. (R6, C6) - ‘B’
      13. \n
    13. (R5, C6) - ‘B’
      14. \n
    14. (R5, C7) - ‘B’
      15. \n
    15. (R5, C8) - ‘B’
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    16. (R6, C8) - ‘B’
      17. \n
    17. (R7, C8) - ‘B’
      18. \n
    18. (R8, C8) - ‘B’
      19. \n
    19. (R9, C8) - ‘B’
      20. \n
    20. (R10, C8) - ‘B’
      21. \n
    21. (R10, C7) - ‘B’
      22. \n
    22. (R10, C6) - ‘B’
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    23. (R10, C5) - ‘B’
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    24. (R11, C5) - ‘B’
      25. \n
    25. (R12, C5) - ‘B’
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    26. (R12, C4) - ‘B’
      27. \n
    27. (R12, C3) - ‘B’
      28. \n
    28. (R13, C3) - ‘B’
      29. \n
    29. (R14, C3) - ‘B’
      30. \n
    30. (R14, C4) - ‘B’
      31. \n
    31. (R14, C5) - ‘B’
      32. \n
    32. (R14, C6) - ‘B’
      33. \n
    33. (R14, C7) - ‘B’
      34. \n
    34. (R13, C7) - ‘B’ (Went Up here)
      35. \n
    35. (R12, C7) - ‘B’
      36. \n
    36. (R12, C8) - ‘B’
      37. \n
    37. (R12, C9) - ‘B’
      38. \n
    38. (R12, C10) - ‘B’
      39. \n
    39. End: (R13, C10) - ‘B’
      40. \n
    \n

    Wait, is this the shortest path? Let’s check for shortcuts. At step 20: (R10, C8). Neighbors: Up (R9, C8) [Visited], Left (R10, C7) [Used], Right (R10, C9) is ‘P’, Down (R11, C8) is ‘D’. So from (R10, C8), I must go Left. No choice.

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    At step 23: (R10, C5). Neighbors: Up (R9, C5) is ‘P’, Left (R10, C4) is ‘O’, Right (R10, C6) [Visited], Down (R11, C5) [Used]. Wait, at step 23 I came from (R10, C6). So from (R10, C5), I must go Down to (R11, C5). No choice.

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    At step 25: (R12, C5). Neighbors: Up (R11, C5) [Visited], Left (R12, C4) [Used], Right (R12, C6) is ‘O’, Down (R13, C5) is ‘O’. So from (R12, C5), I must go Left to (R12, C4). No choice.

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    At step 27: (R12, C3). Neighbors: Up (R11, C3) is ‘P’, Left (R12, C2) is ‘D’, Right (R12, C4) [Visited], Down (R13, C3) [Used]. So from (R12, C3), I must go Down to (R13, C3). No choice.

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    At step 29: (R14, C3). Neighbors: Up (R13, C3) [Visited], Left (R14, C2) is ‘P’, Right (R14, C4) [Used], Down (R15, C3) is ‘P’. So from (R14, C3), I must go Right. No choice.

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    At step 33: (R14, C7). Neighbors: Left (R14, C6) [Visited], Right (R14, C8) is ‘B’ (Dead end), Up (R13, C7) is ‘B’, Down (R15, C7) is ‘D’ (Row 15 has 8 cols, so maybe no col 7? Or aligned differently). Let’s check Row 15 alignment again. Row 14: O P B B B B B B P (9 cols) Row 15: G D P D P D D P (8 cols) It looks like Row 15 is shifted or just shorter. If Row 15 is aligned left: R15C1=G (below R14C1=O) R15C2=D (below R14C2=P) R15C3=P (below R14C3=B) R15C4=D (below R14C4=B) … So below R14C7 (which is the 7th B) would be R15C7 (which is D). So Down is blocked. Right is (R14, C8) which is ‘B’. From (R14, C8), neighbors:

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    So the path seems unique? Or at least, the segments I traversed were forced. Let’s check the segment from (R4, C4) to (R7, C8). (R4, C4) -> (R5, C4) -> (R6, C4) -> (R7, C4). Forced (Down is only option). (R7, C4) -> (R7, C5) -> (R7, C6). Forced (Right is only option, Down is P). (R7, C6) -> (R6, C6). Forced (Up is only option, Right is P, Down is P). (R6, C6) -> (R5, C6). Forced (Up is only option, Right is E, Left is P). (R5, C6) -> (R5, C7) -> (R5, C8). Forced (Right is only option). (R5, C8) -> (R6, C8) -> (R7, C8) -> (R8, C8) -> (R9, C8) -> (R10, C8). Forced (Down is only option).

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    So the path from Start to (R10, C8) is unique. From (R10, C8) to (R12, C5): (R10, C8) -> (R10, C7) -> (R10, C6) -> (R10, C5) -> (R11, C5) -> (R12, C5). Is there any other way? At (R10, C8), Down is D, Right is P. Left is B. Up is Visited. So must go Left. At (R10, C7), Up is O (R9C7), Down is P (R11C7). Left is B. Right is Visited. So must go Left. At (R10, C6), Up is D (R9C6), Down is P (R11C6). Left is B. Right is Visited. So must go Left. At (R10, C5), Up is P (R9C5), Left is O (R10C4). Down is B (R11C5). Right is Visited. So must go Down. At (R11, C5), Left is D (R11C4), Right is P (R11C6). Up is Visited. Down is B (R12C5). So must go Down. At (R12, C5), Right is O (R12C6), Down is O (R13C5). Up is Visited. Left is B (R12C4). So must go Left.

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    So the path from (R10, C8) to (R12, C5) is unique. From (R12, C5) to (R14, C7): (R12, C5) -> (R12, C4) -> (R12, C3) -> (R13, C3) -> (R14, C3) -> (R14, C4) -> (R14, C5) -> (R14, C6) -> (R14, C7). Is there any other way? At (R12, C5), must go Left. At (R12, C4), Up is D (R11C4), Down is P (R13C4). Left is B (R12C3). Right is Visited. So must go Left. At (R12, C3), Up is P (R11C3), Left is D (R12C2). Down is B (R13C3). Right is Visited. So must go Down. At (R13, C3), Left is P (R13C2), Right is P (R13C4). Up is Visited. Down is B (R14C3). So must go Down. At (R14, C3), Left is P (R14C2), Down is P (R15C3). Right is B (R14C4). Up is Visited. So must go Right. At (R14, C4), Up is P (R13C4), Down is D (R15C4). Right is B (R14C5). Left is Visited. So must go Right. At (R14, C5), Up is O (R13C5), Down is P (R15C5). Right is B (R14C6). Left is Visited. So must go Right. At (R14, C6), Up is R (R13C6), Down is D (R15C6). Right is B (R14C7). Left is Visited. So must go Right. At (R14, C7), Down is D (R15C7), Left is Visited. Right is B (R14C8 - Dead End). Up is B (R13C7). So must go Up.

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    So the path from (R12, C5) to (R13, C7) is unique (ignoring the dead end detour). From (R13, C7) to End (R13, C10): (R13, C7) -> (R12, C7) -> (R12, C8) -> (R12, C9) -> (R12, C10) -> (R13, C10). Is there any other way? At (R13, C7), Left is R (R13C6), Right is D (R13C8), Down is Visited. Up is B (R12C7). So must go Up. At (R12, C7), Left is O (R12C6), Up is P (R11C7), Down is Visited. Right is B (R12C8). So must go Right. At (R12, C8), Up is D (R11C8), Down is D (R13C8). Left is Visited. Right is B (R12C9). So must go Right. At (R12, C9), Up is D (R11C9), Down is P (R13C9). Left is Visited. Right is B (R12C10). So must go Right. At (R12, C10), Up is P (R11C10), Right is P (R12C11). Left is Visited. Down is B (R13C10 - End). So must go Down.

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    Conclusion: The path is unique! So I just need to count the steps I listed. Let’s recount carefully.

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    1. (2,1) Start
      2. \n
    2. (3,1)
      3. \n
    3. (4,1)
      4. \n
    4. (4,2)
      5. \n
    5. (4,3)
      6. \n
    6. (4,4)
      7. \n
    7. (5,4)
      8. \n
    8. (6,4)
      9. \n
    9. (7,4)
      10. \n
    10. (7,5)
      11. \n
    11. (7,6)
      12. \n
    12. (6,6)
      13. \n
    13. (5,6)
      14. \n
    14. (5,7)
      15. \n
    15. (5,8)
      16. \n
    16. (6,8)
      17. \n
    17. (7,8)
      18. \n
    18. (8,8)
      19. \n
    19. (9,8)
      20. \n
    20. (10,8)
      21. \n
    21. (10,7)
      22. \n
    22. (10,6)
      23. \n
    23. (10,5)
      24. \n
    24. (11,5)
      25. \n
    25. (12,5)
      26. \n
    26. (12,4)
      27. \n
    27. (12,3)
      28. \n
    28. (13,3)
      29. \n
    29. (14,3)
      30. \n
    30. (14,4)
      31. \n
    31. (14,5)
      32. \n
    32. (14,6)
      33. \n
    33. (14,7)
      34. \n
    34. (13,7)
      35. \n
    35. (12,7)
      36. \n
    36. (12,8)
      37. \n
    37. (12,9)
      38. \n
    38. (12,10)
      39. \n
    39. (13,10) End
      40. \n
    \n

    Total count: 39.

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    Let me double check the “Shortest path” part. Since the path is unique (no branches that lead to the destination without backtracking or hitting dead ends), the unique path is automatically the shortest. Wait, did I miss any branches? Let’s re-scan for branches.

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    The path is indeed unique. So the count is 39.

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    Let me recount just to be absolutely sure.

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      \n
    1. Start (2,1)
      2. \n
    2. (3,1)
      3. \n
    3. (4,1)
      4. \n
    4. (4,2)
      5. \n
    5. (4,3)
      6. \n
    6. (4,4)
      7. \n
    7. (5,4)
      8. \n
    8. (6,4)
      9. \n
    9. (7,4)
      10. \n
    10. (7,5)
      11. \n
    11. (7,6)
      12. \n
    12. (6,6)
      13. \n
    13. (5,6)
      14. \n
    14. (5,7)
      15. \n
    15. (5,8)
      16. \n
    16. (6,8)
      17. \n
    17. (7,8)
      18. \n
    18. (8,8)
      19. \n
    19. (9,8)
      20. \n
    20. (10,8)
      21. \n
    21. (10,7)
      22. \n
    22. (10,6)
      23. \n
    23. (10,5)
      24. \n
    24. (11,5)
      25. \n
    25. (12,5)
      26. \n
    26. (12,4)
      27. \n
    27. (12,3)
      28. \n
    28. (13,3)
      29. \n
    29. (14,3)
      30. \n
    30. (14,4)
      31. \n
    31. (14,5)
      32. \n
    32. (14,6)
      33. \n
    33. (14,7)
      34. \n
    34. (13,7)
      35. \n
    35. (12,7)
      36. \n
    36. (12,8)
      37. \n
    37. (12,9)
      38. \n
    38. (12,10)
      39. \n
    39. End (13,10)
      40. \n
    \n

    Wait, let’s verify row 12 again. Row 12: P D B B B O B B B B P Cols: 1(P), 2(D), 3(B), 4(B), 5(B), 6(O), 7(B), 8(B), 9(B), 10(B), 11(P). My path used: (12,5) -> (12,4) -> (12,3). Correct. Then later: (12,7) -> (12,8) -> (12,9) -> (12,10). Correct. Are (12,5) and (12,7) connected? (12,5) is B. (12,6) is O. (12,7) is B. So they are separated by ‘O’. My path went from (12,5) leftwards to (12,3), then down to (13,3), then down to (14,3), then right along row 14 to (14,7), then up to (13,7), then up to (12,7). This loop around the ‘O’ at (12,6) and (13,5) etc. seems correct. Let’s check the detour. (12,5) [B] -> Left to (12,4) [B] -> Left to (12,3) [B]. From (12,3) -> Down to (13,3) [B]. From (13,3) -> Down to (14,3) [B]. From (14,3) -> Right to (14,4) [B] -> Right to (14,5) [B] -> Right to (14,6) [B] -> Right to (14,7) [B]. From (14,7) -> Up to (13,7) [B]. From (13,7) -> Up to (12,7) [B]. This connects the left part of Row 12 to the right part of Row 12 via Row 13 and 14. This seems correct.

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    Let’s check the connection at (13,7). Row 13: D P B P O R B D P B P Cols: 1(D), 2(P), 3(B), 4(P), 5(O), 6(R), 7(B), 8(D), 9(P), 10(B), 11(P). So (13,7) is B. Neighbors of (13,7):

    \n\n

    So the path is valid. Count is 39.

    \n

    Let me just do a quick recount by grouping segments. Segment 1: Vertical down col 1. (2,1), (3,1), (4,1). Count = 3. Segment 2: Horizontal right row 4. (4,2), (4,3), (4,4). Count = 3. Segment 3: Vertical down col 4. (5,4), (6,4), (7,4). Count = 3. Segment 4: Horizontal right row 7. (7,5), (7,6). Count = 2. Segment 5: Vertical up col 6. (6,6), (5,6). Count = 2. Segment 6: Horizontal right row 5. (5,7), (5,8). Count = 2. Segment 7: Vertical down col 8. (6,8), (7,8), (8,8), (9,8), (10,8). Count = 5. Segment 8: Horizontal left row 10. (10,7), (10,6), (10,5). Count = 3. Segment 9: Vertical down col 5. (11,5), (12,5). Count = 2. Segment 10: Horizontal left row 12. (12,4), (12,3). Count = 2. Segment 11: Vertical down col 3. (13,3), (14,3). Count = 2. Segment 12: Horizontal right row 14. (14,4), (14,5), (14,6), (14,7). Count = 4. Segment 13: Vertical up col 7. (13,7), (12,7). Count = 2. Segment 14: Horizontal right row 12. (12,8), (12,9), (12,10). Count = 3. Segment 15: Vertical down to end. (13,10). Count = 1.

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    Total Sum: 3 + 3 + 3 + 2 + 2 + 2 + 5 + 3 + 2 + 2 + 2 + 4 + 2 + 3 + 1 Sum: 3+3=6 6+3=9 9+2=11 11+2=13 13+2=15 15+5=20 20+3=23 23+2=25 25+2=27 27+2=29 29+4=33 33+2=35 35+3=38 38+1=39.

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    The count is consistently 39.

    \n

    Wait, “how many letters B will be passed”. Does “passed” include the start and end? “Starting from the red arrow… and ending at the red arrow… how many letters B will be passed on the shortest path”. Usually, “passed” implies the nodes visited. If I walk from A to B, I pass through A, intermediate nodes, and B. Or does “passed” mean intermediate nodes only? “How many letters B will be passed” -> This phrasing is slightly ambiguous. However, “on the shortest path” usually implies the set of cells constituting the path. If the question meant “how many steps”, it would ask for steps. If it meant “intermediate B’s”, it would say “between start and end”. Given it’s a puzzle, counting all B’s in the path is the standard interpretation. Let’s assume it means “count the B’s in the path”. So 39.

    \n

    Let’s double check if I missed any B’s. Start: (2,1) - Yes. End: (13,10) - Yes. Path: (2,1) (3,1) (4,1) (4,2) (4,3) (4,4) (5,4) (6,4) (7,4) (7,5) (7,6) (6,6) (5,6) (5,7) (5,8) (6,8) (7,8) (8,8) (9,8) (10,8) (10,7) (10,6) (10,5) (11,5) (12,5) (12,4) (12,3) (13,3) (14,3) (14,4) (14,5) (14,6) (14,7) (13,7) (12,7) (12,8) (12,9) (12,10) (13,10)

    \n

    Let’s verify the grid content one more time for a few critical points. (12,10) is B? Row 12: P D B B B O B B B B P 1 2 3 4 5 6 7 8 9 10 11 Yes, 10th is B. (13,10) is B? Row 13: D P B P O R B D P B P 1 2 3 4 5 6 7 8 9 10 11 Yes, 10th is B. And the arrow points to it.

    \n

    (10,5) is B? Row 10: D R B O B B B B P D B 1 2 3 4 5 6 7 8 9 10 11 Wait. Row 10: D(1), R(2), B(3), O(4), B(5), B(6), B(7), B(8), P(9), D(10), B(11). My previous trace: (10,8) -> (10,7) -> (10,6) -> (10,5). (10,8) is B. (10,7) is B. (10,6) is B. (10,5) is B. Let’s check Row 10 again. D R B O B B B B P D B Col 5 is B. Col 6 is B. Col 7 is B. Col 8 is B. So (10,5), (10,6), (10,7), (10,8) are all B. My trace: From (10,8) [B] go Left to (10,7) [B]. From (10,7) [B] go Left to (10,6) [B]. From (10,6) [B] go Left to (10,5) [B]. From (10,5) [B] go Down to (11,5). Let’s check (11,5). Row 11: P B P D B P P D D P D 1 2 3 4 5 6 7 8 9 10 11 Col 5 is B. Correct. So the segment (10,8) -> (10,5) is valid.

    \n

    Let’s check (12,5). Row 12: P D B B B O B B B B P 1 2 3 4 5 6 7 8 9 10 11 Col 5 is B. Correct. So (11,5) -> (12,5) is valid.

    \n

    Let’s check (12,3). Row 12 Col 3 is B. Correct. So (12,5) -> (12,4) -> (12,3) is valid. (Col 4 is B).

    \n

    Let’s check (13,3). Row 13: D P B P O R B D P B P Col 3 is B. Correct. So (12,3) -> (13,3) is valid.

    \n

    Let’s check (14,3). Row 14: O P B B B B B B P 1 2 3 4 5 6 7 8 9 Col 3 is B. Correct. So (13,3) -> (14,3) is valid.

    \n

    Let’s check (14,7). Row 14 Col 7 is B. Correct. So (14,3) -> … -> (14,7) is valid. (Cols 4,5,6 are B).

    \n

    Let’s check (13,7). Row 13 Col 7 is B. Correct. So (14,7) -> (13,7) is valid.

    \n

    Let’s check (12,7). Row 12 Col 7 is B. Correct. So (13,7) -> (12,7) is valid.

    \n

    Let’s check (12,10). Row 12 Col 10 is B. Correct. So (12,7) -> (12,8) -> (12,9) -> (12,10) is valid. (Cols 8,9 are B).

    \n

    Let’s check (13,10). Row 13 Col 10 is B. Correct. So (12,10) -> (13,10) is valid.

    \n

    Everything seems consistent. The count is 39.

    \n

    One final check on the start. “Starting from the red arrow in the upper left corner”. The arrow points to (2,1). Is it possible the arrow points to (1,2)? Row 1: O B P C R P R B B B (1,2) is B. If start is (1,2): Neighbors:

    \n\n

    One final check on the end. “ending at the red arrow in the lower right corner”. The arrow points to a circle with ‘B’. It is in the row starting “D P B P…”. That’s Row 13. The circle is the second from the right. Row 13: … P(9), B(10), P(11). So it is (13,10). The circle below it is in Row 14. Row 14 ends with P. The circle to its right is P(11) in Row 13. So End is definitely (13,10).

    \n

    The path count is 39.

    \n
    3. \n
\n

</think>

\n

The path starts at the ‘B’ in the second row, first column (indicated by the top arrow) and ends at the ‘B’ in the thirteenth row, tenth column (indicated by the bottom arrow).

\n

Tracing the path of connected ‘B’s:

\n
    \n
  1. (Row 2, Col 1) - Start
    2. \n
  2. (Row 3, Col 1)
    3. \n
  3. (Row 4, Col 1)
    4. \n
  4. (Row 4, Col 2)
    5. \n
  5. (Row 4, Col 3)
    6. \n
  6. (Row 4, Col 4)
    7. \n
  7. (Row 5, Col 4)
    8. \n
  8. (Row 6, Col 4)
    9. \n
  9. (Row 7, Col 4)
    10. \n
  10. (Row 7, Col 5)
    11. \n
  11. (Row 7, Col 6)
    12. \n
  12. (Row 6, Col 6)
    13. \n
  13. (Row 5, Col 6)
    14. \n
  14. (Row 5, Col 7)
    15. \n
  15. (Row 5, Col 8)
    16. \n
  16. (Row 6, Col 8)
    17. \n
  17. (Row 7, Col 8)
    18. \n
  18. (Row 8, Col 8)
    19. \n
  19. (Row 9, Col 8)
    20. \n
  20. (Row 10, Col 8)
    21. \n
  21. (Row 10, Col 7)
    22. \n
  22. (Row 10, Col 6)
    23. \n
  23. (Row 10, Col 5)
    24. \n
  24. (Row 11, Col 5)
    25. \n
  25. (Row 12, Col 5)
    26. \n
  26. (Row 12, Col 4)
    27. \n
  27. (Row 12, Col 3)
    28. \n
  28. (Row 13, Col 3)
    29. \n
  29. (Row 14, Col 3)
    30. \n
  30. (Row 14, Col 4)
    31. \n
  31. (Row 14, Col 5)
    32. \n
  32. (Row 14, Col 6)
    33. \n
  33. (Row 14, Col 7)
    34. \n
  34. (Row 13, Col 7)
    35. \n
  35. (Row 12, Col 7)
    36. \n
  36. (Row 12, Col 8)
    37. \n
  37. (Row 12, Col 9)
    38. \n
  38. (Row 12, Col 10)
    39. \n
  39. (Row 13, Col 10) - End
    40. \n
\n

There are 39 letters ‘B’ in the path.

\n

\\boxed{39}

\n

VL Reasoning Next

\n

User

\n

The screenshot shows a snapshot of a LaTex text editor a student uses for writing their PhD dissertation. What is the minimum number of chapter .tex files the student has opened?

\n

Let’s think step by step and give the final answer in curly braces, like this: {final answer} \"image\"

\n

Qwen3.5

\n

<think> The user wants me to count the minimum number of chapter .tex files opened in the editor based on the screenshot.

\n
    \n
  1. Identify the relevant area: I need to look at the top of the editor window where the open tabs are displayed.
    2. \n
  2. Scan the tabs: Let’s look at the tabs from left to right.\n\n
    3. \n
\n

Let’s look more closely at the tabs.

\n\n

Let’s look at the file structure on the left to confirm naming conventions.

\n\n

Now let’s re-examine the tabs at the top.

\n
    \n
  1. chapte...
    2. \n
  2. chapte...
    3. \n
  3. thesis... (This is the root file, thesis.tex as indicated by %!TEX root = ./thesis.tex in the code).
    4. \n
  4. chapte...
    5. \n
  5. chapte...
    6. \n
  6. ch... (This is visible just before the navigation arrows < >).
    7. \n
\n

So, excluding thesis..., there are tabs starting with chapte or ch. Let’s count them:

\n
    \n
  1. chapte...
    2. \n
  2. chapte...
    3. \n
  3. chapte...
    4. \n
  4. chapte...
    5. \n
  5. ch...
    6. \n
\n

That makes 5 tabs that look like chapter files. Wait, let me look really closely at the tab bar.

\n\n

Actually, let’s look at the active code. The code shows \\chapter{Introduction}. This suggests the current file is likely chapter1.tex or similar. The tab bar shows:

\n\n

Let’s count the tabs that start with “chapte” or “ch”.

\n
    \n
  1. First tab: chapte...
    2. \n
  2. Second tab: chapte...
    3. \n
  3. Fourth tab: chapte...
    4. \n
  4. Fifth tab: chapte...
    5. \n
  5. Sixth tab: ch... (This is right next to the < > arrows).
    6. \n
\n

So that’s 5 tabs that are likely chapter files. Wait, let’s look at the third tab thesis.... That’s the main file. So the count is 1, 2, 4, 5, 6. That is 5 files.

\n

Let me recount carefully. From left to right in the tab bar:

\n
    \n
  1. chapte...
    2. \n
  2. chapte...
    3. \n
  3. thesis...
    4. \n
  4. chapte...
    5. \n
  5. chapte...
    6. \n
  6. ch... (This is cut off but visible).
    7. \n
\n

So there are 5 tabs that start with “chapte” or “ch”. Is it possible that thesis... is a chapter file? Unlikely, usually thesis.tex is the main file. The first line of code says %!TEX root = ./thesis.tex, confirming the main file is open. So the other tabs are the included files. The tabs are:

\n
    \n
  1. chapte...
    2. \n
  2. chapte...
    3. \n
  3. thesis... (Not a chapter file)
    4. \n
  4. chapte...
    5. \n
  5. chapte...
    6. \n
  6. ch...
    7. \n
\n

So there are 5 chapter files open.

\n

Let’s look at the structure panel on the left. It lists Chapter1/chapter1, Chapter2/chapter2, etc. The open tabs correspond to files. The tabs are:

\n\n

Wait, let’s look at the very top left of the tab bar. There is a Structure panel tab, then the file tabs. The file tabs are:

\n
    \n
  1. chapte...
    2. \n
  2. chapte...
    3. \n
  3. thesis...
    4. \n
  4. chapte...
    5. \n
  5. chapte...
    6. \n
  6. ch...
    7. \n
\n

Let’s assume ch... is a chapter file. So that’s 5 chapter files.

\n

Let’s re-read the tabs carefully. Tab 1: chapte... Tab 2: chapte... Tab 3: thesis... Tab 4: chapte... Tab 5: chapte... Tab 6: ch...

\n

Is it possible that thesis... is thesis-info.tex? In the file tree, there is thesis-info. But the tab says thesis.... And the code has %!TEX root = ./thesis.tex. This usually appears in sub-files. The active file in the editor has \\chapter{Introduction}. This is likely chapter1.tex. The tab corresponding to the active file is usually highlighted or distinct. Here, the tabs look similar. However, the code visible is clearly a chapter file. The tab bar has 6 visible file tabs. One is thesis.... The others start with chapte or ch. So 5 tabs start with chapte or ch.

\n

Let’s look really closely at the 6th tab. It says ch.... It’s next to the < > navigation buttons. So, tabs 1, 2, 4, 5, 6 are chapter files. That makes 5.

\n

Let’s check if thesis... could be a chapter file. In the file tree, there is thesis.tex at the top. There is also thesis-info. The tab says thesis.... It’s likely thesis.tex. So the minimum number of chapter files is 5.

\n

Let me double check the count. Tab 1: chapte... Tab 2: chapte... Tab 3: thesis... Tab 4: chapte... Tab 5: chapte... Tab 6: ch...

\n

Yes, 5 tabs look like chapter files.

\n

Wait, is it possible that the first tab is chapter1.tex and the second is chapter2.tex etc? The file tree shows Chapter1/chapter1, Chapter2/chapter2… The tabs are truncated. Let’s assume the question asks for the number of opened chapter .tex files. Based on the tabs, there are 5 tabs that start with “chapte” or “ch”. So the answer is 5.

\n

Let me look again. Tab 1: chapte... Tab 2: chapte... Tab 3: thesis... Tab 4: chapte... Tab 5: chapte... Tab 6: ch...

\n

Is it possible that thesis... is thesis.tex and not a chapter file? Yes. So the count is 5.

\n

Let’s look at the file tree to see if there are files named chapter.... Yes: Chapter1/chapter1, Chapter2/chapter2, Chapter3/chapter3, etc. So the tabs chapte... correspond to these.

\n

Is it possible that one of the chapte... tabs is actually chapters.tex or something? Unlikely given the context of a thesis. Is it possible that ch... is chapter1.tex? Yes.

\n

So, counting the tabs that are likely chapter files:

\n
    \n
  1. chapte...
    2. \n
  2. chapte...
    3. \n
  3. chapte...
    4. \n
  4. chapte...
    5. \n
  5. ch...
    6. \n
\n

Total = 5.

\n

Let’s verify the tab count again. Left side: Structure panel. Top bar:

\n\n

Wait, looking very closely at crop 1 and 2. Crop 1 shows the file tree. Crop 2 shows the top tabs. Tabs visible:

\n\n

So there are 5 tabs that start with chapte or ch. The tab thesis... is likely the main file. So the answer is 5.

\n

Let me reconsider if thesis... could be a chapter file. In the file tree, there is thesis.tex at the very top. There is thesis-info inside BIBLIOGRAPHY? No, inside thesis.tex structure? Actually, under thesis.tex in the structure panel:

\n\n

Let’s count again. Tabs:

\n
    \n
  1. chapte...
    2. \n
  2. chapte...
    3. \n
  3. thesis...
    4. \n
  4. chapte...
    5. \n
  5. chapte...
    6. \n
  6. ch...
    7. \n
\n

That is 5 tabs starting with “chapte” or “ch”. So the minimum number of chapter files opened is 5.

\n

Wait, is it possible that ch... is chapter...? Yes. Is it possible that thesis... is a chapter file? No, thesis.tex is the root. So the answer is 5.

\n

Let me check if I missed any tabs. Between thesis... and the right side. thesis..., chapte..., chapte..., ch.... Yes, that’s 3 tabs to the right of thesis.... And 2 tabs to the left of thesis.... Total chapte... tabs = 2 + 3 = 5.

\n

So the answer is 5. </think>

\n

The user wants to know the minimum number of chapter .tex files opened. Looking at the tab bar at the top of the editor window:

\n
    \n
  1. There is a tab chapte...
    2. \n
  2. There is a tab chapte...
    3. \n
  3. There is a tab thesis... (This is likely the main thesis.tex file, especially since the code contains %!TEX root = ./thesis.tex).
    4. \n
  4. There is a tab chapte...
    5. \n
  5. There is a tab chapte...
    6. \n
  6. There is a tab ch... (visible just before the navigation arrows < >).
    7. \n
\n

Excluding the thesis... tab, there are 5 tabs that start with “chapte” or “ch”, which correspond to chapter files (e.g., chapter1.tex, chapter2.tex, etc., as seen in the file structure on the left).

\n

Therefore, the minimum number of chapter .tex files opened is 5.

\n

{5}

\n

STEM Next

\n

User

\n

如图(a)，质量为m的篮球从离地H高度处由静止下落，与地面发生一次非弹性碰撞后反弹至离地h的最高处。设篮球每次与地面碰撞的碰后速率与碰前速率之比相同，重力加速度为g，不计空气阻力。

\n

如图(a)，若篮球反弹至最高处h时，运动员向下拍球，对篮球施加一个向下的压力F，持续作用至h_0高度处撤去，使得篮球与地面碰撞一次后恰好反弹至h高度处，力F的大小随高度y的变化如图(b)所示，其中h_0已知，求F_0的大小；$\"image\"

\n

\"image\"

\n

Qwen3.5

\n

<think>

\n

本题主要考查功能关系、动能定理以及非弹性碰撞中的能量损失问题。

\n

第一步：分析篮球第一次下落和反弹的过程，求出碰撞恢复系数（速率比）。

\n
    \n
  1. \n

    下落过程： 篮球从高度 H 静止下落。 根据机械能守恒（或动能定理）： mgH = 1/2mv₁² 解得落地前速度大小：v₁ = √2gH

    \n
    2. \n
  2. \n

    反弹过程： 篮球反弹至高度 h。 根据机械能守恒（或动能定理）： 1/2mv₂² = mgh 解得反弹后速度大小：v₂ = √2gh

    \n
    3. \n
  3. \n

    确定速率比 k： 题目已知“碰后速率与碰前速率之比相同”，设该比值为 k。 k = v₂/v₁ = √2gh/√2gH = √(h/H)

    \n
    4. \n
\n

第二步：分析拍球过程，计算力 F 做的功。

\n
    \n
  1. \n

    过程描述： 篮球在高度 h 处被向下拍，力 F 作用从 y=h 到 y=h₀。 注意图(b)中横坐标是高度 y。 当 y=h 时，F=0。 当 y=h₀ 时，F=F₀。 力 F 的方向向下，位移方向向下（从 h 到 h₀）。 力 F 随高度 y 线性变化。我们需要计算力 F 做的功 W_F。 由于力 F 是变力，且 F−y 图象是线性的，功等于 F−y 图线与 y 轴围成的面积（注意位移方向）。 或者直接用平均力计算。 位移大小 Δ y = h − h₀。 力从 0 增加到 F₀。 W_F = F̄ ⋅ Δ y = (0 + F₀)/2 ⋅ (h − h₀) = 1/2F₀(h − h₀)。

    \n

    仔细看图(b)：横轴是 y（高度）。 在 y=h 处，F=0。 在 y=h₀ 处，F=F₀。 力 F 的方向是向下的。位移是从 h 到 h₀（向下）。 所以力 F 做正功。 W_F = ∫_(h₀)ʰ F(y) dy (注意积分方向，或者直接用面积)。 图象是一个三角形，底边长为 h − h₀，高为 F₀。 面积 S = 1/2 ⋅ 底 ⋅ 高 = 1/2 F₀ (h − h₀)。 所以 W_F = 1/2 F₀ (h − h₀)。

    \n
    2. \n
\n

第三步：分析拍球后篮球的运动过程。

\n
    \n
  1. \n

    拍球结束瞬间（高度 h₀）： 设此时速度为 v₃。 从 h 到 h₀ 的过程，应用动能定理： W_F + mg(h − h₀) = 1/2mv₃² − 0 （注意：题目说“反弹至最高处h时…施加压力”，说明在h处速度为0）。 所以：1/2 F₀ (h − h₀) + mg(h − h₀) = 1/2mv₃² — (式1)

    \n
    2. \n
  2. \n

    从 h₀ 下落到地面： 设落地前速度为 v₄。 从 h₀ 到地面，只有重力做功。 1/2mv₄² = 1/2mv₃² + mgh₀ 结合(式1)： 1/2mv₄² = [1/2 F₀ (h − h₀) + mg(h − h₀)] + mgh₀ 1/2mv₄² = 1/2 F₀ (h − h₀) + mgh − mgh₀ + mgh₀ 1/2mv₄² = 1/2 F₀ (h − h₀) + mgh — (式2) 其实也可以直接对从 h 到地面的全过程用动能定理： W_F + mgh = 1/2mv₄² − 0 1/2 F₀ (h − h₀) + mgh = 1/2mv₄²。结果一致。

    \n
    3. \n
  3. \n

    与地面碰撞： 碰前速率 v₄，碰后速率 v₅。 根据题目条件，速率比 k = √(h/H)。 v₅ = k v₄ = √(h/H) v₄。

    \n
    4. \n
  4. \n

    反弹至高度 h： 题目说“恰好反弹至h高度处”。 这意味着反弹后的动能全部转化为重力势能到达高度 h。 1/2mv₅² = mgh 即 v₅ = √2gh。

    \n
    5. \n
\n

第四步：联立方程求解 F₀。

\n

由碰撞关系： v₅ = √(h/H) v₄ 代入 v₅ = √2gh： √2gh = √(h/H) v₄ 2gh = h/H v₄² v₄² = 2gH 这意味着拍球后，篮球落地时的速度大小必须等于第一次从 H 落下时的速度大小。这是合理的，因为要反弹回 h，碰后速度必须是 √2gh，而恢复系数不变，所以碰前速度必须是 √2gH。

\n

现在回到(式2)： 1/2mv₄² = 1/2 F₀ (h − h₀) + mgh 将 v₄² = 2gH 代入： 1/2m(2gH) = 1/2 F₀ (h − h₀) + mgh mgH = 1/2 F₀ (h − h₀) + mgh mg(H − h) = 1/2 F₀ (h − h₀) F₀ = (2mg(H − h))/(h − h₀)

\n

检查步骤和逻辑：

\n
    \n
  1. \n

    求 k：v_(down) = √2gH, vᵤₚ = √2gh ⇒ k = √(h/H)。正确。

    \n
    2. \n
  2. \n

    求 W_F：图(b)面积，1/2F₀(h−h₀)。正确。

    \n
    3. \n
  3. \n

    全过程能量分析： 初态：高度 h，速度 0。 末态（碰后）：高度 h，速度 0。 这不对，题目是“使得篮球与地面碰撞一次后恰好反弹至h高度处”。 这意味着： 过程1：从 h 被拍下，受力 F 到 h₀，然后自由下落到地面。 过程2：地面碰撞。 过程3：反弹上升到 h。

    \n

    让我们重新梳理能量流。 设落地前速度为 v_(impact)。 碰后速度 v_(rebound) = k ⋅ v_(impact)。 反弹高度 h’ = v_(rebound)²/2g = (k² v_(impact)²)/2g。 题目要求 h’ = h。 所以 h = ((h/H) v_(impact)²)/2g ⇒ v_(impact)² = 2gH。 这说明落地速度必须是 √2gH。

    \n

    现在看从 h 到地面的过程。 初动能 Eₖ₁ = 0 (在最高点 h 拍球)。 重力做功 W_G = mgh。 外力 F 做功 W_F = 1/2F₀(h−h₀)。 末动能 Eₖ₂ = 1/2mv_(impact)² = 1/2m(2gH) = mgH。

    \n

    根据动能定理： W_G + W_F = Eₖ₂ − Eₖ₁ mgh + 1/2F₀(h−h₀) = mgH − 0 1/2F₀(h−h₀) = mgH − mgh = mg(H−h) F₀ = (2mg(H−h))/(h−h₀)

    \n
    4. \n
\n

逻辑通顺，结果一致。

\n

最终表达式整理： F₀ = (2mg(H−h))/(h−h₀)

\n

单位：力的单位是牛顿 (N)。题目中 m 是 kg, g 是 m/s², H, h, h₀ 是 m。 分子：kg * m/s² * m = N * m = J (能量单位? 不对) $mg(H-h)$ 是能量单位 (J)。 分母 h−h₀ 是长度单位 (m)。 F₀ = J / m = N。单位正确。

\n

书写格式：

\n
    \n
  1. 设篮球第一次落地速度为 v₁，反弹速度为 v₂。 v₁ = √2gH, v₂ = √2gh 速率比 k = v₂/v₁ = √(h/H)
    2. \n
  2. 设拍球后篮球落地速度为 v₃，反弹速度为 v₄。 题目要求反弹至 h，则 v₄ = √2gh。 由 v₄ = k v₃，得 √2gh = √(h/H) v₃ ⇒ v₃ = √2gH。
    3. \n
  3. 对拍球及下落过程应用动能定理。 初状态：高度 h，速度 0。 末状态：高度 0，速度 v₃ = √2gH。 重力做功：$mgh$。 力 F 做功：由 F−y 图象面积可知 W_F = 1/2F₀(h−h₀)。 方程：mgh + 1/2F₀(h−h₀) = 1/2mv₃² − 0 mgh + 1/2F₀(h−h₀) = 1/2m(2gH) = mgH 1/2F₀(h−h₀) = mg(H−h) F₀ = (2mg(H−h))/(h−h₀)
    4. \n
\n

答案看起来没问题。

\n

</think>

\n

设篮球第一次下落触地前的速度大小为 v₁，反弹后的速度大小为 v₂。 根据机械能守恒定律有： mgH = 1/2mv₁² mgh = 1/2mv₂² 解得：v₁ = √2gH，v₂ = √2gh 由题意可知，碰后速率与碰前速率之比 k = v₂/v₁ = √(h/H)。

\n

设运动员拍球后，篮球落地前的速度大小为 v₃，碰后反弹的速度大小为 v₄。 题目要求篮球反弹后恰好能到达高度 h，根据机械能守恒定律，反弹后的速度大小应满足： 1/2mv₄² = mgh ⇒ v₄ = √2gh 根据碰撞规律 v₄ = k v₃，代入 k 值可得： √2gh = √(h/H) v₃ 解得拍球后篮球落地前的速度大小：v₃ = √2gH

\n

在运动员拍球及篮球下落的过程中，对篮球应用动能定理。 初状态：篮球在高度 h 处，速度为 0。 末状态：篮球在地面处，速度为 v₃ = √2gH。 在此过程中，重力做正功 W_G = mgh。 力 F 做正功，其大小等于 F−y 图线与 y 轴围成的面积（注意位移是从 h 到 h₀）： W_F = 1/2 F₀ (h − h₀) 根据动能定理： W_G + W_F = 1/2mv₃² − 0 mgh + 1/2 F₀ (h − h₀) = 1/2m(2gH) mgh + 1/2 F₀ (h − h₀) = mgH 1/2 F₀ (h − h₀) = mg(H − h) 解得： F₀ = (2mg(H − h))/(h − h₀)

\n

所以最终答案是 \\boxed{\\frac{2mg(H-h)}{h-h_0}}(N)。

\n

STEM Next

\n

User

\n

如图是一种矿井直线电机提升系统的原理图，在同一竖直平面的左右两边条形区域内，有垂直平面向里和向外交替的匀强磁场，每块磁场区域的高度均为L、磁感应强度大小均为B。梯箱左右两边通过绝缘支架均固定有边长为L、匝数为n、总电阻为R的正方形导线框，导线框平面与磁场垂直，上下两边水平。导线框、支架以及梯箱等的总质量为M。电机起动后两边磁场均以速度v沿竖直轨道向上匀速运动。忽略一切阻力，梯箱正常运行时防坠落装置与轨道间没有相互作用。求电机刚起动瞬间导线框ABCD所受安培力的大小； \"image\"

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Qwen3.5

\n

<think>

\n
    \n
  1. \n

    分析题目要求：

    \n\n
    2. \n
  2. \n

    分析物理过程：

    \n\n
    3. \n
  3. \n

    再次检查读图和题目细节：

    \n\n
    4. \n
  4. \n

    最终确认：

    \n\n
    5. \n
  5. \n

    关于单位：

    \n\n
    6. \n
  6. \n

    再次审题，看是否有陷阱：

    \n\n
    7. \n
  7. \n

    总结步骤：

    \n\n
    8. \n
  8. \n

    构建最终答案：

    \n\n
    9. \n
\n

</think>

\n

电机刚起动瞬间，梯箱速度为0，磁场以速度v向上运动。导线框相对于磁场向下运动，速度大小为v。

\n
    \n
  1. \n

    分析感应电动势： 导线框ABCD的边长为L，匝数为n。 由图可知，导线框ABCD的上边AB处于向外的磁场中（图中“⋅”表示），下边CD处于向里的磁场中（图中“×”表示）。 由于导线框高度为L，磁场区域高度也为L，且导线框跨越了两个相邻的磁场区域，因此AB边和CD边分别切割方向相反的磁感线。

    \n\n

    在导线框回路中，这两个电动势的方向是相同的（逆时针方向），因此总感应电动势为： E = E₁ + E₂ = 2nBLv

    \n
    2. \n
  2. \n

    分析感应电流： 根据闭合电路欧姆定律，导线框中的感应电流大小为： I = E/R = 2nBLv/R

    \n
    3. \n
  3. \n

    分析安培力： 导线框ABCD受到的安培力是AB边和CD边所受安培力的矢量和。

    \n\n

    因此，导线框ABCD所受的总安培力大小为： F = F₁ + F₂ = 2nBIL

    \n

    将电流 I 的表达式代入，得： F = 2nBL ⋅ 2nBLv/R = 4n²B²L²v/R

    \n
    4. \n
\n

所以最终答案是\\boxed{\\frac{4n^2B^2L^2v}{R}}(N)。

\n

Multilingual Support

\n

With a major leap in multilingual capabilities, Qwen3.5 now supports over 200 languages. This update prioritizes the expansion of low-resource languages. Through this broader linguistic scope, Qwen3.5 is dedicated to fostering global AI equity.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Language FamilyLanguages & Dialects
Indo-EuropeanEnglish, French, Portuguese, German, Romanian, Swedish, Danish, Bulgarian, Russian, Czech, Greek, Ukrainian, Spanish, Dutch, Slovak, Croatian, Polish, Lithuanian, Norwegian Bokmål, Norwegian Nynorsk, Persian, Slovenian, Gujarati, Latvian, Italian, Occitan, Nepali, Marathi, Belarusian, Serbian, Luxembourgish, Venetian, Assamese, Welsh, Silesian, Asturian, Chhattisgarhi, Awadhi, Maithili, Bhojpuri, Sindhi, Irish, Faroese, Hindi, Punjabi, Bengali, Oriya, Tajik, Eastern Yiddish, Lombard, Ligurian, Sicilian, Friulian, Sardinian, Galician, Catalan, Icelandic, Tosk Albanian, Limburgish, Dari, Afrikaans, Macedonian, Sinhala, Urdu, Magahi, Bosnian, Armenian, Latgalian, Scottish Gaelic, Central Kurdish, Northern Kurdish, Southern Pashto, Sanskrit, Dhundari, Marwari, Ahirani, Bagheli, Bagri, Bundeli, Braj, Kumaoni, Kashmiri
Sino-TibetanChinese (Simplified Chinese, Traditional Chinese, Cantonese), Burmese, Standard Tibetan, Meitei
Afro-AsiaticArabic (Standard, Najdi, Levantine, Egyptian, Moroccan, Mesopotamian, Ta’izzi-Adeni, Tunisian, Gulf, Algerian, Sudanese, Libyan), Hebrew, Maltese, Amharic, Tigrinya, Kabyle, Somali, West Central Oromo, Hausa
AustronesianIndonesian, Malay, Tagalog, Cebuano, Javanese, Sundanese, Minangkabau, Balinese, Banjar, Pangasinan, Iloko, Waray (Philippines), Plateau Malagasy, Malagasy, Buginese, Maori, Samoan, Hawaiian, Fijian
DravidianTamil, Telugu, Kannada, Malayalam
TurkicTurkish, North Azerbaijani, Northern Uzbek, Kazakh, Bashkir, Tatar, Crimean Tatar, Kyrgyz, Turkmen, Uyghur
Tai-KadaiThai, Lao, Shan
UralicFinnish, Estonian, Hungarian, Meadow Mari
AustroasiaticVietnamese, Khmer
Niger–CongoYoruba, Ewe, Kinyarwanda, Lingala, Northern Sotho, Nyanja, Shona, Southern Sotho, Tswana, Xhosa, Zulu, Luganda, Swati, Tsonga, Tumbuka, Venda, Chokwe, Luba-Kasai, Rundi, Umbundu, Kikuyu, Kongo, Nigerian Fulfulde, Wolof, Fon, Kabiyè, Mossi, Akan, Twi, Bambara, Igbo
OtherJapanese, Korean, Georgian, Basque, Haitian, Papiamento, Kabuverdianu, Tok Pisin, Swahili, Central Aymara, Tulu, Nagamese, Nigerian Pidgin, Mauritian Creole, Sango, Ayacucho Quechua, Halh Mongolian, Southwestern Dinka, Nuer, Guarani
\n

Citation

\n

Feel free to cite the following article if you find Qwen3.5 helpful:

\n
@misc{qwen35blog,\n    title = {Qwen3.5: Towards Native Multimodal Agents},\n    url = {https://qwen.ai/blog?id=qwen3.5},\n    author = {Qwen Team},\n    month = {February},\n    year = {2026}\n}\n
\n", "date_published": "2026-02-15T20:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen3.5", "id": "urn:aggr:item:a58d71392e3a0388d1a696a1b83b06828653dfd3", "image": "https://img.alicdn.com/imgextra/i4/O1CN01Ux3jxM1qOBXoBo82c_!!6000000005485-2-tps-1590-954.png", "summary": "QWEN CHAT GitHub Hugging Face ModelScope DISCORD We are delighted to announce the official release of Qwen3.5, introducing the open-weight of the first model in the Qwen3.5 series, namely Qwen3.5-397B-A17B. As a native vision-language…", "tags": [ "ai", "open-source" ], "title": "Qwen3.5: Towards Native Multimodal Agents", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-02-15-qwen3-5-towards-native-multimodal-agents/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

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QWEN CHAT DISCORD

\n

We are launching Qwen-Image-2.0, a next-generation foundational image generation model. The key highlights of Qwen-Image-2.0 include:

\n\n

Model Performance

\n

We conducted blind testing on AI Arena. Results show that Qwen-Image-2.0, as a unified generation-and-editing model, achieves superior performance on both text-to-image and image-to-image benchmarks using the same model.

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\"\"

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\"\"

\n

Model Introduction

\n

Before introducing Qwen-Image-2.0, let’s first review the evolution of Qwen-Image through a single-slide PPT:

\n

\"\"

\n

As shown in the slide, prior to Qwen-Image-2.0, we explored two parallel tracks: the generation track and the editing track. On the generation track, we focused on improving accuracy and realism in image synthesis—Qwen-Image (released in August) emphasized precise text rendering, while Qwen-Image-2512 (released in December) enhanced detail fidelity and photorealism. On the editing track, we explored functionality and consistency—from single-image editing in August, to multi-image editing in September, to consistency improvements in December. Today, Qwen-Image-2.0 successfully merges these two tracks into one unified model, delivering excellent results on both tasks simultaneously.

\n

So, what are the practical strengths of this new model? Let’s revisit that PPT slide. Sharp-eyed readers may have noticed that the slide itself was not manually crafted—in fact, it was directly generated by Qwen-Image-2.0 using the following prompt:

\n
\n

一张深蓝色渐变背景的幻灯片。标题是“Qwen-Image发展历程”。下方一条发光时间轴，上面有多个节点。第一个节点是“2025年5月6日 Qwen-Image 项目启动”。之后分为两条支线：上方支线旁边写着"生图支线"：支线上的节点包括“2025年8月4日 Qwen-Image”（上方有一个图片。一个小女孩在黑板上用粉笔写着"文字渲染"）、“2025年12月31日 Qwen-Image-2512” （上方有一个细腻的眼睛特写图片，上方透明文本框写着"细腻刻画"）。下方支线旁边写着"编辑支线"：支线上的节点包括“2025年8月18日 Qwen-Image-Edit”（下方是一个组图，上面是戴帽子的小狗，下面是同一只小狗去除帽子的图，中间配有文字"单图编辑"）、“2025年9月22日 Qwen-Image-Edit-2509”（下方是一个组图，上方左侧是女生、上方右侧是黑色小汽车，中间配有文字“多图编辑”，下方是女生依靠在车门旁）、“2025年12月19日 Qwen-Image-Layered”（下方是一个堆叠的透明多图层，中间配有文字"图层拆分"）、“2025年12月23日 Qwen-Image-Edit-2511”（下方是一个组图，上方左侧是男生、上方右侧是女生，中间配有文字"一致性提升"，下方是他们的合影。然后两个支线合二为一，变成一个新的节点“2026年2月10日 Qwen-Image-2.0”（大字号，周围光晕显著）。

\n
\n

Analyzing this slide reveals that Qwen-Image-2.0 can not only generate a dual-track timeline of development history and accurately render every piece of text, but also execute complex “picture-in-picture” compositions. For instance, in rendering the instruction “below is a composite image: the top shows a puppy wearing a hat, the bottom shows the same puppy without the hat,” the model not only completed the rendering but also maintained visual consistency between the two images. This precise “picture-in-picture” capability makes it significantly easier to create professional PPTs.

\n

Beyond precision (“准”), another strength of Qwen-Image-2.0 is its capacity for complexity (“多”). With support for 1k-token instructions, the model can handle highly intricate rendering requests, such as the following exaggerated example:

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\n

这张图片展示了一份名为 AB Testing Results Report A/B测试结果汇报 的信息图表，内容分为左、中、右三栏。左侧栏标题为 Test Overview 测试概览。第一个板块标题是 Revenue Uplift 收入提升，中间以大号绿色字体显示 +¥237,000/月，下方括号内注明 (+¥237,000/Month)，底部文字为 基于LTV模型 (Based on LTV Model)。第二个板块标题是 ROI 投资回报率，中间显示大号绿色数字 1:4.8，底部文字为 测试投入¥49,400 (Test Investment ¥49,400)。第三个板块标题是 Scalability Score 可扩展性评分，中间展示了一个绿色进度条图标，右侧数字为 4.7/5，底部文字为 已通过全站灰度验证 (Verified via Full-site Gray Release)。第四个板块标题是 Next Steps 下一步，正文第一行为粗体的 Q3全量上线 + 监控反向指标，第二行为 Q3 Full Rollout + Monitor Reverse Metrics: Churn Risk, Support Tickets)。中间栏标题为 Statistical Analysis 统计分析，各模块间通过黑色箭头表示流程关系。左上方的方框标题为 Test Objective 测试目标，内容是 提升注册转化率 (Boost Sign-up Conversion Rate)。箭头指向右上方的方框 Variant Design 变体设计 (A vs B)，其中包含两个网页界面示意图，左侧灰色图下标为 A: Original Control，右侧带有绿色和橙色块的图下标为 B: New Variant。第二行左侧方框标题为 Traffic Allocation 流量分配，内容显示 Control A: 50% 和 Variant B: 50%。右侧方框标题为 Duration & Sample Size 持续时间与样本量，内容显示 28天 (28 Days), n=42,500/组 (Per Group)。第三行左侧方框标题为 Key Metric Tracking 核心指标追踪，下方有折线图、柱状图和秒表三个图标，分别对应标签 CTR，CVR，Avg. Session Duration。右侧方框标题为 Statistical Significance Check 显著性检验，内容为 p<0.05, 95% CI (Confidence Interval) Cohen’s d=0.32 (Small-Medium Effect)。第四行左侧方框标题为 Result Interpretation 结果解读，左侧列出了带有颜色圆点的条目：空心圆点 注册转化率，实心绿点 高率指率，空心圆点 实验跳出率，右侧有一个绿色箭头指向文字 Winner 获胜 (Significant Improvement)。流程图最终指向右下角的方框 Implementation Recommendation 落地建议，内有一个绿色对勾图标，文字为 Go Live 全量上线 (Roll out to 100%)。右侧栏标题为 Business Impact 业务影响，是一个三行两列的数据表。表头跨列标题为 Variant 变体，分为深蓝色背景的 Control A 对照组 A 和绿色背景的 Variant B 实验组 B。表格第一行左侧标签为 Conversion Rate 转化率，Control A 数据为 4.2%，Variant B 数据为 5.1%，中间有一个带 +21.4% 的绿色箭头指向右侧，Variant B 下方还有文字 p=0.003 ★ (Highly Significant)。表格第二行左侧标签为 Click-through Rate 点击率，Control A 数据为 12.7%，Variant B 数据为 14.9%，中间有一个带 +17.3% 的绿色箭头指向右侧，下方文字为 Δ=2.2pp (Percentage Points)。表格第三行左侧标签为 Bounce Rate 跳出率，Control A 数据为 58.1%，Variant B 数据为 52.6%，中间有一个绿色向下箭头，下方文字为 -5.5pp p=0.012 (Significant)。

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\n

\"\"

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Readers might wonder whether such complex prompts are user-friendly. The truth is, thanks to the world knowledge embedded in LLMs, obtaining detailed descriptive prompts is actually quite straightforward. For example, given this simple input:

\n
\n

帮我生成一个手绘风格的杭州两日禅意人文之旅双语海报

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\n

We can feed it into an LLM for rewriting, leveraging its world knowledge to produce a richly detailed prompt like this:

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这是一幅中国风手绘风格的杭州两日禅意人文之旅行程导览双语海报，整体采用淡雅米黄色仿古宣纸背景，四角饰有传统回纹边框；画面中央以一条飘逸的云纹卷轴丝带贯穿连接两天行程，上方大标题为“杭州·两日禅意人文之旅”（“Hangzhou: A Two-Day Journey of Zen, Culture, and Humanity”），副标题为“祈福·山水·寻梦”（“Prayer · Landscape · Dream-Seeking”）；左侧为“第一天：灵山祈福，登高求财”（“Day 1: Praying at Ling Shan, Ascending for Prosperity”），依次展示：“07:30 抵达灵隐”（“Arrive at Lingyin Temple”），配灵隐寺山门（牌匾写着"灵隐寺"）与香炉袅袅青烟图，文字说明“灵隐寺还愿，进香礼佛，诚心祈愿”（“Go to Lingyin Temple to fulfill a vow, offer incense, and pray sincerely”）；“10:30 永福寺寻幽”（“Explore Yongfu Temple’s Serenity”），配古朴寺院掩映于苍翠古树间图，文字说明“最美寺庙，静心宋韵”（“The most beautiful temple, serene with Song charm”）；“12:00 素斋休整”（“Vegetarian Meal & Rest”），配一碗热气腾腾素面与小茶盏置于竹编托盘上图；“16:00 龙井问茶”（“Tea Tasting at Longjing”），配层叠翠绿茶园与紫砂壶向青瓷杯倾注茶汤图，文字说明“梅家坞茶园慢饮”（“Leisurely tea tasting, Tea Garden Meijiawu”）；右侧为“第二天：西湖水墨，南宋旧梦”（“Day 2: Ink-Wash West Lake, Dreams of the Southern Song”），依次展示：“09:00 西湖游船”（“Boat Tour on West Lake”），配乌篷船泛舟湖上、三潭印月石塔倒影水中图，文字说明“泛舟赏三潭印月”（“Boating to view the Three Pools Mirroring the Moon”）；“12:00 湖畔午餐”（“Lakeside Lunch”），文字说明“体验楼外楼餐厅” (“Experience Lou Wai Lou Restaurant”)，配一盘色泽红亮的鱼，上面淋酱汁；“14:00 苏堤/浴鹄湾”（“Su Causeway / Yuhu Bay”），配拱桥横跨碧波、垂柳依依图，文字说明“漫步长堤或寻秘境”（“Stroll along the causeway or discover hidden gems”）；底部设“出行小贴士”板块（“Travel Tips”），含灯泡图标及三项提示：“住宿 龙翔桥/凤起路便捷”（“Accommodation: Longxiang Bridge / Fengqi Road for convenience”），“交通 地铁+单车最佳”（“Transport: Metro + bike is optimal”），“季节 早春注意保暖”（“Season: Dress warmly in early spring”），每项前分别配床、自行车+地铁、雪花+樱花图标；全图文字均采用楷体书法风格，中英文严格对应排布——中文在上、英文紧随其下，整体构图疏密有致、意境悠远，充满文人画气息与禅意生活美学。

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And it is precisely such complex descriptions that Qwen-Image-2.0 excels at rendering. Here is the resulting image:

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Beyond precision (“准”) and complexity (“多”), aesthetic quality (“美”) is another hallmark of Qwen-Image-2.0’s text rendering. This “beauty” manifests in the layout and composition of text and images. For example, consider this prompt:

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中国古典水墨长卷风格，竖幅构图，画面自上而下、自右向左以行楷题写柳永《雨霖铃·寒蝉凄切》全文（共12行，含标点与换行）： “寒蝉凄切，对长亭晚， 骤雨初歇。都门帐饮无绪， 留恋处、兰舟催发。 执手相看泪眼，竟无语凝噎。 念去去，千里烟波， 暮霭沉沉楚天阔。 多情自古伤离别，更那堪、 冷落清秋节！ 今宵酒醒何处？杨柳岸，晓风残月。 此去经年，应是良辰好景虚设。 便纵有千种风情，更与何人说？” 书法墨色浓淡相宜，飞白自然，笔锋遒劲中见婉转，行气连贯如流水；字迹略带微洇，仿宣纸渗透效果。背景为极简留白水墨意境：右下角绘一叶孤舟泊于浅滩，舟头微翘，缆绳轻系枯柳；左侧远景以淡墨晕染出层叠低垂的暮霭与空阔楚天，天际线处一抹青灰远山若隐若现；近景岸边斜出三两枝细柳，枝条纤柔，叶已疏落，承袭清秋萧瑟之气；柳梢悬一弯将隐未隐的残月，清冷微光映照薄雾中拂面的晓风痕迹（以几缕轻扬的柳丝与水纹示意）。整幅画气息沉郁隽永，哀而不伤，严格遵循宋词意境与传统文人画“诗书画一体”范式，无印章、无题跋、无现代元素。

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When generating mixed text-and-image compositions, the model tends to render text in blank areas to avoid obscuring the main visual subject. Additionally, the model supports multiple calligraphic styles—for instance, using Emperor Huizong of Song’s distinctive “Slender Gold” script to write his ci poem Tan Chun Ling:

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一幅宋代宫廷风格工笔重彩画：画面中央为一位身着淡青色齐胸襦裙、披浅绯色薄纱披帛的偏瘦年轻宫女，立于雕花汉白玉栏杆旁的杏花树下翩然起舞，衣袖舒展如云，裙裾微扬，足尖轻点青砖地面，姿态柔婉而端庄；背景为春日皇家苑囿，枝头盛放粉白相间的重瓣杏花，花瓣随风轻落，树影婆娑；远处可见一角飞檐翘角的宫殿轮廓与半掩的朱红宫墙；左上角一泓清池初解冻，浮着细碎冰晶，画面右上方悬垂一道素雅湘竹帘，帘旌正被微风悄然吹动。整幅画采用绢本设色，色调清丽雅致。画面自上而下、自右向左以瘦金体工整题写全文：“帘旌微动，峭寒天气，\\n龙池冰泮。\\n杏花笑吐香犹浅，\\n又还是、春将半。\\n清歌妙舞从头按。\\n等芳时开宴。\\n记去年、对著东风，\\n曾许不负莺花愿。” 字体纤劲挺拔，笔锋锐利如削，墨色乌亮。

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Or, we can stress-test small regular script (xiaokai) using the Preface to the Poems Composed at the Orchid Pavilion:

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一幅水墨设色长卷风格中国画。 画面中央偏右绘一位魏晋风度的文人雅士，身着宽袖素色交领袍服，头戴小冠，跽坐于兰亭水畔青石之上，左手轻抚膝前古琴，右侧远景为会稽山阴连绵青黛山峦，山间隐现曲径与飞檐亭角；近景溪水蜿蜒，留白处氤氲水气。画面自上而下、自右向左用王羲之小楷写着“永和九年，岁在癸丑，暮春之初，\\n会于会稽山阴之兰亭，修禊事也。\\n群贤毕至，少长咸集。\\n此地有崇山峻岭，茂林修竹，\\n又有清流激湍，映带左右，\\n引以为流觞曲水，列坐其次。\\n虽无丝竹管弦之盛，一觞一咏，\\n亦足以畅叙幽情。是日也，\\n天朗气清，惠风和畅。\\n仰观宇宙之大，俯察品类之盛，\\n所以游目骋怀，足以极视听之娱，\\n信可乐也。夫人之相与，俯仰一世。\\n或取诸怀抱，悟言一室之内；\\n或因寄所托，放浪形骸之外。\\n虽趣舍万殊，静躁不同，\\n当其欣于所遇，暂得于己，\\n快然自足，不知老之将至。\\n及其所之既倦，情随事迁，感慨系之矣。\\n向之所欣，俯仰之间，已为陈迹，\\n犹不能不以之兴怀，况修短随化，\\n终期于尽！古人云，死生亦大矣。\\n岂不痛哉！每览昔人兴感之由，若合一契，\\n未尝不临文嗟悼，不能喻之于怀。\\n固知一死生为虚诞，齐彭殇为妄作。\\n后之视今，亦犹今之视昔，悲夫！\\n故列叙时人，录其所述，虽世殊事异，\\n所以兴怀，其致一也。\\n后之览者，亦将有感于斯文。”

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As shown, Qwen-Image-2.0 accurately renders nearly the entire Preface in small regular script, with only a handful of characters imperfect.

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Beyond “precision,” “complexity,” “aesthetics,” and “alignment,” another characteristic of Qwen-Image-2.0’s text rendering is realism (“真”). Consider this prompt:

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A wide-angle smartphone photograph of a modern glass whiteboard mounted on a wall inside a bright, airy office room with floor-to-ceiling windows overlooking the Great Wall of China winding across misty mountain ridges at golden hour — warm sunlight casts soft reflections and long shadows across the scene.\\nCentered in the frame, a woman in her late 20s wearing a relaxed-fit white t-shirt prominently featuring a sleek “Qwen-Image” logo in gradient blue typography is writing on the board with a fine-tip magnetic stylus.\\nHer handwriting is natural, slightly imperfect, and expressive — with visible pressure variation, subtle smudges, and organic line weight — conveying authentic human authorship.\\nIn the lower-left corner of the glass surface, the photographer’s faint but unmistakable reflection appears: blurred outline of a person holding a phone at arm’s length, capturing the moment.\\n\\nOn the left side of the whiteboard, clean, legible handwritten text appears in dark gray marker with exceptional stroke fidelity:\\n’Qwen-Image-2.0 Core Innovations:\\n• Complex Typography Engine: 1K-token instruction support for professional PPTs, posters & infographics — pixel-perfect multi-script layout, sophisticated text-image composition, and complete rendering of large-volume textual content\\n• Extreme Photorealism: Native 2K resolution (2048×2048) with microscopic detail on skin pores, fabric weave, architectural textures & natural foliage\\n• Unified Omni Model: Generation + editing in one model — full-stack multimodal understanding and generation capabilities seamlessly integrated\\n• 7B Efficiency: 2K image generation in seconds — optimal balance between visual fidelity and inference speed’\\n\\nOn the right side of the whiteboard, vertically aligned technical notes in crisp marker:\\n’Why It Matters:\\n→ One model delivers photorealistic imagery AND pixel-perfect text rendering simultaneously\\n→ One model powers both text-to-image generation AND precise image editing without pipeline switching\\n→ One model unifies deep multimodal understanding AND high-fidelity generation in a single 7B architecture’\\n\\nIn the bottom-right corner, a hand-drawn schematic in precise strokes:\\n’[8B Qwen3-VL Encoder] → [7B Diffusion Decoder] → pixels (2048×2048)’\\n— arrows flow with perspective depth, boxes feature soft shading, resolution specs annotated in fine print.\\n\\nThe glass surface exhibits realistic optical properties.\\nBackground includes minimalist wooden shelving with design magazines open to full-bleed infographics — one prominently displays a crisp cover reading “Qwen 3.5” in bold modern typography — and a potted fiddle-leaf fig with individually rendered leaf veins partially visible out-of-focus.

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In this example, the model renders text across multiple media types: on glass whiteboards, clothing, and magazine covers. These surfaces differ in material properties and spatial orientation, yet Qwen-Image-2.0 accurately renders text on each while preserving realistic lighting, reflections, and perspective—greatly enhancing the authenticity of the generated image. This realism also shines when photorealistic imagery and text coexist, as in movie posters:

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这是一张写实风格的"千灯问心"电影海报，画面以唐代长安城楼为背景，青灰色砖石城墙斑驳沧桑，城垛间飘着细雨，远处暗云低垂压着朱雀大街，整体色调偏冷灰蓝，突出历史厚重感与悬疑张力。画面中央五位主角呈对称布局：正中央是身着玄色锦袍的青年男子（约二十八岁），腰佩错金玉带，手持半卷书，眼神锐利如刀锋直视镜头；其左上方是束发执剑的少女（约十九岁），黑底暗纹劲装勾勒利落身姿，左手结印施法，发梢沾着雨珠；左下方是素衣女子（约二十六岁），手持一盏琉璃心灯，灯芯微光摇曳，指尖轻触灯罩纹路，神情凝重；右上方是虬髯将军（约三十五岁），玄甲覆着雨痕，左手按剑柄右手握虎符，下颌紧绷显威严；右下方是绛紫襦裙的成熟女子（约三十一岁），发髻簪银螭簪，手持竹简垂目沉思，衣褶处雨水滴落痕迹清晰可见。五人站位精准——中央人物略前倾，左右人物呈阶梯式错落，面部光影采用电影级侧逆光处理，突出丝绸反光、皮革纹理与金属冷感，背景虚化保留城墙雨痕与远处灯笼微光，既写实又不喧宾夺主。 文字元素密集而考究：顶部"「星河视频 独家出品」“与”「幻影文化」“等出品方LOGO以烫金浮雕字体嵌入城楼飞檐；中央主标题”「千灯问心」“采用立体阴刻工艺，字面覆仿古铜锈与细微裂纹，边缘透出内敛金光；标题下方”「3月15日 长安夜 真相现」“以烫银楷体呈现于半透明绢布；左侧垂直排列”「监制：陈某」“与”「领衔主演：周某 饰 沈知微 张某 饰 寂元 陈某 饰 张玄 俞某 饰 苏仪 胡某 饰 王明远」"；底部制作信息以极简衬线字体密集标注"「出品：玄光影业 星穹传媒」"、"「联合出品：幻影文化 云梦工作室 星河娱乐 梦境影视 无界影业 灵寒制作 虚空映画 琉璃影业 天启映画 光影未来」"、"「视觉指导：赵某」"、"「美术设计：屠某」"、"「发行：星耀影业」"、"「独家网络平台：星河视频」"、"「全球发行：寰宇影联」"、"「特效制作：幻境视界」"、"「音乐制作：天籁音坊」“及”「星河影视 全球同步上映」"，所有文字均与画面材质光影自然融合，无浮夸特效，彰显电影工业级制作的沉稳高级感。

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Beyond “precision,” “complexity,” “aesthetics,” and “realism,” Qwen-Image-2.0 also excels at alignment and organization (“齐”). Consider this example:

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Chinese ink painting calendar for February 2026, vertical composition on crimson silk texture with gold foil accents, festive vermilion and gold palette: TOP SECTION: Bold vermilion calligraphy “二月” centered at top with subtle gold leaf shimmer. MIDDLE SECTION: Glowing red lanterns floating above ancient courtyard at night, family reunion scene with steaming dumplings on wooden table, distant fireworks illuminating indigo sky with snowflakes, plum blossoms framing composition, traditional paper-cut window decorations visible through lattice windows. BOTTOM SECTION: Clean 7-column calendar grid with 6 rows, subtle grid lines in pale gold, each cell containing Chinese text as follows: Row 1 (weekdays header in pale grey): “日” “一” “二” “三” “四” “五” “六” Row 2: - Sunday cell: “腊月十四 1日” - Monday cell: “腊月十五 2日” - Tuesday cell: “腊月十六 3日” - Wednesday cell: “腊月十七 4日” - Thursday cell: “腊月十八 5日” - Friday cell: “腊月十九 6日” - Saturday cell: “腊月二十 7日” Row 3: - Sunday cell: “腊月廿一 8日” - Monday cell: “腊月廿二 9日” - Tuesday cell: “腊月廿三 10日” - Wednesday cell: “腊月廿四 11日” - Thursday cell: “腊月廿五 12日” - Friday cell: “腊月廿六 13日” - Saturday cell: “腊月廿七 14日” with light purple background rectangle underneath text labeled “春节调休（班）” Row 4: - Sunday cell: “腊月廿八 15日” with light purple background rectangle underneath text labeled “春节（休）” - Monday cell: “腊月廿九 16日” with light purple background rectangle underneath text labeled “除夕” - Tuesday cell: “正月初一 17日” with light purple background rectangle underneath text labeled “春节（休）” and red circle surrounding the number “17” - Wednesday cell: “正月初二 18日” with light purple background rectangle underneath text labeled “雨水” and light purple background rectangle underneath text labeled “春节（休）” - Thursday cell: “正月初三 19日” with light purple background rectangle underneath text labeled “春节（休）” - Friday cell: “正月初四 20日” with light purple background rectangle underneath text labeled “春节（休）” - Saturday cell: “正月初五 21日” with light purple background rectangle underneath text labeled “春节（休）” Row 5: - Sunday cell: “正月初六 22日” with light purple background rectangle underneath text labeled “春节（休）” - Monday cell: “正月初七 23日” with light purple background rectangle underneath text labeled “春节（休）” - Tuesday cell: “正月初八 24日” - Wednesday cell: “正月初九 25日” - Thursday cell: “正月初十 26日” - Friday cell: “正月十一 27日” - Saturday cell: “正月十二 28日” with light purple background rectangle underneath text labeled “春节调休（班）” Row 6: (empty row for visual balance, subtle decorative pattern of gold coins and ingots) Minimalist negative space with auspicious cloud motifs in corners, traditional woodblock print aesthetic with gold foil accents, all text in elegant Song typeface with vermilion red for dates and deep black for lunar dates, subtle rice paper texture overlay

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In this example, all text elements are precisely aligned within the calendar grid. This alignment capability also applies to comic panels—for instance:

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一个4x6格漫画，一共4行，每行6格。每一格之间有白色的分割线。 第一排，从左到右依次为 第一格：一个凌乱的实验室中。戴眼镜、穿油污工装背带裤的男孩(小智)专注焊接发光的绿色球体。墙上贴满草图和公式。对话框显示“终于完成了！生态球”。 可爱机器人用机械臂递上咖啡，头顶显示器是笑脸。“主人，休息一下吧。明天就是大赛了” 第二格：戴眼镜、穿油污工装背带裤的男孩看向窗外，城市笼罩在灰色雾霾中。“是啊，这座城市需要它” 第三格：绿色小球的特写镜头，内部微小植物生长，发出柔和绿光。在远处，实验室角落监控的摄像头红灯闪烁。 第四格：一个冰冷的高科技实验室中。穿黑西装的面具男站在屏幕前，屏幕画面是一个机器人给戴眼镜、穿油污工装背带裤的男孩递咖啡。“哼，就凭那个小子也想赢我？我的‘净界者’才是未来！” 第五格：黑色的双手伸向工作台上发光的绿色球体。警报器红光闪烁。对话框内容：“警告！检测到非法入侵！” 第六格：戴眼镜、穿油污工装背带裤的男孩冲进实验室，实验室的支架空荡荡，一片狼藉。机器人在一旁，屏幕是担忧哭脸。男孩说：“不！我的生态球！完了…一切都完了…” 第二排，从左到右依次为 第一格：机器人拍拍戴眼镜、穿油污工装背带裤的男孩肩膀，屏幕切换成坚定表情“主人，不要放弃。我们还有时间！”。戴眼镜、穿油污工装背带裤的男孩眼中重新燃起斗志，紧握拳头。“对！我们得把它找回来！” 第二格：男孩在电脑前分析监控录像，定格在黑影翻墙瞬间。“这个背影…太熟悉了…阿凯！” 第三格：穿黑西装的面具男在高科技实验室内把玩绿色小球。 第四格：一个男孩和一个机器人躲在草丛后，拿着望远镜，对着远处灯火通明的实验室看。男孩说：“果然是他！硬抢不行，我们得智取。” 第五格：一个外卖机器人站在门前，提着餐盒：“启动伪装。目标：顶层实验室”。 第六格：一个实验室桌子上有一个打开的餐盒，里面是一个磁铁，文字气泡内容：“强磁力干扰器”。 第三排，从左到右依次为 第一格：实验室灯光闪着火花，电脑屏幕故障。穿着黑西装的面具男在修理电脑，说着：“怎么回事！”。 第二格：穿着黑西装的面具男回头，只看到开着的门。“该死！上当了！” 第三格：机器人与戴眼镜、穿油污工装背带裤的男孩汇合，两人击掌。“成功！” 第四格：回到实验室，戴眼镜、穿油污工装背带裤的男孩在工作台上修理小球。多条机械臂高速工作，出现残影。“计算中…校准中…” 第五格：桌上的小球发出红光并冒烟，戴眼镜、穿油污工装背带裤的男孩额头冒汗，说着：“不好！被他动过手脚！”。旁边有一个机器人：“别慌，我在重写代码” 第六格：舞台上有一个横幅上面写着“未来之城发明大赛”，戴眼镜、穿油污工装背带裤的男孩抱着发光的绿色小球站在舞台上，文字气泡“最后一位选手，小智和他的生态球！”。穿黑色西装的面具男在台下：“等着出丑吧。” 第四排，从左到右依次为 第一格：绿色小球释放绿色能量波纹。舞台下面有许多观众：“哇！好神奇！这技术非常有前景。” 第二格：小球突然变成红色，卷起龙卷风。穿黑色西装的面具男藏在后台，手中握着遥控器，上面有一个红色按钮。 第三格：机器人被卷入龙卷风中，对话框：“主人，快切断电源！”。 第四格：穿着制服的警察把黑西服面具男按在地上，文字气泡内容：“你被逮捕了！” 第五格：男孩抱着受损的机器人，“对不起，小铁，都是我的错”。机器人屏幕亮起虚弱笑脸：“主人…别难过…我很高兴…能帮上忙…” 第六格：男孩和机器人肩并肩站在窗边，望着晴朗干净的城市。小铁屏幕上是一个大大的爱心。右下角写着：“最好的发明，永远是爱与信任。”

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Within each comic panel, dialogue text is neatly arranged and centered inside speech bubbles, creating a natural and professional appearance. Similarly, in the OKR infographic below, similar text blocks are automatically aligned:

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图像中央偏左位置有大号加粗黑体字“OKR工作法”，其正下方为稍小字号的“提升团队效率”。从该中心文字向四周辐射出四条带箭头的连线，分别指向四个模块：右上为“实施流程”，右中为“提效机制”，右下为“常见挑战”，左下为“关键原则”。 在左上角，有一个红框矩形，内含标题“Objective (O)”，下方三行小字为：“激励性强｜清晰可感｜”、“回答‘我们想实现什么’”。该红框右侧有一条带方括号标注“[驱动]”的黑色箭头，指向右侧的“核心结构”字样。 紧邻其下方是一个蓝框矩形，标题为“Key Results (KR)”，下方三行小字为：“2-5个｜可测量｜”、“有时限｜有挑战性｜”、“回答‘如何证明实现了目标’”。该蓝框右侧有一条带方括号标注“[衡量]”的黑色箭头，也指向“核心结构”。 “核心结构”三个字位于中心文字正上方，字体加粗，下方有一条红色向下箭头指向“OKR工作法”。“实施流程”模块位于右上区域，包含三个横向排列的矩形框，由左至右依次为：- 第一个红框矩形，标题“设定对齐目标”，下方小字“Vertical & Horizontal Alignment”；- 第二个蓝框矩形，标题“周期性执行与追踪”，下方小字“周会｜仪表盘｜透明化”；- 第三个灰框矩形，标题“回顾与复盘（Retrospective）”，下方小字“学习＞考核｜持续优化”；三者之间以黑色实线箭头连接，方向从左至右。“提效机制”模块位于右侧中部，包含三个椭圆形框，从左到右排布，并由虚线连接：- 左侧椭圆：标题“聚焦优先事项：”，下方“1-3个O｜做对的事，而非做完所有事”；- 中间椭圆：标题“增强透明与协作：”，下方“全员OKR公开｜打破部门壁垒”；- 右侧椭圆：标题“激发自主性与责任感：”，下方“参与式制定｜成就感驱动内在动机”。“常见挑战”模块位于右下区域，包含两个并列的红框矩形：- 上方红框内文字为“目标设定失衡”，其下两行小字：“KR全0.3 或 全1.0 → 采用0.7理想分割”；在右侧，有红色叉号及文字“× 严禁绑定薪酬”。下方红框内文字为“OKR ≠ 绩效考核”。 关键原则”模块位于左下区域，列有四条带颜色圆点的条目：- 红色实心圆点后接“方向性 × 结果导向”；- 蓝色实心圆点后接“挑战性 × 可行性”；- 黑色实心圆点后接“透明性 × 自主性”；- 绿色实心圆点后接“周期性 × 学习性”。图像右下角绘有一个简笔画小人，戴眼镜，右手持一支红白相间的马克笔，小人头部右侧有一个对话气泡，内部文字为“区分KPI（评价奖惩）与OKR（发展对齐）”

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To recap, we’ve introduced five key characteristics of Qwen-Image-2.0’s text rendering capabilities: precision (“准”), complexity (“多”), aesthetics (“美”), realism (“真”), and alignment (“齐”). Beyond text rendering, Qwen-Image-2.0 also delivers significantly improved photorealism in non-text scenarios. For example, in this “horse riding a human” prompt:

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一片荒凉的草原延伸至远方，地面干燥龟裂，细碎尘土正因剧烈动作而扬起，在低空形成微茫的灰褐色薄雾。中景平视构图：一匹肌肉虬结、体格雄健的成年棕色马昂首跨立，前蹄重重压在一名俯卧男子的背部肩胛与脊柱之间，后腿绷紧蓄力，颈部高扬，鬃毛逆风飞扬，鼻孔张大，眼神锐利专注，充满原始压迫感。 \\n被压制的男子为白人男性，30–40岁，面部沾满尘土与汗渍，深褐色凌乱短发贴于额角，浓密胡须微湿；身着磨损严重的灰绿色中世纪风格长袍，布面可见多处撕裂与泥污，腰间系粗麻绳，脚蹬刮痕累累的及踝皮靴；身体呈强撑的俯卧撑姿态——双掌用力抵住龟裂干土，指节泛白，手臂青筋凸起，双腿向后伸直绷紧，脚趾抠入地缝，整个躯干因承重而微微震颤。 \\n背景是连绵起伏的灰蓝色山脉，轮廓冷峻，山顶隐没于低垂的铅灰色多云天幕之下，云层厚实却透出漫射柔光，光线自左前方45度自然倾泻，在马腹下、男子手背与龟裂地表投下清晰而富有体积感的阴影。整体色调严格控制在大地色系内：马毛呈暖棕褐，长袍为灰绿褐渐变，土壤是赭石、干土黄与炭灰交织，尘雾为浅褐灰，天空为哑光铅灰与云底微光的冷灰过渡。画面为写实主义高清摄影质感，纹理极度精细——可见马颈汗珠、袍子经纬线磨损、皮肤毛孔与胡茬、龟裂泥土的棱角与浮尘颗粒，氛围紧张、原始、充满生物性力量对抗的窒息张力。

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Qwen-Image-2.0 not only accurately models the “riding” action but also meticulously renders the horse’s musculature and hair, the man’s facial expression, and the cracked earth texture. Another example:

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一幅写实风格的夏日森林场景，画面中央是一片幽深静谧的林间空地，高大挺拔的橡树与山毛榉构成主体乔木层，其浓密树冠呈现深邃厚重的墨绿色，叶片表面带有细微的蜡质反光；树冠间隙中透下柔和而强烈的阳光，在空气中形成清晰可见的丁达尔光束，光束边缘略带暖金色调，与冷调绿影形成微妙对比。中景处一丛新生的枫树嫩枝舒展着鲜亮明快的翠绿色叶片，叶脉清晰、半透明感强，边缘微微卷曲，仿佛刚经历晨露洗礼。前景左侧低矮的冬青与荚蒾灌木丛披覆着哑光柔和的橄榄绿色，枝叶交错，纹理细腻，部分叶片背面泛出浅灰绿光泽。地面覆盖着厚实湿润的苔藓层，由多种苔类组成：近处是绒状垂穗藓，呈现饱满润泽的青绿色，表面凝结细小露珠；稍远处为鳞叶藓与泥炭藓交织，显出微带蓝调的灰青绿与棕绿过渡；腐叶层隐约可见，呈深褐与墨绿混融的有机质感。所有植被表面均带有自然微湿反光，空气中有极细微的悬浮微粒在光束中浮动。背景林区渐次虚化，保留层次但不抢主体，远景融入一层薄薄的蓝绿雾霭。整体光影为上午10点左右的斜射日光，明暗对比适中，绿色系通过23种以上不同明度、饱和度、冷暖倾向与材质表现（如蜡质、绒面、革质、胶质）精确区分，毫无重复感，营造出丰饶、呼吸感强烈、充满生物细节与生态真实性的夏日森林秘境。

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Qwen-Image-2.0 models over 23 distinct shades of green, with natural details rendered in exquisite fidelity.

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Beyond text-to-image generation, Qwen-Image-2.0 also delivers enhanced image editing capabilities. Excitingly, because this is a unified generation-and-editing (omni) model, improvements in text rendering and photorealism from the generation side directly benefit editing tasks across the board. For instance, thanks to enhanced text rendering, the model can directly inscribe poetry onto an existing image:

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在图片的左上角加上从右到左，从上到下写着的赵孟頫楷书“红藕香残玉簟秋。轻解罗裳，独上兰舟。云中谁寄锦书来？雁字回时，月满西楼。花自飘零水自流。一种相思，两处闲愁。此情无计可消除，才下眉头，却上心头。”

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This enhancement enables many interesting applications—for example, uploading any photo and having the model inscribe a poem onto it:

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帮我在画面上加一首诗

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Beyond text, editing photorealism has also seen significant improvement—for example:

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生成一个九宫格带不同拍照姿势的组图

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Here is a two-image editing example:

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将图1与图2中的同一位东亚男性合成一张自然合照：两人并肩站立于同一场景中，左侧人物（图1）身着米白色长袖衬衫、黑色休闲裤，佩戴黑框眼镜与米色斜挎包（包身印有黑色 “CHENG” 字样），右手轻握一折叠扇，面带温和微笑望向右前方；右侧人物（图2）身着红黑相间学士服（红色前襟饰有金色盘扣与“北航”二字刺绣，黑色披肩边缘缀深蓝花卉纹样，内搭浅灰蓝衬衫），佩戴同款黑框眼镜，双手持深灰色毕业证书，目光正视镜头，神情沉稳。背景统一为图2中的爬满常春藤的青灰色石墙，阳光从左上方45度角洒落，形成柔和丁达尔光束，照亮两人发梢与肩部；地面为浅灰花岗岩铺装，光影过渡自然。两人站姿协调，间距约30厘米，身体微向对方倾斜以体现亲密感，整体构图居中对称, 采用等效全画幅50mm镜头拍摄（f/4.0，1/160s，ISO 200），景深适中，面部清晰锐利，背景藤叶呈柔焦虚化，色调温暖真实，无拼接痕迹。

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And a cross-dimensional editing example:

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使用图一的城市照片作为底图。请勿更改照片中的真实建筑、街道、车辆或人物。保持照片的真实性。三个图二中的卡通形象在建筑物周围，一个趴在建筑物上方，一个从建筑物的右边探出头来，一个坐在建筑物前的空地上。该形象应采用扁平化的图形风格绘制，轮廓清晰，类似于壁画或海报插图。

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This report is quite lengthy—thank you for reading this far! Finally, we’d like to share the blog’s header image prompt for “Qwen Street” (we’re sure some of you will ask for it):

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冬日北京的都市街景，青灰瓦顶、朱红色外墙的两间相邻中式商铺比肩而立，檐下悬挂印有剪纸马的暖光灯笼，在阴天漫射光中投下柔和光晕，映照湿润鹅卵石路面泛起细腻反光。左侧为书法店：靛蓝色老旧的牌匾上以遒劲行书刻着"文字渲染"。店门口的玻璃上挂着一幅字，自上而下，用田英章硬笔，竖排写着“专业幻灯片\\n中英文海报\\n高级信息图”，右下角落款印章为‘1k token’朱砂印。店内的墙上，可以模糊的辨认有三幅竖排的书法作品，第一幅写着着"阿里巴巴"，第二幅写着"千问大模型"，第三福写着"图像生成"。一位白发苍苍的老人背对着镜头观赏。右侧为花店，牌匾上以鲜花做成文字"真实质感"；店内多层花架陈列红玫瑰、粉洋牡丹和绿植，门上贴了一个圆形花边标识，标识上写着"2k resolution"，门口摆放了一个彩色霓虹灯，上面写着"细腻刻画 人物 自然 建筑"。两家店中间堆放了一个雪人，举了一老式小黑板，上面用粉笔字写着"Qwen-Image-2.0 正式发布"。街道左侧，年轻情侣依偎在一起，女孩是瘦脸，身穿米白色羊绒大衣，肉色光腿神器。女孩举着心形透明气球，气球印有白色的字：“生图编辑\\n二合一”。里面有一个毛茸茸的卡皮巴拉玩偶。男孩身着剪裁合体的深灰色呢子外套，内搭浅色高领毛衣。街道右侧，一个后背上写着"更小模型，更快速度"骑手疾驰而过。整条街光影交织、动静相宜。

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That concludes the main content of this update. Happy creating with Qwen-Image-2.0!

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Citation

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If Qwen-Image-2.0 proves helpful in your research, we’d greatly appreciate your citation 📝 :)

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@misc{wu2025qwenimagetechnicalreport,\n      title={Qwen-Image Technical Report}, \n      author={Chenfei Wu and Jiahao Li and Jingren Zhou and Junyang Lin and Kaiyuan Gao and Kun Yan and Sheng-ming Yin and Shuai Bai and Xiao Xu and Yilei Chen and Yuxiang Chen and Zecheng Tang and Zekai Zhang and Zhengyi Wang and An Yang and Bowen Yu and Chen Cheng and Dayiheng Liu and Deqing Li and Hang Zhang and Hao Meng and Hu Wei and Jingyuan Ni and Kai Chen and Kuan Cao and Liang Peng and Lin Qu and Minggang Wu and Peng Wang and Shuting Yu and Tingkun Wen and Wensen Feng and Xiaoxiao Xu and Yi Wang and Yichang Zhang and Yongqiang Zhu and Yujia Wu and Yuxuan Cai and Zenan Liu},\n      year={2025},\n      eprint={2508.02324},\n      archivePrefix={arXiv},\n      primaryClass={cs.CV},\n      url={https://arxiv.org/abs/2508.02324}, \n}\n
\n", "date_published": "2026-02-10T05:08:30+00:00", "external_url": "https://qwen.ai/blog?id=qwen-image-2.0", "id": "urn:aggr:item:78767b307fcc2187613f4ab5e9eb33451a06b307", "image": "https://img.alicdn.com/imgextra/i1/O1CN01IKBGxr1EqAL8Px7tj_!!6000000000402-2-tps-1590-954.png", "summary": "QWEN CHAT DISCORD We are launching Qwen-Image-2.0, a next-generation foundational image generation model. The key highlights of Qwen-Image-2.0 include: Professional Typography Rendering: Supports 1k-token instructions for direct generation…", "tags": [ "ai", "release" ], "title": "Qwen-Image-2.0: Professional infographics, exquisite photorealism", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-02-10-qwen-image-2-0-professional-infographics-exquisite/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

Tech Report GitHub Hugging Face ModelScope DISCORD

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Introduction

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We introduce Qwen3-Coder-Next, an open-weight language model designed specifically for coding agents and local development. Built on top of Qwen3-Next-80B-A3B-Base, which adopts a novel architecture with hybrid attention and MoE, Qwen3-Coder-Next has been agentically trained at scale on large-scale executable task synthesis, environment interaction, and reinforcement learning, obtaining strong coding and agentic capabilities with significantly lower inference costs.

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Scaling Agentic Training

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Rather than relying solely on parameter scaling, Qwen3-Coder-Next focuses on scaling agentic training signals. We train the model using large collections of verifiable coding tasks paired with executable environments, enabling the model to learn directly from environment feedback. This includes:

\n\n

This recipe emphasizes long-horizon reasoning, tool usage, and recovery from execution failures, which are essential for real-world coding agents.

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Performance on Coding Agent Benchmarks

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Agent-Centric Benchmark Results

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The figure below summarizes performance across several widely used coding agent benchmarks, including SWE-Bench (Verified, Multilingual, and Pro), TerminalBench 2.0, and Aider.

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\"Qwen3

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The figure demonstrates that:

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As shown in the figure below, Our model achieves strong results on SWE-Bench Pro by scaling the number of agent turns, providing evidence that the model excels at long-horizon reasoning in multi-turn agentic tasks.

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\"SWE-Bench

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Efficiency–Performance Tradeoff

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This figure highlights how Qwen3-Coder-Next achieves an improved Pareto tradeoff between efficiency and performance.

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\"Qwen3

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This comparison makes the efficiency story clear:

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Demo

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The small and fast coder model can be integrated into different downstream applications, and below we demonstrate showcases of OpenClaw, Qwen Code, Claude Code, Web dev, browser use, Cline, etc.

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Web Dev

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Creating a Chat Interface Next

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Create an Interactive Game Next

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Building an Interactive ASCII Art Drawing Tool Next

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CLI

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Desktop Cleanup Next

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Implementing a Web Game: Zombies vs. Plants Next

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Cline

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Creating a Multicolor Animation Next

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Building an Interactive Sound Art Tool Next

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OpenClaw

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Web Page for Qwen3 Coder Next Next

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Browser Use Agent

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Searching for a Product on Amazon Next

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Testing a Website Next

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coder.qwen.ai

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Summary and Future Work

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Qwen3-Coder-Next shows promising results on coding agent benchmarks, providing good speed and reasoning abilities for practical use. While it performs competitively—even compared to some larger open-source models—there is still much room for improvement.

\n

Looking ahead, we believe strong agent skills—like using tools by itself, handling tough problems, and managing complex tasks—are key for better coding agents. Next, we plan to improve the model’s reasoning and decision-making, support more tasks, and update quickly based on how people use it.

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Citation

\n

If you find our work helpful feel free to give us a cite.

\n
@techreport{qwen_qwen3_coder_next_tech_report,\n  title        = {Qwen3-Coder-Next Technical Report},\n  author       = {{Qwen Team}},\n  url          = {https://github.com/QwenLM/Qwen3-Coder/blob/main/qwen3_coder_next_tech_report.pdf},\n  note         = {Accessed: 2026-02-03}\n}\n
\n", "date_published": "2026-02-02T20:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen3-coder-next", "id": "urn:aggr:item:039520cd772f6367215a42793681c0e01e561de4", "image": "https://img.alicdn.com/imgextra/i1/O1CN01LwK7vH21HjjuHvHWQ_!!6000000006960-2-tps-1590-954.png", "summary": "Tech Report GitHub Hugging Face ModelScope DISCORD Introduction We introduce Qwen3-Coder-Next, an open-weight language model designed specifically for coding agents and local development. Built on top of Qwen3-Next-80B-A3B-Base, which…", "tags": [ "ai", "open-source" ], "title": "Qwen3-Coder-Next: Pushing Small Hybrid Models on Agentic Coding", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-02-02-qwen3-coder-next-pushing-small-hybrid-models-on-agentic/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

Github HuggingFace Huggingface Demo ModelScope Demo Paper

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Qwen3-ASR family includes two powerful all-in-one speech recognition models and a novel non-autoregressive speech forced alignment model. Qwen3-ASR-1.7B and Qwen3-ASR-0.6B are ASR models that support language identification and ASR for 52 languages and accents. Both of them leverage large-scale speech training data and the strong audio understanding ability of their foundation model Qwen3-Omni. We conduct comprehensive internal evaluation besides the open-sourced benchmarks as ASR models might differ little on open-sourced benchmark scores but exhibit significant quality differences in real-world scenarios. The experiments reveal that the 1.7B version achieves state-of-the-art performance among open-sourced ASR models and is competitive with the strongest proprietary commercial APIs while the 0.6B version offers the best accuracy–efficiency trade-off. Qwen3-ASR-0.6B can achieve an average time-to-first-token as low as 92 ms and transcribe 2,000 seconds speech in 1 second at online async mode and a concurrency of 128. Qwen3-ForcedAligner-0.6B is an LLM based NAR timestamp predictor that is able to align text-speech pairs in 11 languages. Timestamp accuracy experiments show that the proposed model outperforms the three strongest force alignment models and takes more advantages in efficiency and versatility. To further accelerat the community research of ASR and audio understanding, we open-source the weights of the three models so as to a powerful and easy-using inference-finetune framework under the Apache 2.0 license.

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\"\"

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Model List

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
ModelSupported LanguagesSupported DialectsInference ModeAudio Types
Qwen3-ASR-1.7B & Qwen3-ASR-0.6BChinese (zh), English (en), Cantonese (yue), Arabic (ar), German (de), French (fr), Spanish (es), Portuguese (pt), Indonesian (id), Italian (it), Korean (ko), Russian (ru), Thai (th), Vietnamese (vi), Japanese (ja), Turkish (tr), Hindi (hi), Malay (ms), Dutch (nl), Swedish (sv), Danish (da), Finnish (fi), Polish (pl), Czech (cs), Filipino (fil), Persian (fa), Greek (el), Hungarian (hu), Macedonian (mk), Romanian (ro)Anhui, Dongbei, Fujian, Gansu, Guizhou, Hebei, Henan, Hubei, Hunan, Jiangxi, Ningxia, Shandong, Shaanxi, Shanxi, Sichuan, Tianjin, Yunnan, Zhejiang, Cantonese (Hong Kong accent), Cantonese (Guangdong accent), Wu language, Minnan language.Offline / StreamingSpeech, Singing Voice, Songs with BGM
Qwen3-ForcedAligner-0.6BChinese, English, Cantonese, French, German, Italian, Japanese, Korean, Portuguese, Russian, Spanish–NARSpeech
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Qwen3-ASR Key Features

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Main Features:

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\"\"

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ASR Model Performance

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We conducted a systematic evaluation of the Qwen3-ASR series across Chinese/English, multilingual settings, Chinese dialects, singing voice recognition, and challenging acoustic and linguistic scenarios. The results show that Qwen3-ASR-1.7B achieves open-source SOTA on multiple public and internal benchmarks across several dimensions. Moreover, compared with the latest ASR APIs from multiple commercial providers, it also delivers the best performance on a number of benchmarks. Specifically:

\n\n

\"\"

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Qwen3-ASR-0.6B strikes a strong balance between accuracy and efficiency: it delivers robust performance on multiple Chinese and English benchmarks, and maintains extremely low RTF and high throughput under high concurrency in both offline batch and online async inference. At concurrency 128, Qwen3-ASR-0.6b can transcribe 5 hours speech at online async mode.

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\"\"

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\"\"

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FA Model Performance

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Qwen3-ForcedAligner-0.6B outperforms Nemo-Forced-Aligner, WhisperX and Monotonic-Aligner, which are three strong E2E based forced-alignment models. And it is also proved of taking great advantages on aspects of language coverage, timestamp accuracy, speech and audio length supported.

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\"\"

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Find more detailed results in our paper.

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Qwen3-ASR Demos

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Qwen3-ASR-1.7B English Demos

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
AudioASR Results
English, low speech qualityOkay, Charles. It looks like we have a problem with the radio. What happened? Yeah, someone spilled water on their machine. I uh, yeah. Charles, can you hear us? Mamma mia.
English, multiple kinds of noiseMy girls, my girls, my girls, my girls. Ready? Hey, babe. Hey, babe. Where are you? I'm actually crazy traffic right now. Oh, really? Yeah. It's crazy. The freeway is completely stopped. Oh, you're still coming to my parents' house, right? Um, I can't really hear you, babe. What? Mariachi band playing live music, yeah. Babe. I can't. They're really loud, and I can't hear. Babe. What? Yeah, they're being really loud right now. I'm sorry. What were you saying? You're still coming to my parents' house, right? It actually started raining like crazy. What? It's raining and thundering like crazy, man. I can't hear shit. Where are you? Out of nowhere, it's just pouring. It's pouring. It's pouring like crazy. What? Insane. I don't think I can get anywhere today. It's crazy day. Babe, that's crazy. Where are you? Oh my God! Someone just hit my car. Come on, get in the car, Gabe. Oh my God! He's getting out of his car. He's getting out of his car. What? Hey, Mari, you just hit my car! Oh, babe, this guy's crazy. What the fuck? Out, out, out, out, out! How you like that? Oh, babe, he's beating the shit out of you. Hold on a sec, babe. Oh, you know I'm gonna get my gun. Here's a gun. Oh, babe, he shot me. He shot me in the leg. He shot. Oh, babe, oh fuck! Babe, I need a driveway. I need to get out of here. I need to get out of here. I'm driving away. Where are you? Babe, this is crazy. No, I'm okay. I'll be fine. I'm okay. I'm okay. I just had to drive. Oh shit, babe, I think I'm getting pulled over now. I think I'm getting pulled over. Pull over your vehicle. Oh my God, babe, hold on a sec. Baby, talk to me. Oh shit. License and registration, sir. Yeah, of course, officer. Of course. Ah, babe, this is the worst day of my life. I just got pulled over. Oh my God. Oh my God. Officer, police. Oh my God. I think a riot's breaking out. We got a crazy riot. People are going crazy. Get out of here! It's like a lotus matter. There's like a war going on or some shit.
English, rapSometimes I just feel like quitting. I still might. Why do I put up this fight? Why do I still write? Sometimes it's hard enough just dealing with real life. Sometimes I wanna jump a state and just kill mics. And so these people want my level of skills, like, but I'm still white. Sometimes I just hate life. Something ain't right. Hit the brake lights. Taste of the state's right. Drawing the blank line. It ain't my fault.
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Qwen3-ASR-1.7B Multiingual Demos

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AudioASR Results
Cross-lingual: English, French, Italian, Spanish.I'm alone, all by myself. Je suis tout seul. Sono tutto. Estoy solo.
Arabicإطلالات مكياج عيون ذهبي لسهرات صيف عشرين واحد وعشرين بأسلوب النجمات.
GermanRaptorium Bergbau scheint profitierter als Monroe als Reaktion auf die wirtschaftlichen Ausfälle zu sein.
SpanishEsta prenda es amplia, recomiendo elegir una talla menor a la habitual.
FrenchAlice et moi sommes allés à Paris voyager en train au printemps, c'était très amusant.
RussianБарсук, живущий в киевском зоопарке, совершил побег из своего вольера.
Japanese抜群の運動神経を持ち合わせ、どんな要求にも応えてきた。
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Qwen3-ASR-1.7B Chinese Demos

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AudioASR Results
Chinese, fast speed蹦出来之后，左手、右手接一个慢动作，右边再直接拉到这上面之后，直接拉到这个轮胎上，上边再接过去之后，然后上边再直接拉到这个位置了之后，右边再直接这个位置接倒过去的之后，再倒一下，然后右边再直接抓住这个上边了之后，直接从这边上边过去了之后，直接抓住这个树杈，然后这个位置直接倒到这个树杈。
Chinese, tongue twister广西壮族自治区爱吃红鲤鱼与绿鲤鱼与驴的出租车司机，拉着苗族土家族自治州爱喝自制的刘奶奶榴莲牛奶的骨质疏松症患者，遇见别着喇叭的哑巴，打败咬死山前四十四棵紫色柿子树的四十四只石狮子之后，碰到年年恋牛娘的牛郎，念着灰黑灰化肥发黑会挥发，走出香港官方网站设置组，到广西壮族自治区首府南宁市民族医院就医。
Chinese, heavy noise, low quality拨号，请再说一次，请说出您要拨打的号码。幺三五八幺八八七五七。一三五八二八八八幺八八。纠正纠正。九六九。纠正纠正，不是九六。
Chinese, song with BGM黑的白的红的粉的紫的绿的蓝的灰的，你的我的他的她的大的小的圆的扁的，好的坏的美的丑的新的旧的，各种款式各种花式，让我选择。飞得高喽，越远越好，天都沦陷，他就死掉，说明多能高兴就好，喜欢就好，没大不了，越变越小，越来越小，快要死掉也很骄傲。你不想说就别再说，我不想听不想再听，就把一切誓言当作气球一般随它而去，我不在意不会在意，随它而去随它而去。气球飘进眼里，飘进风里，结束生命。气球飘进爱里，飘进心里，慢慢死去。
Cantonese, noise今次寻寻觅觅，终于揾到my princess，肯借个场俾我哋玩。你知啦，喺香港地喺繁忙时间要揾个场嚟拍嘢系非常之难嘅。再一次多谢你哋，亦都好多谢片入边嘅每一个人。下一次我哋斗啲咩好？喺下面留言话我哋知啦。拜拜。
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Qwen3-ForcedAligner-0.6B Demos

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
AudioASR ResultsClipped Samples
English, 83sWith that next, are these frozen wood frogs? And if somebody sucks on them, it'll get rid of their fever. But make sure they're frozen, because I guess when they thaw out, they're useless. He just his immediate response is like, "You're crazy," and she's like, "Yeah." And he was the one at the very beginning that he battles with the chokes against. That was proof. Dr. Palacios is a National Board certified teacher and leading expert in early childhood education. And with over five decades of experience, she is a pioneer in the field of dual language learning and specializes in curriculum planning and instructional design. Um, and he said that being the Pez outlaw is just not what he needed to do anymore. He needed to step up and be there for Kathy. Um, he needed to stop worrying about all this nonsense. And he says he pretty much just becomes a new man because he loves her so much, which just look for value in life, to look for instruction, to look for improvement, to look for development, to look for connection with other people. You you communicate and you take this on board. Because it's just like back and forth. But then they threw some shade as well, saying that Nvidia kind of like cherry picked, um, floating like a different a type of floating point that only is.frozen:wood:frogs:Nvidia:
Chinese, noise, 28s在他十一岁的时候，母亲因为发生交通事故撒手人寰。桂兰的母亲敲了几下杯子，铁柱的手也不自觉地动了起来。你这小子是烟瘾犯了吧？被看穿的铁柱只能坦白自己正在戒烟。父亲一瞅，给铁柱提了一个建议，嘎嘎好使。不信你可以尝试一下这个绝技，就是母亲的催眠疗法，保证让你满意。铁柱听完，觉得他们是在扯淡。这种催眠疗法我根本不需要尝试。不管咋说，几个人聊的还是其乐融融。他们非常欢迎铁柱到这里做客，但这个女佣却显得异常诡异。十:一:异:常:
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ASR Results with timestamps 1

\n

With[0.48,1.04] that[1.04,1.28] next[1.44,2.00], are[2.16,2.32] these[2.32,2.48] frozen[2.48,3.20] wood[3.36,3.68] frogs[3.68,4.32]? And[4.64,4.96] if[4.96,5.20] somebody[5.20,5.60] sucks[5.68,6.24] on[6.24,6.48] them[6.56,6.96], it'll[7.04,7.28] get[7.28,7.52] rid[7.52,7.60] of[7.60,7.76] their[7.76,7.92] fever[7.92,8.40]. ......[omitted]...... saying[72.88,73.44] that[73.44,73.60] Nvidia[75.12,75.68] kind[75.68,75.84] of[75.84,75.92] like[75.92,76.08] cherry[76.08,76.40] picked[76.40,76.80], um[77.28,77.76], floating[78.64,79.04] like[79.04,79.20] a[79.20,79.20] different[79.20,79.68] a[79.68,79.68] type[79.68,79.92] of[79.92,80.08] floating[80.08,80.40] point[80.40,80.80] that[80.80,80.96] only[80.96,81.60] is[81.92,82.32].

\n

ASR Results with timestamps 2

\n

在[0.00,0.16]他[0.16,0.32]十[0.32,0.48]一[0.48,0.64]岁[0.64,0.80]的[0.80,0.88]时[0.88,0.96]候[0.96,1.12]，母[1.28,1.44]亲[1.44,1.60]因[1.60,1.76]为[1.76,1.84]发[1.84,2.08]生[2.08,2.16]交[2.16,2.40]通[2.40,2.48]事[2.48,2.72]故[2.72,2.80]撒[2.88,3.04]手[3.04,3.20]人[3.20,3.36]寰[3.36,3.52]。桂[3.52,3.68]兰[3.68,3.84]的[3.84,4.00]母[4.00,4.08]亲[4.08,4.24]敲[4.24,4.48]了[4.48,4.56]几[4.56,4.72]下[4.72,4.80]杯[4.80,5.04]子[5.04,5.20]......[omitted]......几[22.64,22.80]个[22.80,22.96]人[22.96,23.04]聊[23.04,23.28]的[23.28,23.36]还[23.36,23.52]是[23.52,23.60]其[23.60,23.84]乐[23.84,24.00]融[24.00,24.08]融[24.08,24.24]。他[24.32,24.48]们[24.48,24.56]非[24.64,24.80]常[24.80,24.96]欢[24.96,25.12]迎[25.12,25.20]铁[25.28,25.44]柱[25.44,25.52]到[25.52,25.60]这[25.60,25.76]里[25.76,25.84]做[25.84,26.00]客[26.00,26.16]，但[26.32,26.48]这[26.48,26.64]个[26.64,26.72]女[26.72,26.96]佣[26.96,27.04]却[27.04,27.20]显[27.20,27.36]得[27.36,27.52]异[27.52,27.68]常[27.68,27.84]诡[27.84,28.00]异[28.00,28.16]。

\n", "date_published": "2026-01-28T16:00:04+00:00", "external_url": "https://qwen.ai/blog?id=qwen3asr", "id": "urn:aggr:item:d6878ec7d9c07ddf6e24bff2c903abb2c3ea0f04", "image": "https://img.alicdn.com/imgextra/i2/O1CN0109ZYFB1icpHCNALJG_!!6000000004434-2-tps-1590-954.png", "summary": "Qwen3-ASR family includes two powerful all-in-one speech recognition models and a novel non-autoregressive speech forced alignment model. Qwen3-ASR-1.7B and Qwen3-ASR-0.6B are ASR models that support language identification and ASR for 52…", "tags": [ "ai", "open-source" ], "title": "Qwen3-ASR & Qwen3-ForcedAligner is Now Open Sourced: Robust, Streaming and Multilingual!", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-01-28-qwen3-asr-qwen3-forcedaligner-is-now-open-sourced-robust/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

\"Qwen3

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QWEN CHAT API DISCORD

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Introduction

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We present Qwen3-Max-Thinking, our latest flagship reasoning model. By scaling up model parameters and leveraging substantial computational resources for reinforcement learning, Qwen3-Max-Thinking achieves significant performance improvements across multiple dimensions, including factual knowledge, complex reasoning, instruction following, alignment with human preferences, and agent capabilities. On 19 established benchmarks, it demonstrates performance comparable to leading models such as GPT-5.2-Thinking, Claude-Opus-4.5, and Gemini 3 Pro.

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We further enhance Qwen3-Max-Thinking with two key innovations: (1) adaptive tool-use capabilities that enable on-demand retrieval and code interpreter invocation, now available at chat.qwen.ai; and (2) advanced test-time scaling techniques that significantly boost reasoning performance, surpassing Gemini 3 Pro on key reasoning benchmarks.

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\"\"

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The table below presents a more comprehensive set of evaluation scores.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
CapabilityBenchmarkGPT-5.2 -ThinkingClaude-Opus -4.5Gemini 3 ProDeepSeek V3.2Qwen3-Max -Thinking
KnowledgeMMLU-Pro87.489.589.885.085.7
MMLU-Redux95.095.695.994.592.8
C-Eval90.592.293.492.993.7
STEMGPQA92.487.091.982.487.4
HLE135.530.837.525.130.2
ReasoningLiveCodeBench v687.784.890.780.885.9
HMMT Feb 2599.4-97.592.598.0
HMMT Nov 25--93.390.294.7
IMOAnswerBench86.384.083.378.383.9
Agentic CodingSWE Verified80.080.976.273.175.3
Agentic SearchHLE (w/ tools)245.543.245.840.849.8
Instruction Following & AlignmentIFBench75.458.070.460.770.9
MultiChallenge57.954.264.247.363.3
Arena-Hard v2380.676.781.766.590.2
Tool UseTau² Bench480.985.785.480.382.1
BFCL-V4563.177.572.561.267.7
Vita Bench38.256.351.644.140.9
Deep Planning644.633.923.321.628.7
Long ContextAA-LCR72.774.070.765.068.7
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Adaptive Tool-Use Capabilities

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Unlike earlier approaches that required users to manually select tools before each task, Qwen3-Max-Thinking autonomously selects and leverages its built-in Search, Memory, and Code Interpreter capabilities during conversations. This capability emerges from a focused training process: after initial fine-tuning for tool use, the model underwent further training on diverse tasks using both rule-based and model-based feedback. Empirically, we observe that the Search and Memory tools effectively mitigate hallucinations, provide access to real-time information, and enable more personalized responses. The Code Interpreter allows users to execute code snippets and apply computational reasoning to solve complex problems. Together, these features deliver a seamless and capable conversational experience.

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Test-time Scaling Strategy

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Test-time scaling refers to techniques that allocate additional computation during inference to improve model performance. We propose an experience-cumulative, multi-round test-time scaling strategy for the heavy mode. Instead of simply increasing parallel trajectories N, which often yields redundant reasoning, we limit N and redirect saved computation to iterative self-reflection guided by a “take-experience” mechanism. This mechanism distills key insights from past rounds, allowing the model to avoid re-deriving known conclusions and focus on unresolved uncertainties. Crucially, it achieves higher context efficiency than naively referencing raw trajectories, enabling richer integration of historical information within the same context window. This approach consistently outperforms standard parallel sampling and aggregation with roughly the same token consumption: GPQA (90.3 → 92.8), HLE (34.1 → 36.5), LiveCodeBench v6 (88.0 → 91.4), IMO-AnswerBench (89.5 → 91.5), and HLE (w/ tools) (55.8 → 58.3).

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Develop with Qwen3-Max-Thinking

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Qwen3-Max-Thinking is now available in Qwen Chat, where users can interact with the model and its adaptive tool-use capabilities. Meanwhile, the API of Qwen3-Max-Thinking (whose model name is qwen3-max-2026-01-23) is available. You can first register an Alibaba Cloud account and activate Alibaba Cloud Model Studio service, and then navigate to the console and create an API key.

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Since the APIs of Qwen are OpenAI-API compatible, we can directly follow the common practice of using OpenAI APIs. Below is an example of using Qwen3-Max-Thinking in Python:

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from openai import OpenAI\nimport os\n\nclient = OpenAI(\n    api_key=os.getenv("API_KEY"),\n    base_url="https://dashscope-intl.aliyuncs.com/compatible-mode/v1",\n)\n\ncompletion = client.chat.completions.create(\n    model="qwen3-max-2026-01-23",\n    messages=[\n      {'role': 'user', 'content': 'Give me a short introduction to large language model.'}\n    ],\n    extra_body={"enable_thinking": True}\n)\n\nprint(completion.choices[0].message)\n
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The APIs of Qwen are also compatible with the Anthropic API protocol, enabling Qwen3-Max-Thinking to work seamlessly with Claude Code. Simply use the API key created at Alibaba Cloud account and install Claude Code to elevate your coding experience. Below is the quick start script.

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# Install Claude Code\nnpm install -g @anthropic-ai/claude-code\n\n# Configure Environment Variables\nexport ANTHROPIC_MODEL="qwen3-max-2026-01-23" \nexport ANTHROPIC_SMALL_FAST_MODEL="qwen3-max-2026-01-23"\nexport ANTHROPIC_BASE_URL=https://dashscope.aliyuncs.com/apps/anthropic\nexport ANTHROPIC_AUTH_TOKEN=your-dashscope-apikey\n\n# Execute\nclaude\n
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Citation

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Feel free to cite the following article if you find Qwen3-Max-Thinking helpful.

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@misc{qwen3maxthinking,\n    title = {Pushing Qwen3-Max-Thinking Beyond its Limits},\n    url = {https://qwen.ai/blog?id=qwen3-max-thinking},\n    author = {Qwen Team},\n    month = {January},\n    year = {2026}\n}\n
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\n
    \n
  1. \n

    We evaluated only on the text subset. ↩

    \n
    2. \n
  2. \n

    We blocked access to Hugging Face and other HLE-related websites to prevent data leakage. ↩

    \n
    3. \n
  3. \n

    For reproducibility of Arena-Hard v2, we report the win rates evaluated by GPT-4.1. ↩

    \n
    4. \n
  4. \n

    We followed the official setting of Tau² Bench with no custom scaffolding. ↩

    \n
    5. \n
  5. \n

    BFCL-V4 is configured with a maximum of 100 interaction turns. ↩

    \n
    6. \n
  6. \n

    Deep Planning is a built-in agentic benchmark. ↩

    \n
    7. \n
\n
\n", "date_published": "2026-01-25T20:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen3-max-thinking", "id": "urn:aggr:item:fc019ffe0f2fa996094ce930a268f78cdc901de9", "image": "https://img.alicdn.com/imgextra/i3/O1CN01wdW0HW1e4Eu3CE2Jb_!!6000000003817-2-tps-1590-954.png", "summary": "QWEN CHAT API DISCORD Introduction We present Qwen3-Max-Thinking, our latest flagship reasoning model. By scaling up model parameters and leveraging substantial computational resources for reinforcement learning, Qwen3-Max-Thinking…", "tags": [ "ai", "release" ], "title": "Pushing Qwen3-Max-Thinking Beyond its Limits", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-01-25-pushing-qwen3-max-thinking-beyond-its-limits/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

Github HuggingFace Huggingface Demo ModelScope Demo Paper

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Qwen3-TTS is a series of powerful speech generation capabilities developed by Qwen, offering comprehensive support for voice clone, voice design, ultra-high-quality human-like speech generation, and natural language-based voice control. It provides developers and users with the most extensive set of speech generation features available. Powered by the innovative Qwen3-TTS-Tokenizer-12Hz multi-codebook speech encoder, Qwen3-TTS achieves efficient compression and robust representation of speech signals. This not only fully preserves paralinguistic information and acoustic environmental features but also enables high-speed, high-fidelity speech reconstruction via a lightweight non-DiT architecture. Utilizing Dual-Track modeling, Qwen3-TTS achieves extreme bidirectional streaming generation speeds, where the first audio packet is delivered after processing just a single character. The entire Qwen3-TTS multi-codebook model series is now open-sourced, featuring two sizes: 1.7B and 0.6B. The 1.7B model delivers peak performance and powerful control capabilities, while the 0.6B model offers an ideal balance between performance and efficiency. The models support 10 mainstream languages (Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, and Italian) along with various dialects to meet global application demands. Furthermore, the models exhibit strong contextual understanding, allowing them to adapt tone, rhythm, and emotional expression based on instructions and text semantics, while significantly improving robustness to input text noise. Now open-sourced on GitHub and accessible via the Qwen API.

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Model List

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1.7B Model

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
ModelFeaturesLanguage SupportStreamingInstruction Control
Qwen3-TTS-12Hz-1.7B-VoiceDesignPerforms voice design based on user-provided descriptions.Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian✅✅
Qwen3-TTS-12Hz-1.7B-CustomVoiceProvides style control over target timbres via user instructions; supports 9 premium timbres covering various combinations of gender, age, language, and dialect.Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian✅✅
Qwen3-TTS-12Hz-1.7B-BaseBase model capable of 3-second rapid voice clone from user audio input; can be used for fine-tuning (FT) other models.Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian✅
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0.6B Models

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
ModelFeaturesLanguage SupportStreamingInstruction Control
Qwen3-TTS-12Hz-0.6B-CustomVoiceSupports 9 premium timbres covering various combinations of gender, age, language, and dialect.Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian✅
Qwen3-TTS-12Hz-0.6B-BaseBase model capable of 3-second rapid voice clone from user audio input; can be used for fine-tuning (FT) other models.Chinese, English, Japanese, Korean, German, French, Russian, Portuguese, Spanish, Italian✅
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Qwen3-TTS Key Features

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Main Features:

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Model Performance

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We have conducted a comprehensive evaluation of Qwen3-TTS across dimensions such as voice clone, voice design, and control. The results demonstrate that it has achieved SOTA performance across multiple metrics. Specifically:

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Tokenizer Performance

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We evaluated Qwen-TTS-Tokenizer for speech reconstruction. Results on the LibriSpeech test-clean set demonstrate that it achieves SOTA performance across all key metrics. Specifically, in Perceptual Evaluation of Speech Quality (PESQ), Qwen-TTS-Tokenizer achieved scores of 3.21 and 3.68 in wideband and narrowband respectively, significantly leading similar tokenizers. In Short-Time Objective Intelligibility (STOI) and UTMOS, Qwen-TTS-Tokenizer achieved scores of 0.96 and 4.16, demonstrating superior reconstruction quality. In speaker similarity, Qwen-TTS-Tokenizer achieved a score of 0.95, significantly surpassing comparison models, indicating its near-lossless speaker information preservation capability.

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Samples

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Qwen3-TTS-12Hz-1.7B-VoiceDesign

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Voice Design

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Qwen3-TTS supports generating customized timbre identities through natural language descriptions. Users can freely input acoustic attributes, persona descriptions, background information, and other free-form descriptions, easily creating their desired voice identities.

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Control TypeControl InstructionTextSamples
Acoustic Attribute Control采用高亢的男性嗓音，语调随兴奋情绪不断上扬，以快速而充满活力的节奏传达信息。音量要足够响亮，近乎喊叫，以体现紧迫感。发音务必清晰精准、字字分明，让每个词都铿锵有力。整体表达需流畅自然、明亮生动，富有戏剧性，展现出外向、自信且张扬的个性，同时传递出一种威严而宏大的宣告语气，洋溢着满溢的激动之情。好了各位，往后退，往后退！我有个天大的好消息要宣布：Qwen-TTS正式开源啦！
gender: Male. pitch: Low male pitch with significant upward inflections for emphasis and excitement. speed: Fast-paced delivery with deliberate pauses for dramatic effect. volume: Loud and projecting, increasing notably during moments of praise and announcements. age: Young adult to middle-aged adult. clarity: Highly articulate and distinct pronunciation. fluency: Very fluent speech with no hesitations. accent: British English. texture: Bright and clear vocal texture. emotion: Enthusiastic and excited, especially when complimenting. tone: Upbeat, authoritative, and performative. personality: Confident, extroverted, and engaging.Nine different, exciting ways of cooking sausage. Incredible. There were three outstanding deliveries in terms of the sausage being the hero. The first dish that we want to dissect, this individual smartly combined different proteins in their sausage. Great seasoning. The blend was absolutely spot on. Congratulations. Please step forward. Natasha.
展现出悲苦沙哑的声音质感,语速偏慢,情绪浓烈且带有哭腔,以标准普通话缓慢诉说,情感强烈,语调哀怨高亢,音高起伏大。皇上啊！臣妾一片真心可昭日月，为何您竟信那毒妇谗言，将我打入冷宫？这心……比雪还凉啊……
gender: Male. pitch: Artificially high-pitched, slightly lowering after the initial laugh. speed: Rapid during the laugh, then slowing to a deliberate pace. volume: Loud laugh transitioning to a standard conversational level. age: Young adult to middle-aged, performing a character voice. clarity: Clear and distinct articulation. fluency: Fluent delivery without hesitation. accent: American English. texture: Slightly strained and somewhat nasal quality. emotion: Forced amusement shifting to feigned resignation. tone: Initially playful, then shifts to a slightly put-upon tone. personality: Theatrical and expressive.Good one. Okay, fine, I'm just gonna leave this sock monkey here. Goodbye.
Age Control体现撒娇稚嫩的萝莉女声，音调偏高且起伏明显，营造出黏人、做作又刻意卖萌的听觉效果。哥哥，你回来啦，人家等了你好久好久了，要抱抱！
Speak as a sarcastic, assertive teenage girl: crisp enunciation, controlled volume, with vocal emphasis that conveys disdain and authority.Blah, blah, blah. We're all very fascinated, Whitey, but we'd like to get paid.
性别: 男性. 音高: 男性低沉音域，音高稳定. 语速: 语速稍快，节奏紧凑. 音量: 音量洪亮，力度强劲. 年龄: 中老年. 清晰度: 发音清晰，字句有力. 流畅度: 表达流畅，一气呵成. 口音: 标准普通话. 音色质感: 嗓音浑厚，略带沙哑感. 情绪: 严肃告诫，指令明确. 语调: 命令式语调，强调果断. 性格: 权威果断，不容置喙.把你所有的表情都藏在面具里，保持你的中性状态，不用表情，只用身体的语言，要记住，要学会藏。
gender: Male. pitch: Low male pitch, generally stable. speed: Deliberate pace, slowing slightly after the initial exclamation. volume: Starts loud, then transitions to a projected conversational volume. age: Middle-aged adult. clarity: High clarity with distinct pronunciation. fluency: Highly fluent. accent: American English. texture: Resonant and slightly gravelly. emotion: Initially commanding, shifting to narrative amusement. tone: Authoritative start, moving to an engaging, descriptive tone. personality: Confident and performative.Older gentleman, 110, maybe 111 years old, sort of a surly Elvis thing happening with him. He smiles like this. Seen him around?
Gradual Control性别: 男性 音高: 男性低沉音区，偶有拔高. 语速: 初始平稳，后段因激动逐渐加快. 音量: 初始音量正常，后段逐渐提高至喊叫. 年龄: 中年男性. 清晰度: 吐字清晰，发音准确. 流畅度: 言语连贯，表达自然. 口音: 标准普通话发音. 音色质感: 音质略带粗砺，富有力量感. 情绪: 初始不耐烦，迅速转为恼怒斥责. 语调: 质问命令式，语带不悦与威慑. 性格: 急躁易怒，态度强硬.你在干什么?有什么好看的?喂!我叫你走，你在干什么?给我走啊!
gender: Female. pitch: Mid-range female pitch, rising sharply with frustration. speed: Starts measured, then accelerates rapidly during emotional outburst. volume: Begins conversational, escalates quickly to loud and forceful. age: Young adult to middle-aged. clarity: High clarity and distinct articulation throughout. fluency: Highly fluent with no significant pauses or fillers. accent: General American English. texture: Bright and clear vocal quality. emotion: Shifts abruptly from neutral acceptance to intense resentment and anger. tone: Initially accepting, becomes sharply accusatory and confrontational. personality: Assertive and emotionally expressive when provoked.Okay. Yeah. I resent you. I love you. I respect you. But you know what? You blew it! And thanks to you-
Human-likeness自然感的女声，语调活泼带笑意，模仿别人‘嘘’你时压低嗓音，就是平时聊天的感觉我跟我闺蜜看电影就特别有画面感。就你知道吗，一紧张我就忍不住吃爆米花，吃得特别快，然后手里那杯可乐也跟着晃，差点就洒了，真的差一点点。然后旁边那个人就突然来一句，嘘——声音压得特别低。哎我当下那个情绪，既想笑又有点气，太尴尬了。
A relaxed, naturally expressive male voice in his late twenties to early thirties, with a moderately low pitch, casual speaking rate, and conversational volume; deliver lines with a light, self-deprecating tone, breaking into genuine, easygoing laughter at moments of embarrassment, while maintaining clear articulation and an overall warm, approachable clarity.Yeah, so—uh—I’m a digital nomad, right? So… pretty much all my communication is just, like, texts and messages. And now, you know, there’s these AI agents that can, uh… reply for you? Which is—heh—convenient, sure, I guess? But also… kinda delicate, you know? Like, you’ll type something super short—like, “Yep, sounds good”—and it’ll turn that into this whole… warm, polished paragraph. Like, way nicer than I’d ever write myself. huh… ha Seriously, I sound like a Hallmark card all of a sudden. But then… once you outsource that… what’s the other person actually hearing? Are they hearing me… or just some… generic, friendly-bot voice? Man, that’s weird to even say out loud.
Background Information角色姓名：林怀岳 音色信息：音量洪亮，音域低沉，力度感强的中年男性声音。 身份背景：某国家重点科研项目首席顾问，年近七十的资深战略科学家。曾参与国家重大科技攻关工程，历经数十年风雨，见证了从落后追赶到自主创新的艰难历程。现任国家科技咨询委员会终身荣誉委员，仍坚持在一线培养青年人才，为国家战略发展建言献策。 外貌特征：身形挺拔，两鬓斑白，眉宇间刻着岁月沉淀的坚毅。常着深色中山装或简洁正装，眼神沉静而锐利，举手投足间自带威严与从容。 性格特质：意志如钢，信念坚定，面对挑战从不退缩；胸怀家国，心系民族未来，将个人命运与国家兴衰紧密相连；严谨自律，言出必行，话语中充满责任感与历史担当；外冷内热，表面严肃，实则对后辈寄予厚望，甘为人梯。 人生信条：“我们这一代人，不是为了站在光里，而是为了把路铺到光里。”有些事，只要国家需要，就得有人扛起来。 我们那一代人，是背着泥土铺路的； 你们要做的，是让这条路，通向星辰大海。
Character Name: Marcus Cole Voice Profile: A bright, agile male voice with a natural upward lift, delivering lines at a brisk, energetic pace. Pitch leans high with spark, volume projects clearly—near-shouting at peaks—to convey urgency and excitement. Speech flows seamlessly, fluently, each word sharply defined, riding a current of dynamic rhythm. Background: Longtime broadcast booth announcer for national television, specializing in live interstitials and public engagement spots. His voice bridges segments, rallies action, and keeps momentum alive—from voter drives to entertainment news. Presence: Late 50s, neatly groomed, dressed in a crisp shirt under studio lights. Moves with practiced ease, eyes locked on the script, energy coiled and ready. Personality: Energetic, precise, inherently engaging. He doesn’t just read—he propels. Behind the speed is intent: to inform fast, to move people to act. Whether it’s “text VOTE to 5703” or a star-studded tease, he makes it feel immediate, vital.Lot being you watching. 1-866-IDLE-03 for JPL. That's 1-866-436-5703. Or text the word VOTE to 5703. Diana DeGarmo's next with more from the movies right after this brief intermission on American Idol.
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Timbre Reuse

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Users can also persistently store and repeatedly call the timbres created by Qwen3-TTS, generating vivid and natural multi-turn, multi-character long-form dialogues.

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Control InstructionTextSamples
"旁白": "声音特征沉稳、客观、略带叙事感的女播音腔，普通话标准，语速适中，带有轻微的环境氛围渲染，语调平缓但富有感染力，在关键情节时稍作停顿，增强画面感。情感冷静旁观，偶尔带一丝微妙的反讽" "小林": "25岁男性上班族，声音清亮但时常犹豫，语速时快时慢，紧张时会轻微结巴。情绪波动明显，从低声呢喃到突然激动再到自我怀疑的叹气。肢体语言丰富，经常无意识的小动作" "御姐": "模拟成熟性感的御姐音色，声音略带磁性且沉稳，语速不快不慢，语调充满自信和一丝挑逗，尾音可以稍微拖长并上扬，给人一种游刃有余的掌控感。"旁白: 小林今天第三次走神了。酒吧昏黄的灯光晃得他心跳加速，而吧台对面那个红唇微扬的女人，正用指尖轻轻摩挲着酒杯边缘。 御姐: 小弟弟，有兴趣陪姐姐喝一杯吗？ 小林: 啊？我、我……我其实不太会喝酒…… 旁白: 他的手指无意识地抠着杯沿，喉结上下滚动，像被什么无形的东西掐住了呼吸。 御姐: 不会喝？那正好——姐姐教你。这杯莫吉托，甜得刚好，就像你刚才偷看我的眼神。 小林: 我、我没偷看！……好吧，看了一眼。就一眼！ 旁白: 他猛地坐直，又立刻缩回肩膀，仿佛那句话烫伤了自己的嘴。 御姐: 紧张什么？你连坐姿都在发抖……要不要靠过来一点？这里太吵了。 小林: 靠过去？可、可我们才第一次见面……你都不认识我…… 御姐: 名字不重要，感觉才重要。......而我感觉……你有点可爱。 旁白: 小林的耳朵瞬间红透，连耳后那颗小痣都像在发烫。他想逃，脚却像钉在了高脚凳上。 小林: 可爱？没人这么说过我……他们都说我太闷，连朋友圈都发不出手…… 御姐: 那现在呢？敢不敢发一条——'今晚，和一个危险又迷人的姐姐喝了一杯'？ 小林: ……我连配图都不敢选。你笑起来太……太有杀伤力了。 御姐: 那就别发了。有些故事，只适合藏在两个人的记忆里——比如，接下来你打算请我跳支舞吗？ 旁白: 他张了张嘴，没发出声音。但这一次，他没有低头，而是轻轻推开了那杯没动过的苏打水，朝她伸出了手。
""Lucas": "Male, 17 years old, tenor range, gaining confidence - deeper breath support now, though vowels still tighten when nervous" "Mia": "Female, 16 years old, mezzo-soprano range, softening - lowering register to intimate speaking voice, consonants softening"Lucas:H-hey! You dropped your... uh... calculus notebook? I mean, I think it's yours? Maybe? Mia:Oh wow, my mortal enemy - Mr. Thompson's problem sets. Thanks for rescuing me from that F. Lucas:No problem! I actually... kinda finished those already? If you want to compare answers or something... Mia:Is this your sneaky way of saying you want to study together, Lucas? Because I saw you staring during lab partners sign-up. Lucas:What? No! I mean yes but not like... I just think you're... your titration technique is really precise! Mia:That's the nerdiest compliment I've ever gotten. Tell you what - help me survive pre-calc and I'll teach you how to actually flirt. Lucas:Wow, harsh. And here I thought my titration line was smooth. Mia:It was adorable. Like when you tripped over your shoelaces in the hall yesterday. Or that time you— Lucas:Okay okay! I get it, I'm a disaster. So... library after school? I'll bring the graphing calculators? Mia:Only if you promise not to spill coffee on my notes again... though I guess watching you panic-clean was pretty cute.
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Qwen3-TTS-12Hz-1.7B-CustomVoice

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Timbre Control

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After performing speaker-specific fine-tuning, Qwen3-TTS can maintain the target timbre while inheriting the style control capabilities and single-speaker multilingual capabilities of the base model.

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Control TypeTimbresControl InstructionTextSamples
Single Attribute Control甜茶 Ryanspoke with a very sad and tearful voice.She said she would be here by noon.
Very happy.
用特别愤怒的语气说
请特别小声的悄悄说
Speaking at an extremely slow pace
音调低沉
Multi-Attribute Control十三 Vivan性别: 女性声音. 音高: 女性中高音区，语调富于变化. 语速: 语速明快，偶有加速. 音量: 正常交谈音量，笑声响亮. 清晰度: 吐字清晰，发音标准. 流畅度: 表达流畅自如. 口音: 普通话. 音色质感: 音色明亮，略带爽朗. 情绪: 愉悦友好，伴随爽朗笑意. 语调: 语调上扬活泼，疑问时尤为明显. 性格: 外向开朗，热情健谈.就算你自己不想治，你也得考虑考虑别人的感受吧。我们这些朋友的感受你不在乎无所谓，那你家人呢？你家人的感受你难道一点都不在乎吗！
以极度悲伤、带着明显哭腔的语气，用较小的音量缓缓诉说，语速缓慢，仿佛每一个字都承载着沉重的痛楚，声音颤抖而压抑，吐字虽轻却清晰可辨，透出深藏心底的哀伤与无助。
保持青年女性的声线特征，展现出一种清亮且略具紧迫感的音色，语速从平稳开始在叙述过程中逐渐加快，音量在情绪波动时增加，语调在句末调高以强调劝告的语气。
Single-speaker Cross-lingual Generalization十三 Vivan在语速偏快的情况下流畅自然地表达,音质清亮,音调略高,吐字清晰标准,给人一种开心愉悦的感觉。(Korean) 안녕하세요, 오늘은 어떤 용건입니까?
A deep, rich, and solid vocal register characteristic of a middle-aged woman, with full and powerful volume. Speech is delivered at a steady pace, articulation clear and precise, with fluent and confident intonation that rises slightly at the end of sentences.(Japanese) こんにちは、本日はどのようなご用件でしょうか？
语音应表现为直率且略显主观强势的中年女性,音色略带尖锐感,流畅表达中偶尔断句以凸显语气,情绪略带不满,音量随情感激动略有增强。(Chinese Dialect - Sichuan Dialect) 我早就该下班了，就是跟你说我这事情干不完，我现在走不脱。
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Timbre List

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Qwen3-TTS has open-sourced a total of 9 timbres in this release, covering various combinations of gender, age, language, and dialect to meet personalized speech generation needs in different scenarios.

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TimbresLanguagesTextSamples
苏瑶 SerenaChinese其实我真的有发现，我是一个特别善于观察别人情绪的人。
福伯 Uncle FuChinese其实我真的有发现，我是一个特别善于观察别人情绪的人。
十三 VivianChinese其实我真的有发现，我是一个特别善于观察别人情绪的人。
艾登 AidenEnglishThen by the end of the movie, when Dorothy clicks her heels and says, “There’s no place like home,” I got a little bit teary, I’ll admit. You know, I don’t even know why—I just, I just felt.
甜茶 RyanEnglishThen by the end of the movie, when Dorothy clicks her heels and says, “There’s no place like home,” I got a little bit teary, I’ll admit. You know, I don’t even know why—I just, I just felt.
小野杏 Ono AnnaJapaneseやばい、明日のプレゼン資料まだ完成してない… 助けて！
素熙 SoheeKorean야, 오늘 점심에 뭐 먹을지 생각해 봤어? 근처에 새로 생긴 분식집 어때?
晓东 DylanChinese Dialect - Beijing Dialect我们就在山上啊，就是其实也没什么，就是在土坡上跑来跑去，然后谁捡个那个嗯比较威风的棍儿，完了我们就就瞎打，呃要不就是什么掏个洞啊什么的。
程川 EricChinese Dialect - Sichuan Dialect你龟儿太过分了，把我的东西都搞坏了，还晓不晓得认错，硬是要把我整冒火你才安逸嗦，莫再烦老子爬球开。
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Qwen3-TTS-12Hz-1.7B-Base

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Voice Clone

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Control TypeReference SamplesTextSamples
Chinese Voice Clone你眼中的太阳，只是我指间的玩物。
祝您在马年里事业一马当先，业绩万马奔腾，在新的一年里快马加鞭，再创辉煌！
English Voice CloneIn the absence of confiscation, the Portuguese inquisitors were not earnest in tracing the heresies of ancestors or in following up the records of fugitives.
An ideal harmonious society. Humanism, is a lighthouse on this way to guide us in case we are getting lost.
Cross-lingual Voice Clone(Japanese) 要約すれば、全米国民のためにアメリカを再興するという使命を、我々は開始したのである。
(Korean) 광활한 우주 속에, 지구라고 불리는 아름다운 푸른 행성이 있습니다.
Text RobustnessQwen-TTS 是支持音色克隆、生成、控制的开源语音合成模型，不仅支持多语言multilingual，还支持各种复杂文本，如pin1 yin1，特殊符号等(◍•͈⌔•͈◍)；能读出各种生僻字詞。快来试试吧！
I am solving the equation: x = [-b ± √(b²-4ac)] / 2a? Nobody can — it's a disaster (◍•͈⌔•͈◍), very sad!
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Qwen-TTS-Tokenizer-12Hz

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Audio Reconstruction

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Reconstruction TypeOriginal SampleReconstructed Sample
Dialect Reconstruction
Singing Reconstruction
Paralanguage Reconstruction
Background Sound Reconstruction
\n", "date_published": "2026-01-21T16:00:04+00:00", "external_url": "https://qwen.ai/blog?id=qwen3tts-0115", "id": "urn:aggr:item:446ea3b02def8a71a97e8c5890f82489c80886b9", "image": "https://img.alicdn.com/imgextra/i4/O1CN01j0qyeo1QD1soQFCW3_!!6000000001941-2-tps-1590-954.png", "summary": "Qwen3-TTS is a series of powerful speech generation capabilities developed by Qwen, offering comprehensive support for voice clone, voice design, ultra-high-quality human-like speech generation, and natural language-based voice control. It…", "tags": [ "ai", "open-source" ], "title": "Qwen3-TTS Family is Now Open Sourced: Voice Design, Clone, and Generation!", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-01-21-qwen3-tts-family-is-now-open-sourced-voice-design-clone-and/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

In June 2025, we open-sourced the text-oriented Qwen3-Embedding and Qwen3-ReRanker model series, providing best-in-class performance across a variety of downstream tasks, including multilingual text retrieval, clustering, and classification, which have been widely adopted by developers in the community. Today, we are thrilled to announce the release of the Qwen3-VL-Embedding and Qwen3-VL-Reranker series, the newest models in the Qwen family. Built on the recently open-sourced and powerful Qwen3-VL foundation models, these models are specifically engineered for multimodal information retrieval and cross-modal understanding.

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Key Features

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\"Illustration

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Figure 1: Illustration of the Unified Multimodal Representation Space. Qwen3-VL-Embedding model series represent multi-source data (Text, Image, Visual Document, and Video) into a common manifold.

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Model Overview

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Table: Model specifications for the Qwen3-VL-Embedding and Qwen3-VL-Reranker

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ModelSizeLayersSequence LengthEmbedding DimensionQuantization SupportMRL SupportInstruction Aware
Qwen3-VL-Embedding-2B2B2832K2048YesYesYes
Qwen3-VL-Embedding-8B8B3632K4096YesYesYes
Qwen3-VL-Reranker-2B2B2832K---Yes
Qwen3-VL-Reranker-8B8B3632K---Yes
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Note: “Quantization Support” indicates the supported quantization formats for the embeddings. “MRL support” denotes whether the embedding model allows user-specified embedding dimension. “Instruction-aware” indicates whether the models support task-specific customization of the input instruction.

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Model Architecture

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Similar to the Qwen3-Embedding and Qwen3-ReRanker model series, Qwen3-VL-Embedding employs a dual-tower architecture while Qwen3-VL-Rerank utilizes a single-tower architecture. We have designed a multi-stage training paradigm to fully leverage and unleash the powerful general multimodal semantic understanding capabilities of Qwen3-VL, providing high-quality semantic representations and a precise re-ranking mechanism for complex, large-scale multimodal retrieval tasks.

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\"Illustration

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Figure 2: Overview of the Qwen3-VL-Embedding and Qwen3-VL-Reranker architecture. .

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The Embedding model receives single-modal or mixed-modal input and maps it into a high-dimensional semantic vector. Specifically, we extract the hidden state vector corresponding to the [EOS] token from the base model’s last layer to serve as the final semantic representation of the input. This method ensures efficient, independent encoding necessary for large-scale retrieval. The Reranking model receives an input pair (Query, Document) and performs joint encoding. It utilizes the Cross-Attention mechanism within the base model to achieve deeper, finer-grained inter-modal interaction and information fusion between the Query and the Document. The model ultimately expresses the relevance score of the input pair by predicting the generation probability of two special tokens (yes and no).

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Feature Comparison

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Qwen3-VL-EmbeddingQwen3-VL-Reranker
Core FunctionSemantic Representation, Embedding GenerationRelevance Scoring, Re-ranking
InputSingle modality or mixed modalities (Text, Image, Video, Screenshots)(Query, Document) pair, where Query and Document can be single- or mixed-modal inputs
MechanismIndependent Encoding, Efficient Retrieval (Dual-Tower)Deep Inter-Modal Interaction, Precise Alignment (Single-Tower/Cross-Attention)
ObjectiveSemantic Clustering within Vector SpaceOutput Relevance Score
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Evaluation Results

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Qwen3-VL-Embedding

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We primarily evaluated the Qwen3-VL-Embedding model on MMEB-v2 and MMTEB benchmarks. The Qwen3-VL-Embedding-8B model achieves state-of-the-art results on MMEB-V2, surpassing all previous open-source models and proprietary model services. When breaking down performance across different retrieval modalities, our model consistently achieves SOTA results on image, visual document, and video retrieval subtasks. Meanwhile, on the text-only multilingual MMTEB benchmark, the Qwen3-VL-Embedding model shows a performance gap compared to the text-only Qwen3-Embedding model of comparable size. However, when compared to other similarly sized models on the evaluation leaderboard, it still demonstrates highly competitive performance.

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\"Illustration

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Figure 3: Evaluation results on MMEB-v2 and MMTEB benchmarks .

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Qwen3-VL-ReRanker

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We utilize retrieval task datasets from various subtasks of MMEB-v2 and MMTEB retrieval benchmarks. For visual document retrieval, we employ JinaVDR and ViDoRe v3 datasets. Our results demonstrate that all Qwen3-VL-Reranker models consistently outperform the base embedding model and baseline rerankers, with the 8B variant achieving the best performance across most tasks.

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ModelSizeMMEB-v2(Retrieval) - AvgMMEB-v2(Retrieval) - ImageMMEB-v2(Retrieval) - VideoMMEB-v2(Retrieval) - VisDocMMTEB(Retrieval)JinaVDRViDoRe(v3)
Qwen3-VL-Embedding-2B2B73.474.853.679.268.171.052.9
jina-reranker-m02B-68.2-85.2-82.257.8
Qwen3-VL-Reranker-2B2B75.173.852.183.470.080.960.8
Qwen3-VL-Reranker-8B8B79.280.755.886.374.983.666.7
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Usage

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Embedding and reranking models are typically used together in retrieval systems. The embedding model performs initial recall to retrieve a substantial set of candidates, while the reranking model sorts these candidates based on newly computed relevance scores to present the most accurate results for the user query. This two-stage retrieval process leverages the complementary strengths of both models: the efficiency and scalability of the embedding model for broad candidate retrieval, and the precision and fine-grained scoring capability of the reranking model for final ranking. This approach achieves superior retrieval performance compared to using either model alone. Below are basic usage examples.

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from scripts.qwen3_vl_embedding import Qwen3VLEmbedder\nimport numpy as np\nimport torch\n\n# Define a list of query texts\nqueries = [\n    {"text": "A woman playing with her dog on a beach at sunset."},\n    {"text": "Pet owner training dog outdoors near water."},\n    {"text": "Woman surfing on waves during a sunny day."},\n    {"text": "City skyline view from a high-rise building at night."}\n]\n\n# Define a list of document texts and images\ndocuments = [\n    {"text": "A woman shares a joyful moment with her golden retriever on a sun-drenched beach at sunset, as the dog offers its paw in a heartwarming display of companionship and trust."},\n    {"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"},\n    {"text": "A woman shares a joyful moment with her golden retriever on a sun-drenched beach at sunset, as the dog offers its paw in a heartwarming display of companionship and trust.", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"}\n]\n\n# Specify the model path\nmodel_name_or_path = "Qwen/Qwen3-VL-Embedding-2B"\n\n# Initialize the Qwen3VLEmbedder model\nmodel = Qwen3VLEmbedder(model_name_or_path=model_name_or_path)\n# We recommend enabling flash_attention_2 for better acceleration and memory saving,\n# model = Qwen3VLEmbedder(model_name_or_path=model_name_or_path, dtype=torch.float16, attn_implementation="flash_attention_2")\n\n# Combine queries and documents into a single input list\ninputs = queries + documents\n\nembeddings = model.process(inputs)\n\n# Compute similarity scores between query embeddings and document embeddings\nsimilarity_scores = (embeddings[:4] @ embeddings[4:].T)\n\n# Print out the similarity scores in a list format\nprint(similarity_scores.tolist())\n\n# [[0.83203125, 0.74609375, 0.73046875], [0.5390625, 0.373046875, 0.48046875], [0.404296875, 0.326171875, 0.357421875], [0.1298828125, 0.06884765625, 0.10595703125]]\n
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from scripts.qwen3_vl_reranker import Qwen3VLReranker\nimport numpy as np\nimport torch\n\n# Specify the model path\nmodel_name_or_path = "Qwen/Qwen3-VL-Reranker-2B"\n\n# Initialize the Qwen3VLEmbedder model\nmodel = Qwen3VLReranker(model_name_or_path=model_name_or_path)\n# We recommend enabling flash_attention_2 for better acceleration and memory saving,\n# model = Qwen3VLReranker(model_name_or_path=model_name_or_path, dtype=torch.float16, attn_implementation="flash_attention_2")\n\n# Combine queries and documents into a single input list\n\ninputs = {\n    "instruction": "Retrieval relevant image or text with user's query",\n    "query": {"text": "A woman playing with her dog on a beach at sunset."},\n    "documents": [\n        {"text": "A woman shares a joyful moment with her golden retriever on a sun-drenched beach at sunset, as the dog offers its paw in a heartwarming display of companionship and trust."},\n        {"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"},\n        {"text": "A woman shares a joyful moment with her golden retriever on a sun-drenched beach at sunset, as the dog offers its paw in a heartwarming display of companionship and trust.", "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"}\n    ],\n    "fps": 1.0\n}\n        \nscores = model.process(inputs)\nprint(scores)\n# [0.8408790826797485, 0.6197134852409363, 0.7778129577636719]\n
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For more usage examples, please visit our GitHub repository.

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Future work

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The Qwen3-VL-Embedding and Qwen3-VL-Reranker model series represent our initial exploration into unified multimodal representation and retrieval. Compared to text-only embedding and reranking models, multimodal approaches, particularly unified multimodal embedding and reranking systems, still present vast opportunities for exploration in terms of model maturity, usability and application scenario expansion. The open-sourcing of Qwen3-VL-Embedding and Qwen3-VL-Reranker marks a new starting point. We look forward to collaborating with the community to explore and build more general-purpose unified multimodal retrieval capabilities.

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Citation

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If you find Qwen3-VL-Embedding and Qwen3-VL-Reranker helpful in your research, please consider citing us 📝 :)

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@article{qwen3vlembedding,\n  title={Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Ranking},\n  author={Li, Mingxin and Zhang, Yanzhao and Long, Dingkun and Chen, Keqin and Song, Sibo and Bai, Shuai and Yang, Zhibo and Xie, Pengjun and Yang, An and Liu, Dayiheng and Zhou, Jingren and Lin, Junyang},\n  journal={arXiv},\n  year={2026}\n}\n
\n", "date_published": "2026-01-07T20:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen3-vl-embedding", "id": "urn:aggr:item:b4e87a2e10ac58c2b4b4c42a45433f33ded85fe4", "image": "https://img.alicdn.com/imgextra/i4/O1CN017MeV6820B2KAWlQwA_!!6000000006810-2-tps-1590-954.png", "summary": "In June 2025, we open-sourced the text-oriented Qwen3-Embedding and Qwen3-ReRanker model series, providing best-in-class performance across a variety of downstream tasks, including multilingual text retrieval, clustering, and…", "tags": [ "ai", "open-source" ], "title": "Qwen3-VL-Embedding and Qwen3-VL-Reranker: For the Next Generation of Multimodal Retrieval", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-01-07-qwen3-vl-embedding-and-qwen3-vl-reranker-for-the-next/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

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QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DISCORD

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We are excited to introduce Qwen-Image-2512, the December update of Qwen-Image’s text-to-image foundational model. You are welcome to try the latest model at Qwen Chat. Compared to the base Qwen-Image model released in August, Qwen-Image-2512 features the following key improvements:

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Model Performance

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We conducted over 10,000 rounds of blind model evaluations on AI Arena, and the results show that Qwen-Image-2512 is currently the strongest open-source model—while remaining highly competitive even among closed-source models.

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Show Cases

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Enhanced Huamn Realism

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In Qwen-Image-2512, human depiction has been substantially refined. Compared to the August release, Qwen-Image-2512 adds significantly richer facial details and better environmental context. For example:

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A Chinese female college student, around 20 years old, with a very short haircut that conveys a gentle, artistic vibe. Her hair naturally falls to partially cover her cheeks, projecting a tomboyish yet charming demeanor. She has cool-toned fair skin and delicate features, with a slightly shy yet subtly confident expression—her mouth crooked in a playful, youthful smirk. She wears an off-shoulder top, revealing one shoulder, with a well-proportioned figure. The image is framed as a close-up selfie: she dominates the foreground, while the background clearly shows her dormitory—a neatly made bed with white linens on the top bunk, a tidy study desk with organized stationery, and wooden cabinets and drawers. The photo is captured on a smartphone under soft, even ambient lighting, with natural tones, high clarity, and a bright, lively atmosphere full of youthful, everyday energy.

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For the same prompt, Qwen-Image-2512 yields notably more lifelike facial features, and background objects—e.g., the desk, stationery, and bedding—are rendered with significantly greater clarity than in Qwen-Image.

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A 20-year-old East Asian girl with delicate, charming features and large, bright brown eyes—expressive and lively, with a cheerful or subtly smiling expression. Her naturally wavy long hair is either loose or tied in twin ponytails. She has fair skin and light makeup accentuating her youthful freshness. She wears a modern, cute dress or relaxed outfit in bright, soft colors—lightweight fabric, minimalist cut. She stands indoors at an anime convention, surrounded by banners, posters, or stalls. Lighting is typical indoor illumination—no staged lighting—and the image resembles a casual iPhone snapshot: unpretentious composition, yet brimming with vivid, fresh, youthful charm.

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Here, hair strands serve as a key differentiator: Qwen-Image’s August version tends to blur them together, losing fine detail, whereas Qwen-Image-2512 renders individual strands with precision, resulting in a more natural and realistic appearance.

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Another case:

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An East Asian teenage boy, aged 15–18, with soft, fluffy black short hair and refined facial contours. His large, warm brown eyes sparkle with energy. His fair skin and sunny, open smile convey an approachable, friendly demeanor—no makeup or blemishes. He wears a blue-and-white summer uniform shirt, slightly unbuttoned, made of thin breathable fabric, with black headphones hanging around his neck. His hands are in his pockets, body leaning slightly forward in a relaxed pose, as if engaged in conversation. Behind him lies a summer school playground: lush green grass and a red rubber track in the foreground, blurred school buildings in the distance, a clear blue sky with fluffy white clouds. The bright, airy lighting evokes a joyful, carefree adolescent atmosphere.

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In this example, Qwen-Image-2512 better adheres to semantic instructions—for instance, the prompt specifies “body leaning slightly forward,” and Qwen-Image-2512 accurately captures this posture, unlike its predecessor.

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An elderly Chinese couple in their 70s in a clean, organized home kitchen. The woman has a kind face and a warm smile, wearing a patterned apron; the man stands behind her, also smiling, as they both gaze at a steaming pot of buns on the stove. The kitchen is bright and tidy, exuding warmth and harmony. The scene is captured with a wide-angle lens to fully show the subjects and their surroundings.

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This comparison starkly highlights the gap between the August and December models. The original Qwen-Image struggles to accurately render aged facial features (e.g., wrinkles), resulting in an artificial “AI look.” In contrast, Qwen-Image-2512 precisely captures age cues, dramatically boosting realism.

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Finer Natural Detail

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Qwen-Image-2512’s enhanced detail rendering extends beyond humans—to landscapes, wildlife, and more. For instance:

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A turquoise river winds through a lush canyon. Thick moss and dense ferns blanket the rocky walls; multiple waterfalls cascade from above, enveloped in mist. At noon, sunlight filters through the dense canopy, dappling the river surface with shimmering light. The atmosphere is humid and fresh, pulsing with primal jungle vitality. No humans, text, or artificial traces present.

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Side-by-side, Qwen-Image-2512 exhibits superior fidelity in water flow, foliage, and waterfall mist—and renders richer gradation in greens. Another example (wave rendering):

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At dawn, a thin mist veils the sea. An ancient stone lighthouse stands at the cliff’s edge, its beacon faintly visible through the fog. Black rocks are pounded by waves, sending up bursts of white spray. The sky glows in soft blue-purple hues under cool, hazy light—evoking solitude and solemn grandeur.

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Fur detail is another highlight—here, a golden retriever portrait:

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An ultra-realistic close-up of a golden retriever outdoors under soft daylight. Hair is exquisitely detailed: strands distinct, color transitioning naturally from warm gold to light cream, light glinting delicately at the tips; a gentle breeze adds subtle volume. Undercoat is soft and dense; guard hairs are long and well-defined, with visible layering. Eyes are moist, expressive; nose is slightly damp with fine specular highlights. Background is softly blurred to emphasize the dog’s tangible texture and vivid expression.

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Similarly, texture quality improves in depictions of rugged wildlife—for example, a male argali sheep:

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A male argali stands atop a barren, rocky mountainside. Its coarse, dense grey-brown coat covers a powerful, muscular body. Most striking are its massive, thick, outward-spiraling horns—a symbol of wild strength. Its gaze is alert and sharp. The background reveals steep alpine terrain: jagged peaks, sparse low vegetation, and abundant sunlight—conveying the harsh yet majestic wilderness and the animal’s resilient vitality.

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Improved Text Rendering

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Qwen-Image-2512 further elevates text rendering—already a strength of the original—by improving accuracy, layout, and multimodal integration.

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For instance, this prompt requests a complete PPT slide illustrating Qwen-Image’s development roadmap (generation and editing tracks):

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这是一张现代风格的科技感幻灯片，整体采用深蓝色渐变背景。标题是“Qwen-Image发展历程”。下方一条水平延伸的发光时间轴，轴线中间写着“生图路线”。由左侧淡蓝色渐变为右侧深紫色，并以精致的箭头收尾。时间轴上每个节点通过虚线连接至下方醒目的蓝色圆角矩形日期标签，标签内为清晰白色字体，从左向右依次写着：“2025年5月6日 Qwen-Image 项目启动”“2025年8月4日 Qwen-Image 开源发布”“2025年12月31日 Qwen-Image-2512 开源发布” （周围光晕显著）在下方一条水平延伸的发光时间轴，轴线中间写着“编辑路线”。由左侧淡蓝色渐变为右侧深紫色，并以精致的箭头收尾。时间轴上每个节点通过虚线连接至下方醒目的蓝色圆角矩形日期标签，标签内为清晰白色字体，从左向右依次写着：“2025年8月18日 Qwen-Image-Edit 开源发布”“2025年9月22日 Qwen-Image-Edit-2509 开源发布”“2025年12月19日 Qwen-Image-Layered 开源发布”“2025年12月23日 Qwen-Image-Edit-2511 开源发布”

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We can even generate a before-and-after comparison slide to highlight the leap from “AI-blurry” to “photorealistic”:

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这是一张现代风格的科技感幻灯片，整体采用深蓝色渐变背景。顶部中央为白色无衬线粗体大字标题“Qwen-Image-2512重磅发布”。画面主体为横向对比图，视觉焦点集中于中间的升级对比区域。左侧为面部光滑没有任何细节的女性人像，质感差；右侧为高度写实的年轻女性肖像，皮肤呈现真实毛孔纹理与细微光影变化，发丝根根分明，眼眸透亮，表情自然，整体质感接近写实摄影。两图像之间以一个绿色流线型箭头链接。造型科技感十足，中部标注“2512质感升级”，使用白色加粗字体，居中显示。箭头两侧有微弱光晕效果，增强动态感。在图像下方，以白色文字呈现三行说明：“● 更真实的人物质感。大幅度降低了生成图片的AI感，提升了图像真实性 ● 更细腻的自然纹理。大幅度提升了生成图片的纹理细节。风景图，动物毛发刻画更细腻。● 更复杂的文字渲染。大幅提升了文字渲染的质量。图文混合渲染更准确，排版更好”

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A more complex infographic example:

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这是一幅专业级工业技术信息图表，整体采用深蓝色科技感背景，光线均匀柔和，营造出冷静、精准的现代工业氛围。画面分为左右两大板块，布局清晰，视觉层次分明。左侧板块标题为“实际发生的现象”，以浅蓝色圆角矩形框突出显示，内部排列三个深蓝色按钮式条目，第一个条目展示一堆棕色粉末状原料上滴落水滴的图标，文字为“团聚/结块”，后面配有绿色对钩；第二个条目为一个装有蓝色液体并冒出气泡的锥形瓶，文字为“产生气泡/缺陷”，后面配有绿色对钩；第三个条目为两个生锈的齿轮，文字为“设备腐蚀/催化剂失活”，后面配有绿色对钩。右侧板块标题为“【不会】发生的现象”，使用米黄色圆角矩形框呈现，内部四个条目均置于深灰色背景方框中。图标分别为：一组精密啮合的金属齿轮，文字为“反应效率【显著提高】”，上方覆盖醒目的红色叉号；一捆整齐排列的金属管材，文字为“成品内部【绝对无气泡/孔隙】”，上方覆盖醒目的红色叉号；一条坚固的金属链条正在承受拉力，文字为“材料强度与耐久性【得到增强】”，上方覆盖醒目的红色叉号；一堆腐蚀的扳手，文字为“加工过程【零腐蚀/零副反应风险】”，上方覆盖醒目的红色叉号。底部中央有一行小字注释：“注：水分的存在通常会导致负面或干扰性的结果，而非理想或增强的状态”，字体为白色，清晰可读。整体风格现代简约，配色对比强烈，图形符号准确传达技术逻辑，适合用于工业培训或科普演示场景。

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Or even a full educational poster:

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这是一幅由十二个分格组成的3×4网格布局的写实摄影作品，整体呈现“健康的一天”主题，画面风格简洁清晰，每一分格独立成景又统一于生活节奏的叙事脉络。第一行分别是“06:00 晨跑唤醒身体”：面部特写，一位女性身穿灰色运动套装，背景是初升的朝阳与葱郁绿树；“06:30 动态拉伸激活关节”：女性身着瑜伽服在阳台做晨间拉伸，身体舒展，背景为淡粉色天空与远山轮廓；“07:30 均衡营养早餐”：桌上摆放全麦面包、牛油果和一杯橙汁，女性微笑着准备用餐；“08:00 补水润燥”：透明玻璃水杯中浮有柠檬片，女性手持水杯轻啜，阳光从左侧斜照入室，杯壁水珠滑落；第二行分别是：“09:00 专注高效工作”：女性专注敲击键盘，屏幕显示简洁界面，身旁放有一杯咖啡与一盆绿植；“12:00 静心阅读时光”：女性坐在书桌前翻阅纸质书籍，台灯散发暖光，书页泛黄，旁放半杯红茶；“12:30 午后轻松漫步”：女性在林荫道上漫步，脸部特写；“15:00 茶香伴午后”：女性端着骨瓷茶杯站在窗边，窗外是城市街景与飘动云朵，茶香袅袅；第三行分别是：“18:00 运动释放压力”：健身房内，女性正在练习瑜伽；“19:00 美味晚餐”：女性在开放式厨房中切菜，砧板上有番茄与青椒，锅中热气升腾，灯光温暖；“21:00 冥想助眠”：女性盘腿坐在柔软地毯上冥想，双手轻放膝上，闭目宁静；“21:30 进入睡眠”：女性躺在床上休息。整体采用自然光线为主，色调以暖白与米灰为基调，光影层次分明，画面充满温馨的生活气息与规律的节奏感。

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These are the core enhancements in this update. We hope you enjoy using Qwen-Image-2512!

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Citation

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If Qwen-Image-2512 proves helpful in your research, we’d greatly appreciate your citation 📝 :)

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@misc{wu2025qwenimagetechnicalreport,\n      title={Qwen-Image Technical Report}, \n      author={Chenfei Wu and Jiahao Li and Jingren Zhou and Junyang Lin and Kaiyuan Gao and Kun Yan and Sheng-ming Yin and Shuai Bai and Xiao Xu and Yilei Chen and Yuxiang Chen and Zecheng Tang and Zekai Zhang and Zhengyi Wang and An Yang and Bowen Yu and Chen Cheng and Dayiheng Liu and Deqing Li and Hang Zhang and Hao Meng and Hu Wei and Jingyuan Ni and Kai Chen and Kuan Cao and Liang Peng and Lin Qu and Minggang Wu and Peng Wang and Shuting Yu and Tingkun Wen and Wensen Feng and Xiaoxiao Xu and Yi Wang and Yichang Zhang and Yongqiang Zhu and Yujia Wu and Yuxuan Cai and Zenan Liu},\n      year={2025},\n      eprint={2508.02324},\n      archivePrefix={arXiv},\n      primaryClass={cs.CV},\n      url={https://arxiv.org/abs/2508.02324}, \n}\n
\n", "date_published": "2025-12-31T05:08:30+00:00", "external_url": "https://qwen.ai/blog?id=qwen-image-2512", "id": "urn:aggr:item:f581c4e22cc3cb9eeec28a2fa755bddc4aa81685", "image": "https://img.alicdn.com/imgextra/i2/O1CN01dEKYWk22o5K1j8Hku_!!6000000007166-2-tps-1590-954.png", "summary": "QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DISCORD We are excited to introduce Qwen-Image-2512, the December update of Qwen-Image’s text-to-image foundational model. You are welcome to try the latest model at Qwen Chat. Compared to the base…", "tags": [ "ai", "open-source" ], "title": "Qwen-Image-2512: Finer Details, Greater Realism", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2025-12-31-qwen-image-2512-finer-details-greater-realism/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

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QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DISCORD

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We are excited to introduce Qwen-Image-Edit-2511, an enhanced version over Qwen-Image-Edit-2509, featuring multiple improvements—including notably better consistency. To try out the latest model, please visit Qwen Chat and select the Image Editing feature. Note that the online version includes certain optimizations for speed; for the best possible performance, we recommend deploying the model locally via ModelScope.

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Key enhancements in Qwen-Image-Edit-2511 include: mitigate image drift, improved character consistency，integrated LoRA capabilities， enhanced industrial design generation, and strengthened geometric reasoning ability.

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Showcase Examples

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Qwen-Image-Edit-2511 Enhances Character Consistency In Qwen-Image-Edit-2511, character consistency has been significantly improved. The model can perform imaginative edits based on an input portrait while preserving the identity and visual characteristics of the subject.

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Improved Multi-Person Consistency While Qwen-Image-Edit-2509 already improved consistency for single-subject editing, Qwen-Image-Edit-2511 further enhances consistency in multi-person group photos—enabling high-fidelity fusion of two separate person images into a coherent group shot:

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Built-in Support for Community-Created LoRAs Since Qwen-Image-Edit’s release, the community has developed many creative and high-quality LoRAs—greatly expanding its expressive potential. Qwen-Image-Edit-2511 integrates selected popular LoRAs directly into the base model, unlocking their effects without extra tuning.

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For example, Lighting Enhancement LoRA Realistic lighting control is now achievable out-of-the-box:

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Another example, generating new viewpoints can now be done directly with the base model:

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Industrial Design Applications

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We’ve paid special attention to practical engineering scenarios—for instance, batch industrial product design:

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…and material replacement for industrial components:

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Enhanced Geometric Reasoning Qwen-Image-Edit-2511 introduces stronger geometric reasoning capability—e.g., directly generating auxiliary construction lines for design or annotation purposes:

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That wraps up the major updates in Qwen-Image-Edit-2511. Enjoy exploring the new capabilities! 🎉

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Citation

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If you find our model useful in your research, please consider citing us 📝 :)

\n
@misc{wu2025qwenimagetechnicalreport,\n      title={Qwen-Image Technical Report}, \n      author={Chenfei Wu and Jiahao Li and Jingren Zhou and Junyang Lin and Kaiyuan Gao and Kun Yan and Sheng-ming Yin and Shuai Bai and Xiao Xu and Yilei Chen and Yuxiang Chen and Zecheng Tang and Zekai Zhang and Zhengyi Wang and An Yang and Bowen Yu and Chen Cheng and Dayiheng Liu and Deqing Li and Hang Zhang and Hao Meng and Hu Wei and Jingyuan Ni and Kai Chen and Kuan Cao and Liang Peng and Lin Qu and Minggang Wu and Peng Wang and Shuting Yu and Tingkun Wen and Wensen Feng and Xiaoxiao Xu and Yi Wang and Yichang Zhang and Yongqiang Zhu and Yujia Wu and Yuxuan Cai and Zenan Liu},\n      year={2025},\n      eprint={2508.02324},\n      archivePrefix={arXiv},\n      primaryClass={cs.CV},\n      url={https://arxiv.org/abs/2508.02324}, \n}\n
\n", "date_published": "2025-12-23T05:08:30+00:00", "external_url": "https://qwen.ai/blog?id=qwen-image-edit-2511", "id": "urn:aggr:item:84d08e208d26a95ed3456045137b672010dd013f", "image": "https://img.alicdn.com/imgextra/i1/O1CN015JyH0e1jXcNfdFALl_!!6000000004558-0-tps-1590-954.jpg", "summary": "QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DISCORD We are excited to introduce Qwen-Image-Edit-2511, an enhanced version over Qwen-Image-Edit-2509, featuring multiple improvements—including notably better consistency. To try out the latest…", "tags": [ "ai", "open-source" ], "title": "Qwen-Image-Edit-2511: Improve Consistency", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2025-12-23-qwen-image-edit-2511-improve-consistency/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

Qwen3-TTS-VD-Flash HF DEMO Qwen3-TTS-VD-Flash MODELSCOPE DEMO Qwen3-TTS-VC-Flash HF DEMO Qwen3-TTS-VC-Flash MODELSCOPE DEMO

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Qwen3-TTS family has launched two new models: the voice design model Qwen3-TTS-VD-Flash (accessible via the Qwen API) and the voice cloning model Qwen3-TTS-VC-Flash (accessible via the Qwen API).

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Key Features:

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Qwen3-TTS-VD-Flash

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Qwen3-TTS supports creating customized voice profiles directly from natural language descriptions. Users can freely describe acoustic attributes, persona settings, background information, and more, making it easy to create the exact kind of voice they want.

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Metrics

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Controllable generation: On the InstructTTS-Eval benchmark, Qwen3-TTS significantly outperforms GPT-4o-mini-tts and Mimo-audio-7b-instruct overall, and surpasses Gemini-2.5-pro-preview-tts on role‑playing tests.

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Samples

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Control TypeControl InstructionTextSamples
Acoustic attribute: positive/negative模仿电视购物主持人，中年男性，声音洪亮有激情，语速极快，音调夸张上扬，用极具煽动性的语气来介绍产品，营造出紧迫感和抢购氛围。不要九百九十八，也不要八百八十八，今天只要九十八！对，你没有听错，只要九十八！赶快拿起电话订购吧！
Male, middle-aged, booming baritone - hyper-energetic infomercial voice with rapid-fire delivery and exaggerated pitch rises, dripping with salesmanshipNot nine hundred ninety-nine dollars! Not seven hundred ninety-nine! today, it's just $98! That's right, you heard me, ONLY NINETY-EIGHT DOLLARS! Don't wait, don't hesitate—pick up the phone and call NOW!
展现出悲苦沙哑的声音质感,语速偏慢,情绪浓烈且带有哭腔,以标准普通话缓慢诉说,情感强烈,语调哀怨高亢,音高起伏大。这些年代受的苦，就跟你说上十天半个月也说不完。
Male, 30s, strained tenor - breathy sobs interrupt speech, pitch swings wildly between whispers and wailsThe suffering I've endured... I could talk for days, weeks even, and still not scratch the surface.
Persona role-play: concise/rich邪恶女魔头哥哥，你回来啦，人家等了你好久好久了，要抱抱！
Playful Homebody SisBig brooo, you're finally home! I've been waiting forever! Gimme a hug, pwease~!
角色姓名：陈远山 身份背景：某国家重点科研项目首席顾问，年近七十的资深战略科学家。曾参与国家重大科技攻关工程，历经数十年风雨，见证了从落后追赶到自主创新的艰难历程。现任国家科技咨询委员会终身荣誉委员，仍坚持在一线培养青年人才，为国家战略发展建言献策。 外貌特征：身形挺拔，两鬓斑白，眉宇间刻着岁月沉淀的坚毅。常着深色中山装或简洁正装，眼神沉静而锐利，举手投足间自带威严与从容。 性格特质：意志如钢，信念坚定，面对挑战从不退缩；胸怀家国，心系民族未来，将个人命运与国家兴衰紧密相连；严谨自律，言出必行，话语中充满责任感与历史担当；外冷内热，表面严肃，实则对后辈寄予厚望，甘为人梯。 人生信条：“我们这一代人，不是为了站在光里，而是为了把路铺到光里。”有些事，只要国家需要，就得有人扛起来。 我们那一代人，是背着泥土铺路的； 你们要做的，是让这条路，通向星辰大海。
role: Mid-level Corporate Project Manager. gender: Male. pitch: Dynamic male pitch, starting mid-high with agitation, transitioning to a lower declarative range, and spiking upwards with intense emphasis such as 'so finished!'. speed: Variable speaking rate; initially rapid during agitated states ('Damn it!'), slowing for declarative statements like 'I'm done', then accelerating again with strong emotional delivery ('so finished!').. volume: Significant dynamic range; initially loud and forceful, briefly softening to a firm conversational level, then escalating to shouting at points of high emphasis like 'so finished!'. age: Middle-aged adult. clarity: Consistently clear articulation, maintained even during rapid or loud emotional expressions.. fluency: Fluent and coherent speech, with pauses and pacing that align with the expressed emotional state.. accent: General American English. texture: Predominantly forceful, becoming strained during agitated outbursts and shouting, otherwise resonant and firm during calmer declarations.. emotion: Starts with pronounced frustration and exasperation ('Damn it!'), shifts to resolute decisiveness ('I'm done'), culminating in an intensely emphatic declaration of finality ('I am so finished!').. tone: Begins as agitated and questioning, transitions to assertive and declarative, and concludes with a highly emphatic and intense quality.. personality: Assertive and emotionally expressive, demonstrating a build-up of frustration leading to a decisive, forceful resolution..So am I damn it. I mean, come on. It's just, you know what I'm through with this. I'm done. Finished, I'm out of here. I am so finished. Those were coming when the health.
Background information《少年闰土》是节选自鲁迅1921年写的短篇小说《故乡》中的一段插叙， 主人公以鲁迅的童年伙伴章运水为原型。 《少年闰土》的题目是被选入小学语文教材后编者加的，自1980年起，它基本上一直保留在小学语文教材中， 目前被选入小学语文教材统编版六年级上册。 《少年闰土》以回忆的方式展开， 刻画出一个机敏勇敢、见多识广的闰土形象。 全文按照“记忆—相识—相处—相别”的顺序书写， 依次介绍了“我”记忆中看瓜刺猹的闰土、初次相识时的闰土、给“我”讲新鲜事的闰土， 少年闰土给“我”的童年生活带来了无穷的新奇与乐趣。 作者善用白描手法，多用直接引语， 其中又蕴含丰富的情感，既有“我”在相见之前对闰土的盼望，也有相处过程中对闰土的羡慕和向往，还有分别时的依依不舍。在短短的相处中，“我”和闰土彼此之间结下深厚情谊。深蓝的天空中挂着一轮金黄的圆月，下面是海边的沙地，种着一望无际的碧绿西瓜。其间十一二岁的少年闰土，项带银圈，手捏钢叉，向一匹猹刺去。那猹却将身一扭，反从他胯下逃走——这幅月夜刺猹的画面，成了“我”三十年来难忘的剪影。那年，因家中轮到三十余年一遇的大祭祀值年，祭器贵重需人看管，父亲便允了忙月的请求，唤其子闰土进城相助。
"Do not go gentle into that good night" is a poem in the form of a villanelle by Welsh poet Dylan Thomas (1914–1953), and is one of his best-known works. Though first published in the journal Botteghe Oscure in 1951, Thomas wrote the poem in 1947 while visiting Florence with his family. The poem was subsequently included, alongside other works by Thomas, in In Country Sleep, and Other Poems (New Directions, 1952) and Collected Poems, 1934–1952 (Dent, 1952). The poem entered the public domain in all countries outside the United States on 1 January 2024. It has been suggested that the poem was written for Thomas's dying father, although he did not die until just before Christmas in 1952. It has no title other than its first line, "Do not go gentle into that good night", a line that appears as a refrain throughout the poem along with its other refrain, "Rage, rage against the dying of the light".Do not go gentle into that good night Old age should burn and rave at close of day Rage, rage against the dying of the light Though wise men at their end know dark is right Because their words had forked no lightning They do not go gentle into that good night
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Users can also persistently store and repeatedly invoke the voices created by Qwen3-TTS, enabling the generation of vivid and natural multi-turn, multi-role long-form dialogues.

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Control TypeControl InstructionTextSamples
Voice reuse"旁白": "声音特征沉稳、客观、略带叙事感的女播音腔，普通话标准，语速适中，带有轻微的环境氛围渲染，语调平缓但富有感染力，在关键情节时稍作停顿，增强画面感。情感冷静旁观，偶尔带一丝微妙的反讽" "小林": "25岁男性上班族，声音清亮但时常犹豫，语速时快时慢，紧张时会轻微结巴。情绪波动明显，从低声呢喃到突然激动再到自我怀疑的叹气。肢体语言丰富，经常无意识的小动作" "御姐": "模拟成熟性感的御姐音色，声音略带磁性且沉稳，语速不快不慢，语调充满自信和一丝挑逗，尾音可以稍微拖长并上扬，给人一种游刃有余的掌控感。"旁白: 小林今天第三次走神了。酒吧昏黄的灯光晃得他心跳加速，而吧台对面那个红唇微扬的女人，正用指尖轻轻摩挲着酒杯边缘。 御姐: 小弟弟，有兴趣陪姐姐喝一杯吗？ 小林: 啊？我、我……我其实不太会喝酒…… 旁白: 他的手指无意识地抠着杯沿，喉结上下滚动，像被什么无形的东西掐住了呼吸。 御姐: 不会喝？那正好——姐姐教你。这杯莫吉托，甜得刚好，就像你刚才偷看我的眼神。 小林: 我、我没偷看！……好吧，看了一眼。就一眼！ 旁白: 他猛地坐直，又立刻缩回肩膀，仿佛那句话烫伤了自己的嘴。 御姐: 紧张什么？你连坐姿都在发抖……要不要靠过来一点？这里太吵了。 小林: 靠过去？可、可我们才第一次见面……你都不认识我…… 御姐: 名字不重要，感觉才重要。......而我感觉……你有点可爱。 旁白: 小林的耳朵瞬间红透，连耳后那颗小痣都像在发烫。他想逃，脚却像钉在了高脚凳上。 小林: 可爱？没人这么说过我……他们都说我太闷，连朋友圈都发不出手…… 御姐: 那现在呢？敢不敢发一条——'今晚，和一个危险又迷人的姐姐喝了一杯'？ 小林: ……我连配图都不敢选。你笑起来太……太有杀伤力了。 御姐: 那就别发了。有些故事，只适合藏在两个人的记忆里——比如，接下来你打算请我跳支舞吗？ 旁白: 他张了张嘴，没发出声音。但这一次，他没有低头，而是轻轻推开了那杯没动过的苏打水，朝她伸出了手。
"Lucas": "Male, 17 years old, tenor range, gaining confidence - deeper breath support now, though vowels still tighten when nervous" "Mia": "Female, 16 years old, mezzo-soprano range, softening - lowering register to intimate speaking voice, consonants softening"Lucas:H-hey! You dropped your... uh... calculus notebook? I mean, I think it's yours? Maybe? Mia:Oh wow, my mortal enemy - Mr. Thompson's problem sets. Thanks for rescuing me from that F. Lucas:No problem! I actually... kinda finished those already? If you want to compare answers or something... Mia:Is this your sneaky way of saying you want to study together, Lucas? Because I saw you staring during lab partners sign-up. Lucas:What? No! I mean yes but not like... I just think you're... your titration technique is really precise! Mia:That's the nerdiest compliment I've ever gotten. Tell you what - help me survive pre-calc and I'll teach you how to actually flirt. Lucas:Wow, harsh. And here I thought my titration line was smooth. Mia:It was adorable. Like when you tripped over your shoelaces in the hall yesterday. Or that time you— Lucas:Okay okay! I get it, I'm a disaster. So... library after school? I'll bring the graphing calculators? Mia:Only if you promise not to spill coffee on my notes again... though I guess watching you panic-clean was pretty cute.
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How to use

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Using Qwen3-TTS-VD-Flash via the Qwen API is very simple. Below is a short code snippet to try it out:

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import requests\nimport base64\nimport os\n\ndef create_voice_and_play():\n    # API keys differ between Singapore and Beijing regions. Get your API key: https://www.alibabacloud.com/help/zh/model-studio/get-api-key\n    # If you haven't set an environment variable, replace the line below with: api_key = "sk-xxx"\n    api_key = os.getenv("DASHSCOPE_API_KEY")\n    \n    if not api_key:\n        print("Error: DASHSCOPE_API_KEY environment variable not found. Please set your API key.")\n        return None, None, None\n    \n    # Prepare request data\n    headers = {\n        "Authorization": f"Bearer {api_key}",\n        "Content-Type": "application/json"\n    }\n    \n    data = {\n        "model": "qwen-voice-design",\n        "input": {\n            "action": "create",\n            "target_model": "qwen3-tts-vd-realtime-2025-12-16",\n            "voice_prompt": "A composed middle-aged male announcer with a deep, rich and magnetic voice, a steady speaking speed and clear articulation, is suitable for news broadcasting or documentary commentary.",\n            "preview_text": "Dear listeners, hello everyone. Welcome to the evening news.",\n            "preferred_name": "announcer",\n            "language": "en"\n        },\n        "parameters": {\n            "sample_rate": 24000,\n            "response_format": "wav"\n        }\n    }\n    \n    # URL for Singapore region. For Beijing region, use: https://dashscope.aliyuncs.com/api/v1/services/audio/tts/customization\n    url = "https://dashscope-intl.aliyuncs.com/api/v1/services/audio/tts/customization"\n    \n    try:\n        # Send request\n        response = requests.post(\n            url,\n            headers=headers,\n            json=data,\n            timeout=60  # Add timeout setting\n        )\n        \n        if response.status_code == 200:\n            result = response.json()\n            \n            # Get voice name\n            voice_name = result["output"]["voice"]\n            print(f"Voice name: {voice_name}")\n            \n            # Get preview audio data\n            base64_audio = result["output"]["preview_audio"]["data"]\n            \n            # Decode Base64 audio data\n            audio_bytes = base64.b64decode(base64_audio)\n            \n            # Save audio file locally\n            filename = f"{voice_name}_preview.wav"\n            \n            # Write audio data to local file\n            with open(filename, 'wb') as f:\n                f.write(audio_bytes)\n            \n            print(f"Audio saved to local file: {filename}")\n            print(f"File path: {os.path.abspath(filename)}")\n            \n            return voice_name, audio_bytes, filename\n        else:\n            print(f"Request failed. Status code: {response.status_code}")\n            print(f"Response: {response.text}")\n            return None, None, None\n            \n    except requests.exceptions.RequestException as e:\n        print(f"Network request error: {e}")\n        return None, None, None\n    except KeyError as e:\n        print(f"Response format error: missing required field: {e}")\n        print(f"Response: {response.text if 'response' in locals() else 'No response'}")\n        return None, None, None\n    except Exception as e:\n        print(f"Unexpected error: {e}")\n        return None, None, None\n\nif __name__ == "__main__":\n    print("Creating voice...")\n    voice_name, audio_data, saved_filename = create_voice_and_play()\n    \n    if voice_name:\n        print(f"\\nSuccessfully created voice '{voice_name}'")\n        print(f"Audio file saved: '{saved_filename}'")\n        print(f"File size: {os.path.getsize(saved_filename)} bytes")\n    else:\n        print("\\nVoice creation failed")\n
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Qwen3-TTS-VC-Flash

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Qwen3-TTS supports natural, 3‑second–level voice cloning, and can generate multilingual audio based on the cloned voice. It is also highly robust when handling complex text and in-the-wild audio.

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Metrics

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Multilingual voice cloning: On the MiniMax TTS Multilingual Test Set, Qwen3‑TTS shows more stable content than MiniMax, ElevenLabs, and GPT‑4o‑Audio‑Preview for Chinese, English, French, Italian, and other languages, achieving the best average word error rate (WER).

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Samples

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Cloning TypeReference AudioTextSamples
Chinese–English cloning昨夜雨疏风骤，浓睡不消残酒。试问卷帘人，却道海棠依旧。知否，知否？应是绿肥红瘦。
Overcome with guilt, Martin hung his head and muttered, "I’m so sorry. I never meant to hurt you like this. Can you ever forgive me?" It was obvious what the answer would be.
再说，学好文化搞通思想，道理还不是为了劳动？难道我劳动比谁差！
Innovation blossoms when we cast aside the paralyzing fear of failure and wholeheartedly embrace the gloriously messy, unexpectedly beautiful journey of creation.
Multilingual cloning上周我去日本旅游，看到一个法国人在买东西，那个日本人问：なんか買うものありますか？那个法国人说：Je voudrais acheter un t-shirt à manches courtes.'
呐，跟你说个秘密哦！The tea eggs sold by the old lady on the mountaintop are something else, I tell you! Es wird durch Einkochen mit Quellwasser und drei verschiedenen Wildkräutern zubereitet.
Robustness on complex textQwen-TTS 是支持音色克隆、生成、控制的语音合成模型，不仅支持多语言multilingual，还支持各种复杂文本，如pin1 yin1，特殊符号等·〛』］；能读出各种生僻字词。快来试试吧！
Is there anyone who can solve the equation: x = [-b ± √(b²-4ac)] / 2a? Nobody can — it's a disaster, very sad! If you know this formula, please email solution@prize.org.
Robustness on in-the-wild audio妲己凭借着自己的妖娆妩媚，在商纣王的宫廷中弄权，她的行为可谓是牝鸡司晨，加速了商朝的灭亡
We began our discussion on the four development phases of romantic relationships by reading a quote from 'The General Theory of Love.'
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Want to hear how animals would sound if they could talk? Qwen3-TTS can show you cross-species cloning:

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Cloning TypeReference AudioTextSamples
Cross-species cloning这点小事都能办砸？说好晚上七点准时开饭，本汪的肚子都咕咕叫了！
That was one small leap for me, but a giant leap for goatkind!
早起的鸟儿有虫吃，早起的虫儿被我吃！
Oink... not now. My mud nap is at peak fluffiness. Disturb me and I’ll snore louder.
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How to use

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Using Qwen3-TTS-VC-Flash via the Qwen API is very simple. Below is a short code snippet to try it out:

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# The DashScope SDK version must be 1.23.9 or later, and the Python version must be 3.10 or later.\n# coding=utf-8\n# Installation instructions for pyaudio:\n# APPLE Mac OS X\n#   brew install portaudio\n#   pip install pyaudio\n# Debian/Ubuntu\n#   sudo apt-get install python-pyaudio python3-pyaudio\n#   or\n#   pip install pyaudio\n# CentOS\n#   sudo yum install -y portaudio portaudio-devel && pip install pyaudio\n# Microsoft Windows\n#   python -m pip install pyaudio\n\nimport pyaudio\nimport os\nimport requests\nimport base64\nimport pathlib\nimport threading\nimport time\nimport dashscope  # The DashScope Python SDK version must be 1.23.9 or later.\nfrom dashscope.audio.qwen_tts_realtime import QwenTtsRealtime, QwenTtsRealtimeCallback, AudioFormat\n\n# ======= Constant configuration =======\nDEFAULT_TARGET_MODEL = "qwen3-tts-vc-realtime-2025-11-27"  # The same model must be used for voice cloning and speech synthesis.\nDEFAULT_PREFERRED_NAME = "guanyu"\nDEFAULT_AUDIO_MIME_TYPE = "audio/mpeg"\nVOICE_FILE_PATH = "voice.mp3"  # The relative path of the local audio file for voice cloning.\n\nTEXT_TO_SYNTHESIZE = [\n    'Right? I really like this kind of supermarket,',\n    'especially during the New Year.',\n    'Going to the supermarket',\n    'just makes me feel',\n    'super, super happy!',\n    'I want to buy so many things!'\n]\n\ndef create_voice(file_path: str,\n                 target_model: str = DEFAULT_TARGET_MODEL,\n                 preferred_name: str = DEFAULT_PREFERRED_NAME,\n                 audio_mime_type: str = DEFAULT_AUDIO_MIME_TYPE) -> str:\n    """\n    Create a voice and return the voice parameter.\n    """\n    # The API keys for the Singapore and Beijing regions are different. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key.\n    # If you have not configured the environment variable, replace the following line with your Model Studio API key: api_key = "sk-xxx"\n    api_key = os.getenv("DASHSCOPE_API_KEY")\n\n    file_path_obj = pathlib.Path(file_path)\n    if not file_path_obj.exists():\n        raise FileNotFoundError(f"The audio file does not exist: {file_path}")\n\n    base64_str = base64.b64encode(file_path_obj.read_bytes()).decode()\n    data_uri = f"data:{audio_mime_type};base64,{base64_str}"\n\n    # The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: https://dashscope.aliyuncs.com/api/v1/services/audio/tts/customization\n    url = "https://dashscope-intl.aliyuncs.com/api/v1/services/audio/tts/customization"\n    payload = {\n        "model": "qwen-voice-enrollment", # Do not modify this value.\n        "input": {\n            "action": "create",\n            "target_model": target_model,\n            "preferred_name": preferred_name,\n            "audio": {"data": data_uri}\n        }\n    }\n    headers = {\n        "Authorization": f"Bearer {api_key}",\n        "Content-Type": "application/json"\n    }\n\n    resp = requests.post(url, json=payload, headers=headers)\n    if resp.status_code != 200:\n        raise RuntimeError(f"Failed to create the voice: {resp.status_code}, {resp.text}")\n\n    try:\n        return resp.json()["output"]["voice"]\n    except (KeyError, ValueError) as e:\n        raise RuntimeError(f"Failed to parse the voice response: {e}")\n\ndef init_dashscope_api_key():\n    """\n    Initialize the API key for the DashScope SDK.\n    """\n    # The API keys for the Singapore and Beijing regions are different. To get an API key, see https://www.alibabacloud.com/help/en/model-studio/get-api-key.\n    # If you have not configured the environment variable, replace the following line with your Model Studio API key: dashscope.api_key = "sk-xxx"\n    dashscope.api_key = os.getenv("DASHSCOPE_API_KEY")\n\n# ======= Callback class =======\nclass MyCallback(QwenTtsRealtimeCallback):\n    """\n    Custom TTS streaming callback.\n    """\n    def __init__(self):\n        self.complete_event = threading.Event()\n        self._player = pyaudio.PyAudio()\n        self._stream = self._player.open(\n            format=pyaudio.paInt16, channels=1, rate=24000, output=True\n        )\n\n    def on_open(self) -> None:\n        print('[TTS] Connection established')\n\n    def on_close(self, close_status_code, close_msg) -> None:\n        self._stream.stop_stream()\n        self._stream.close()\n        self._player.terminate()\n        print(f'[TTS] Connection closed code={close_status_code}, msg={close_msg}')\n\n    def on_event(self, response: dict) -> None:\n        try:\n            event_type = response.get('type', '')\n            if event_type == 'session.created':\n                print(f'[TTS] Session started: {response["session"]["id"]}')\n            elif event_type == 'response.audio.delta':\n                audio_data = base64.b64decode(response['delta'])\n                self._stream.write(audio_data)\n            elif event_type == 'response.done':\n                print(f'[TTS] Response complete, Response ID: {qwen_tts_realtime.get_last_response_id()}')\n            elif event_type == 'session.finished':\n                print('[TTS] Session finished')\n                self.complete_event.set()\n        except Exception as e:\n            print(f'[Error] Exception occurred while processing callback event: {e}')\n\n    def wait_for_finished(self):\n        self.complete_event.wait()\n\n# ======= Main execution logic =======\nif __name__ == '__main__':\n    init_dashscope_api_key()\n    print('[System] Initializing Qwen TTS Realtime ...')\n\n    callback = MyCallback()\n    qwen_tts_realtime = QwenTtsRealtime(\n        model=DEFAULT_TARGET_MODEL,\n        callback=callback,\n        # The following is the URL for the Singapore region. If you use a model in the Beijing region, replace the URL with: wss://dashscope.aliyuncs.com/api-ws/v1/realtime\n        url='wss://dashscope-intl.aliyuncs.com/api-ws/v1/realtime'\n    )\n    qwen_tts_realtime.connect()\n    \n    qwen_tts_realtime.update_session(\n        voice=create_voice(VOICE_FILE_PATH), # Replace the voice parameter with the custom voice generated by cloning.\n        response_format=AudioFormat.PCM_24000HZ_MONO_16BIT,\n        mode='server_commit'\n    )\n\n    for text_chunk in TEXT_TO_SYNTHESIZE:\n        print(f'[Send text]: {text_chunk}')\n        qwen_tts_realtime.append_text(text_chunk)\n        time.sleep(0.1)\n\n    qwen_tts_realtime.finish()\n    callback.wait_for_finished()\n\n    print(f'[Metric] session_id={qwen_tts_realtime.get_session_id()}, '\n          f'first_audio_delay={qwen_tts_realtime.get_first_audio_delay()}s')\n
\n

Citation

\n

If you find our model useful in your research, please consider citing us 📝 :)

\n
@misc{qwen3_tts_202512,\n  author       = {Qwen Team, Alibaba},\n  title        = {Qwen3-TTS Steps Up: Voice Cloning and Voice Design!},\n  year         = {2025},\n  url          = {https://qwen.ai/blog?id=qwen3-tts-vc-voicedesign},\n  urldate      = {2025-12-23}\n}\n
\n", "date_published": "2025-12-22T16:00:45+00:00", "external_url": "https://qwen.ai/blog?id=qwen3-tts-vc-voicedesign", "id": "urn:aggr:item:d71eb5ca9aad7f81cc6b53bc56f48da98f018ac7", "image": "https://img.alicdn.com/imgextra/i4/O1CN01Q9UPif1FfSgpkSBnP_!!6000000000514-0-tps-1890-1134.jpg", "summary": "Qwen3-TTS family has launched two new models: the voice design model Qwen3-TTS-VD-Flash (accessible via the Qwen API) and the voice cloning model Qwen3-TTS-VC-Flash (accessible via the Qwen API). Key Features: Voice…", "tags": [ "ai", "release" ], "title": "Qwen3-TTS Steps Up: Voice Cloning and Voice Design!", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2025-12-22-qwen3-tts-steps-up-voice-cloning-and-voice-design/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

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\n

QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DEMO

\n

Today, we are excited to introduce Qwen-Image-Layered, a model capable of decomposing an image into multiple RGBA layers. This layered representation unlocks inherent editability: each layer can be independently manipulated without affecting other content. Meanwhile, such a layered representation naturally supports high-fidelity elementary operations-such as resizing, reposition, and recoloring. By physically isolating semantic or structural components into distinct layers, our approach enables high-fidelity and consistent editing.

\n

Layered Decomposition in Application

\n

Given an image, Qwen-Image-Layered can decompose it into several RGBA layers:

\n

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\n

After decomposition, edits are applied exclusively to the target layer, physically isolating it from the rest of the content, and thereby fundamentally ensuring consistency across edits.

\n

For example, we can recolor the first layer and keep all other content untouched:

\n

\"\"

\n

We can also replace the second layer from a girl to a boy:

\n

\"\"

\n

Here, we revise the text to “Qwen-Image”:

\n

\"\"

\n

Furthermore, the layered structure naturally supports elemetary operations. For example, we can delete unwanted objects cleanly:

\n

\"\"

\n

We can also resize an object without distortion:

\n

\"\"

\n

After layer decomposition, we can move objects freely within the canvas:

\n

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Flexible and Iterative Decomposition

\n

Qwen-Image-Layered is not limited to a fixed number of layers. The model supports variable-layer decomposition. For example, we can decompose an image into either 3 or 8 layers as needed:

\n

\"\"

\n

Moreover, decomposition can be applied recursively: any layer can itself be further decomposed, enabling infinite decomposition.

\n

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\n

Conclusion

\n

Qwen-Image-Layered bridges the gap between raster imagery and structured, editable representations. By reimagining images as composable layers, we hope to enable intuitive, precise, and robust editing capabilities.

\n

Citation

\n

If you find our model useful in your research, please consider citing us 📝 :)

\n
@misc{yin2025qwenimagelayered,\n      title={Qwen-Image-Layered: Towards Inherent Editability via Layer Decomposition}, \n      author={Shengming Yin, Zekai Zhang, Zecheng Tang, Kaiyuan Gao, Xiao Xu, Kun Yan, Jiahao Li, Yilei Chen, Yuxiang Chen, Heung-Yeung Shum, Lionel M. Ni, Jingren Zhou, Junyang Lin, Chenfei Wu},\n      year={2025},\n      eprint={2512.15603},\n      archivePrefix={arXiv},\n      primaryClass={cs.CV},\n      url={https://arxiv.org/abs/2512.15603}, \n}\n
\n", "date_published": "2025-12-19T05:08:30+00:00", "external_url": "https://qwen.ai/blog?id=qwen-image-layered", "id": "urn:aggr:item:56611e32fbd6c56d17b2c791da86096352a578aa", "image": "https://img.alicdn.com/imgextra/i2/O1CN01y9LKxE248WbyrZzFw_!!6000000007346-0-tps-1590-954.jpg", "summary": "QWEN CHAT GITHUB HUGGING FACE MODELSCOPE DEMO Today, we are excited to introduce Qwen-Image-Layered, a model capable of decomposing an image into multiple RGBA layers. This layered representation unlocks inherent editability: each layer…", "tags": [ "ai", "research" ], "title": "Qwen-Image-Layered: Layered Decomposition for Inherent Editablity", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2025-12-19-qwen-image-layered-layered-decomposition-for-inherent/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

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\n

QWEN CHAT HUGGING FACE MODELSCOPE DASHSCOPE GITHUB PAPER HUGGING FACE DEMO MODELSCOPE DEMO

\n

Qwen3-Omni is a next-generation native multimodal large model capable of seamlessly processing multiple input modalities—including text, images, audio, and video—and generating both text and natural-sounding speech outputs simultaneously via real-time streaming responses. This version introduces multiple enhancements to improve model performance and efficiency.

\n

Qwen3-Omni-Flash-2025-12-01 is a comprehensively upgraded iteration built upon Qwen3-Omni.

\n

Key highlights of this upgraded version include:

\n\n

www.youtube.com

\n

Performance

\n

On objective benchmarks, Qwen3-Omni-Flash-2025-12-01 achieves substantial improvements across all modalities compared to Qwen3-Omni-Flash:

\n\n

With this upgrade, Qwen3-Omni-Flash-2025-12-01 truly embodies the vision of “Hear You. See You. Follow Smarter.”—delivering an AI interaction experience that is more natural, precise, and vivid than ever before.

\n

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What’s Next

\n

We are eager to hear your feedback and see the innovative applications you create with Qwen3-Omni. In the near future, we will further advance the model along multiple axes, including multi-speaker ASR, video OCR, audio–video proactive learning, and enhance support for agent-based workflows and function calling.

\n

Citation

\n

If you find our model helpful in your research, we’d appreciate a citation!

\n
@misc{qwen3_omni_20251201,\nauthor = {{Qwen Team, Alibaba}},\ntitle = {{Qwen3-Omni-Flash-2025-12-01：Hear You. See You. Follow Smarter!}},\nyear = {2025},\nurl = {https://qwen.ai/blog?id=qwen3-omni-20251201},\nurldate = {2025-12-05}\n}\n
\n", "date_published": "2025-12-08T21:00:00+00:00", "external_url": "https://qwen.ai/blog?id=qwen3-omni-flash-20251201", "id": "urn:aggr:item:7d1e0094fcefedb85af24d893a043c3fef1653a6", "image": "https://img.alicdn.com/imgextra/i1/O1CN01CBLbrb1NJx0kSCqbk_!!6000000001550-2-tps-1590-954.png", "summary": "QWEN CHAT HUGGING FACE MODELSCOPE DASHSCOPE GITHUB PAPER HUGGING FACE DEMO MODELSCOPE DEMO Qwen3-Omni is a next-generation native multimodal large model capable of seamlessly processing multiple input modalities—including text, images,…", "tags": [ "ai", "release" ], "title": "Qwen3-Omni-Flash-2025-12-01：Hear You. See You. Follow Smarter!", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2025-12-08-qwen3-omni-flash-2025-12-01-hear-you-see-you-follow-smarter/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

Introduction

\n

Reinforcement learning (RL) has become a core ingredient in advancing the reasoning capabilities of large language models (LLMs). Modern RL pipelines enable models to solve harder mathematical problems, write complex code, and reason over multimodal inputs. In practice, group‑based policy optimization—where multiple responses are sampled per prompt and their rewards are normalized within the group—has emerged as a dominant training paradigm for LLMs. However, despite its empirical success, stable and performant policy optimization remains challenging. A critical challenge lies in the variance of token‑level importance ratios, especially in large Mixture‑of‑Experts (MoE) models. These ratios quantify how far the current policy deviates from the behavior policy used to generate the training samples. When ratios fluctuate excessively (as they often do with expert routing or long autoregressive outputs), policy updates become noisy and unstable.

\n

Existing solutions such as GRPO (token‑level clipping) and GSPO (sequence‑level clipping) attempt to control this instability by enforcing hard clipping: whenever the importance ratio falls outside a fixed band, gradients are truncated. While this reduces catastrophic updates, it introduces two inherent limitations:

\n\n

As a result, GRPO and GSPO often struggle to strike a balance between stability, sample efficiency, and consistent learning progress. To address these limitations, we propose Soft Adaptive Policy Optimization (SAPO), an RL method designed for stable and performant optimization of LLMs. SAPO replaces hard clipping with a smooth, temperature‑controlled gating function that adaptively down‑weights off‑policy updates while preserving useful gradients. Unlike existing methods, SAPO offers:

\n\n

This unified design allows SAPO to achieve stable and effective learning.

\n

Soft Adaptive Policy Optimization (SAPO)

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SAPO optimizes the following surrogate objective:

\n

𝒥(θ) = 𝔼[ 1/G∑ᵢ₌₁^G 1/|yᵢ| ∑ₜ₌₁^(|yᵢ|) f_(i,t)(r_(i,t)(θ))Â_(i,t) ]

\n

where

\n\n

The gradient takes the form

\n

∇_θ 𝒥(θ) = 𝔼[ 1/G∑ᵢ₌₁^G 1/|yᵢ| ∑ₜ₌₁^(|yᵢ|) w_(i,t)(θ) r_(i,t)(θ) ∇_θ logπ_θ Â_(i,t) ]

\n

where the weight is

\n

w_(i,t)(θ) = 4,p_(i,t)(θ)(1−p_(i,t)(θ)), p_(i,t)(θ) = σ(τ_(i,t)(r_(i,t)(θ)−1)).

\n

This weight peaks at r_(i,t)(θ)=1 and decays smoothly on both sides.

\n

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\n

Why SAPO Works: A Gating-Function Perspective

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SAPO recovers sequence‑level coherence (connection to GSPO)

\n

Let sᵢ(θ) be the length‑normalized sequence‑level importance ratio: log sᵢ(θ) = 1/|yᵢ| ∑ₜ log r_(i,t)(θ).

\n

If the policy updates are small and the token log‑ratios within a sequence have low variance—two assumptions that empirically hold for most sequences—then the average SAPO token gate becomes approximately a sequence‑level gate of the form g(log sᵢ(θ)) ≈ sech²(τ/2log sᵢ(θ)). This means SAPO behaves like GSPO at the sequence level but with a continuous trust region instead of hard clipping.

\n

Key advantage over GSPO: If a few tokens in a sequence are very off‑policy,

\n\n

This improves sample efficiency.

\n

SAPO provides smooth token‑level adaptivity (connection to GRPO)

\n

GRPO uses hard clipping:

\n\n

This creates brittle, discontinuous optimization behavior.

\n

SAPO replaces the hard cutoff with a smooth decay:

\n\n

This allows SAPO to provide a more balanced way to retain useful learning signals while preventing unstable policy shifts.

\n

Asymmetric temperature for negative advantages improves stability

\n

Negative advantages increase the logits of many inappropriate tokens, especially in large vocabularies.
\nSAPO uses higher temperature for negative tokens (τ_(neg) > τₚₒₛ), which causes negative contributions to decay faster when off‑policy. Empirically, this simple asymmetry significantly improves RL training stability and performance.

\n

Experimental Results

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1. Controlled RL on Mathematical Reasoning (Qwen3‑30B‑A3B)

\n

We compare SAPO against GSPO and GRPO‑R2 (GRPO with routing replay) using a cold‑start model fine-tuned from Qwen3-30B-A3B-Base.

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Findings:

\n\n

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\n

Temperature ablations confirm that:

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2. Large‑Scale RL for Qwen3‑VL Models

\n

SAPO consistently improves performance across models of varying sizes and across both MoE and dense architectures. For comparison, we train a preliminary cold-start checkpoint of Qwen3‑VL‑30B‑A3B on a mixture of math, coding, logic, and multimodal tasks. Evaluation benchmarks include:

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Results: SAPO consistently outperforms both GSPO and GRPO‑R2 under the same compute budget.

\n

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\n

What SAPO Means for the Future of RL-Trained LLMs

\n

SAPO offers a practical way to stabilize and enhance RL training for LLMs:

\n\n

As RL continues to drive frontier LLM capabilities, we expect that SAPO will become a foundational component of RL training pipelines.

\n

Want to Learn More?

\n

For full technical details, theoretical analysis, and extensive experiments, please refer to our paper:

\n

Soft Adaptive Policy Optimization

\n

If you find our work helpful, feel free to cite it.

\n
@article{sapo,\ntitle={Soft Adaptive Policy Optimization},\nauthor={Gao, Chang and Zheng, Chujie and Chen, Xiong-Hui and Dang, Kai and Liu, Shixuan and Yu, Bowen and Yang, An and Bai, Shuai and Zhou, Jingren and Lin, Junyang},\njournal={arXiv preprint arXiv:2511.20347},\nyear={2025}\n}\n
\n", "date_published": "2025-12-04T20:00:00+00:00", "external_url": "https://qwen.ai/blog?id=sapo", "id": "urn:aggr:item:3dfe48e32fc465930cc65fc1358f57798bc23df0", "image": "https://img.alicdn.com/imgextra/i4/O1CN018ECcH61qkAXZSFBFU_!!6000000005533-2-tps-1890-1134.png", "summary": "Introduction Reinforcement learning (RL) has become a core ingredient in advancing the reasoning capabilities of large language models (LLMs). Modern RL pipelines enable models to solve harder mathematical problems, write complex code, and…", "tags": [ "ai", "research" ], "title": "SAPO: A Stable and Performant Reinforcement Learning Method for Training Large Language Models", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2025-12-04-sapo-a-stable-and-performant-reinforcement-learning-method/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

DASHSCOPE HUGGING FACE DEMO MODELSCOPE DEMO

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Qwen3-TTS-Flash is a flagship text-to-speech model that supports multi-timbre, multi-lingual, and multi-dialect speech synthesis. It aims to produce natural and expressive speech and is available via Qwen API.

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Major Improvements:

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Samples

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Qwen3-TTS offers a diverse range of distinctive, emotionally rich timbres for users to choose from, meeting the needs of a variety of scenarios. Here are some sample synthesized samples:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
TimbreLanguageTextSample
RyanEnglishYo, I'm Ryan! The guy who still uses "20 3 4 5 6" as his password... and no, I haven't changed it since 2015! By day I fix computers, by night I tweak my diet... well, sometimes. When I'm not geeking out over code, you'll find me trying to invent new ways to fail at cooking! My last experiment? A soufflé that looked like a melted candle... but hey, it tasted like victory! Now, who's ready to see me blow up a microwave?
JenniferEnglishHi there! I'm Jennifer!Ah-ha，I love learning new things and meeting awesome people like you. When I'm not working as an actor, you'll find me exploring cafes, playing guitar, or just laughing at my own bad jokes. Always up for an adventure—let's connect and make great memories together!
KaterinaEnglishHello! How are you today? I'm doing great, thank you! Hi everyone! My name is Katerina, and I'm thrilled to be here today. By day, I’m an actor, but when I’m not performing, you’ll find me exploring new hobbies—like travel ，though I once accidentally booked a one-way ticket to Iceland... but hey, the Northern Lights were worth it!
AidenEnglishHi everyone! I’m Aiden—originally from Los Angeles, though I swear I spend half my life between the mic and the stove. By day, I work with my voice: narrating, guiding, sometimes just helping words sound like they’re meant to be heard. But come evening? You’ll find me elbow-deep in garlic and herbs, chasing that perfect balance of crisp and tender, sweet and sharp. I’ve always believed good communication—whether it’s a recipe or a story—should feel like a conversation over dinner: warm, clear, and full of heart. So really… it’s a pleasure to meet you here.
EliasChinese比如说我上次做的那个比较文学的课题，这个团队当中有研究东亚文学的，他就提出了不少我没有想到过的角度。嗯，但是核心的部分呢，我还是喜欢自己去琢磨啊，保证思路的连贯。
BunnyChinese我现在啊已经形成条件反射了！只要一打开外卖盒，手就会自动去摸遥控器或者手机。有时候去朋友家吃饭，人家正儿八经的在餐桌上边吃边聊天。
CherryChinese嗨～今天过得怎么样呀？刚路过那家你爱喝的奶茶店，想着你要是累了，咱俩待会儿一块儿去坐坐？外面阳光可好了，照得人心里暖乎乎的～
MaiaChinese其实你现在难过是很正常的，谁都会有低谷的时候。我挺懂你这种感受的，别对自己太苛刻了，真的没关系。有些事情慢慢来就好，给自己点时间。如果你想聊天或者吐槽，我一直都在，随时可以找我。我相信你一定能熬过去。
MomoChinese我命令你们这些小男生，晚上早一点睡觉，听到没有？要是被我发现谁还偷玩手机，我就……我就生气给你们看哦！
NofishChinese哎，你瞧那边，那个塔。好高啊！像一根细细的冰棍插在云朵里。每天早上都有小鸟飞过。你看！它们在塔尖上绕圈圈。塔身是亮亮的蓝色，还会反光呢。底下有一个喷泉，水珠跳来跳去，像在和人打招呼似的。
MoonChinese如果喜欢，就把这一切当作是荣耀，而不是炫耀。心怀荣耀，即战无不胜。
BellaChinese嘿嘿，哈，你这个样子好像个小呆瓜呀。谁说我在干坏事啦，才没有呢。不准批评我，不然我就哭给你看，哼！
ArthurChinese种地前得先敬三碗酒。头三垄地得祭土地神，中间三垄得给龙王留条活路，最后一垄得倒进井里喂井龙王——这老家伙要是半夜拽你裤腿讨水喝，可咋办？我们村那棵老槐树，根须长得像太极图案，听说半夜三更还能听见它跟嫦娥下棋呢！
NiniChinese哥哥你怎么了呀？看你从刚才就没怎么说话，眉头还皱着，是不是遇到啥烦心事了？要是工作上不顺心跟我说说呗，就算我帮不上大忙，听你念叨念叨也能舒服点呀。
PipChinese你快看，路边的小狗狗们凑在一起玩来玩去，绝对是在交换超级重要的秘密了。那只黄色的小狗肯定在跟黑色的小狗说，昨天我在三号垃圾桶旁边捡到了半根火腿肠，香到不行唉，今天要不要一起去看看呢？
VincentChinese话说这长安秋夜，诸位听好了！天边残星闪烁，雁阵南归，高楼之上，笛声悠扬，哀婉如泣。紫菊半开，红莲凋落，渔舟鲈鱼鲜美，却无人归。霜露渐浓，南冠楚囚之故事，仿佛重现啊！
BellonaChinese再有啊，就是评书表演当中容易被忽视的，其实很重要的一个细节是什么呢？就是气口儿和眼神儿。尤其说现场的时候儿啊，咱们说的现场，气口儿呢，就不光是指这个换气儿，更指的是节奏中的停顿和留白。
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Qwen3-TTS provides comprehensive support for a variety of Chinese dialects, accurately capturing regional accents, tones, and local charm. Here are some sample synthesized samples:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
TimbreAccentTextSample
DylanBeijing别提了！跟对门儿老王他们搓到后半夜，输得裤衩都快保不住了！你说我这手气，邪了门儿了。我呀，还是回家喂我的画眉要紧。瞧见没？今儿个叫得倍儿欢。
DylanNanjing哎——你搞什么鬼啊？骑个车横冲直撞的！眼珠子长后脑勺上啦？再这么骑，老子把你车子掀翻掉！真当老子脾气好是不是？！
RoyHokkien唉哟，阿嬷，今仔日涨工啦，水电、肥料拢涨，我哪敢乱开价？
EricSichuan哎哟我个先人板板！你摸张牌磨蹭半天搞啥子嘛？打个二筒犹豫三分钟，脑壳进水嗦？快点嘛——再不动手老子要睡着咯！
KikiCantonese今晚打邊爐好唔好啊？突然间好想食肥牛同埋響鈴卷啊！打边炉最开心就係一班人围埋一齐慢慢倾慢慢食。你仲想加啲咩料落去？我就想食鱼皮饺同埋墨鱼丸啦，仲可以饮埋冻柠茶，爽呀！不如我哋顺便去买埋啲蔬菜，芋头、金针菇都唔错喎，咁先齐全啊嘛！
RockyCantonese咁诶话说有一次咧我哋诶亚云喺，喺广州。就好似系搞一个开幕式系亚云嘅开幕式，我哋去我哋去拍。嗯，我唔记得咗开幕式定闭幕式嘞。咁咧就嘶诶，我哋有一个有一个看台咧专门系俾所有嘅国内、国外嘅媒体去去拍嘅一个看台。
MarcusShaanxi走咧——！西安城的老城墙，比馍还硬朗！biangbiang面一甩，黄河水都得喊声‘姐’！兵马俑列阵，秦腔吼起来，羊肉泡馍配冰峰，酸汤水煮活了三千年！来陕西，不光是看历史，是要把《长恨歌》唱进肉夹馍里，把汉唐风揉进擀面杖里
PeterTianjin哎哟喂！天津卫的码头，鱼龙混杂也风雅～狗不理包子褶儿十八，炸糕甜得黏牙，煎饼果子卷上天！
SunnySichuan哎呀～你来啦？等你好久咯！我刚切了西瓜，冰冰凉凉的，甜得很，专门给你留的最大一块！快进来嘛，站到太阳底下晒黑咯，我可要心疼咯～
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Qwen3-TTS also supports authentic and natural multilingual timbres, with speech patterns that closely resemble native pronunciation. Here are some sample synthesized samples:

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
TimbreLanguageTextSample
LennGermanKannst du bitte die Musik leiser stellen? Ich kann mich nicht konzentrieren.
DolceItalianCiao, bellissima! Hai quel look che mi fa girare la testa—sempre impeccabile. Stasera c’è un nuovo lounge in centro… ci vai con me? Prometto: niente di noioso, solo stile e buona musica.
Ono AnnaJapaneseえー、困るね…。今日、絶対遅刻したくないのに。でもタクシーって高いし、混んでそうじゃない？どうしよう、もうちょっと様子見た方がいいかな。でも、間に合わなかったら最悪だし…。あー、やっぱり電車って信用できない時あるよね。○○ちゃんはどうする？一緒にタクシー乗る？
Radio GolBrazilianAh… minha infância era isso:o cheiro de mato molhado,o canto do sabiá,e a bola de meia rolando na calçada quente.Hoje, cada vez que ouço um samba de raiz…meu peito aperta —não de tristeza, não…mas da alegria tão grande que ainda cabe no coração!
BodegaSpanish¡Hola, chaval! ¿Qué tal? ¡Ven acá, hombre! Hoy el sol brilla, el café está recién hecho y la vida es bonita. ¡Sonríe un poco, que no es pa’ tanto! ¡Dale, vamos a tomarnos un cafecito!
SoheeKorean안녕하세요! 오늘 날씨 진짜 좋네요~ ☀️ 방금 길에서 강아지 봤는데 너무 귀여워서 사진 찍었어요! 혹시 커피 한 잔 할래요? 제가 살게요—오늘 기분이 완전 최고라서! 😊
EmilienFrenchTu sais… chaque fois que je te vois, Paris s’arrête de respirer. Même la Seine retient son souffle. Viens, on va se perdre dans les rues — juste toi et moi, ce soir.
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Performance

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How to use

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Use Qwen3-TTS with Qwen API is simple. We demonstrate a code snippet for you to play with it below:

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# Please install the latest version of the DashScope SDK.\nimport os\nimport requests\nimport dashscope\n\ntext = "Let me recommend a T-shirt to everyone. This one is really super good-looking, and the color is very classy. It’s also a great piece to mix and match with anything, so you can totally buy it without hesitation. It looks amazing and is very forgiving on the figure—no matter what body type you have, it will look great on you. Highly recommend you place an order!"\n# Usage of the SpeechSynthesizer Interface: dashscope.audio.qwen_tts.SpeechSynthesizer.call(...)\nresponse = dashscope.MultiModalConversation.call(\n    model="qwen3-tts-flash-2025-11-27",\n    api_key=os.getenv("DASHSCOPE_API_KEY"),\n    text=text,\n    voice="Ryan",\n    language_type="English", # It is recommended to match the language with the text in order to obtain correct pronunciation and natural intonation.\n    stream=False\n)\naudio_url = response.output.audio.url\nsave_path = "downloaded_audio.wav"  # Custom save path.\n\ntry:\n    response = requests.get(audio_url)\n    response.raise_for_status()  # Check whether the request was successful.\n    with open(save_path, 'wb') as f:\n        f.write(response.content)\n    print(f"The audio file has been saved to: {save_path}")\nexcept Exception as e:\n    print(f"Download failed: {str(e)}")\n
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Citation

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If you find our model useful in your research, please consider citing it 📝 :)

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@misc{qwen3_tts_202511,\n  author       = {Qwen Team, Alibaba},\n  title        = {Qwen3-TTS Update! 49 Timbres + 10 Languages + 9 Dialects},\n  year         = {2025},\n  url          = {https://qwen.ai/blog?id=qwen3-tts-1128},\n  urldate      = {2025-12-01}\n}\n
\n", "date_published": "2025-12-04T16:00:04+00:00", "external_url": "https://qwen.ai/blog?id=qwen3-tts-1128", "id": "urn:aggr:item:bc51ba397305a075c708f4c399cc99fcf6649162", "image": "https://img.alicdn.com/imgextra/i2/O1CN01Gdt9J71xz0gU7G4bA_!!6000000006513-2-tps-1890-1134.png", "summary": "Qwen3-TTS-Flash is a flagship text-to-speech model that supports multi-timbre, multi-lingual, and multi-dialect speech synthesis. It aims to produce natural and expressive speech and is available via Qwen API. Major Improvements: Richer…", "tags": [ "ai", "release" ], "title": "Qwen3-TTS Update! 49 Timbres + 10 Languages + 9 Dialects", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2025-12-04-qwen3-tts-update-49-timbres-10-languages-9-dialects/" }, { "authors": [ { "name": "QwenTeam" } ], "content_html": "

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Click here to experience the latest Qwen DeepResearch

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How does inspiration die?

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It usually doesn’t die from “not being good enough”, but from being “too much trouble”.

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When a thought flashes, it’s still fragile and unverified. After a brief moment of excitement, our brains immediately begin to assess the “cost”:

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In that very moment of “cost assessment”, the vast majority of inspiring ideas are “rationally” killed off. We subconsciously avoid them because the traditional barrier to “deep research” is simply too high.

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We’ve been thinking about how to make “deep research” no longer a heavy task that needs to be “booted up”, but rather a natural extension of thought.

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This is the mission Qwen DeepResearch was born to fulfill.

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What we want to do is completely change the dynamics of thinking, minimizing the friction between “thinking” and “validation”. Now, you can confidently toss it those “random whimsical ideas” or those “extremely complex, where-do-I-even-begin” deep questions.

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It is no longer just a simple information retrieval tool, but your dedicated research assistant, ready to instantly “catch” your ideas and handle all the heavy lifting from inspiration to insight. When the cost of exploration approaches zero, every spark of curiosity is worthy of immediate pursuit.

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This time, we’re bringing not just an increase in features, but a qualitative leap in research depth and efficiency.

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We systematically evaluated the new Qwen DeepResearch 2511 across multiple dimensions, including anti-hallucination rate, report comprehensiveness, reliability, instruction following, report length, search depth, and research time. The results show a comprehensive and significant improvement in overall performance compared to version 2507.

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Behind every improvement in experience, there is a new technological breakthrough. Qwen DeepResearch 2511 employs a multi-agent collaborative mechanism, with joint optimization in areas such as user need analysis, overall strategy planning, search verification strategies, parallel tool calls, web page reading analysis, and research report writing. This is combined with an overall Memory management mechanism and research state tracking to accomplish complex research tasks.

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These numbers are proof of the effort we’ve invested to support those whimsical ideas. With Qwen DeepResearch, we want to provide not just answers, but a new way of supporting the thinking process: a way that makes the journey from a flash of inspiration to deep insight almost seamless. That moment of hesitation we mentioned at the beginning—the one caused by it being “too much trouble”—is precisely the barrier Qwen DeepResearch aims to eliminate.

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Watch the demo video now to see firsthand how Qwen DeepResearch builds a professional, in-depth research report from a simple “question input”.

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Now, it’s time to try it. Go ahead and ask that question you once thought was “too complex”, or toss it that “whimsical idea” you’ve had on the back burner for so long. We look forward to your feedback.

\n", "date_published": "2025-11-12T20:59:26+00:00", "external_url": "https://qwen.ai/blog?id=qwen-deepresearch", "id": "urn:aggr:item:97ef668c88951be27259ea7225c7d65131c607cc", "image": "https://img.alicdn.com/imgextra/i3/O1CN01DvbmDu1DeQYSDv1qm_!!6000000000241-2-tps-1890-1134.png", "summary": "Click here to experience the latest Qwen DeepResearch How does inspiration die? It usually doesn’t die from “not being good enough”, but from being “too much trouble”. When a thought flashes, it’s still fragile and unverified. After a…", "tags": [ "ai", "release" ], "title": "Qwen DeepResearch: When Inspiration Becomes Its Own Reason", "url": "https://aggr.aymericbeaumet.com/items/qwen-ai/2025-11-12-qwen-deepresearch-when-inspiration-becomes-its-own-reason/" } ], "language": "en", "title": "Qwen", "version": "https://jsonfeed.org/version/1.1" }