{ "@context": "https://schema.org", "@id": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-04-15-qwen3-6-35b-a3b-agentic-coding-power-now-open-to-all/#webpage", "@type": [ "WebPage", "ArchiveComponent" ], "articleBody": "![Qwen3.6-35B-A3B Main Image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/Figures/3.6_35b_a3b_banner.png)\n\n[QWEN STUDIO](https://chat.qwen.ai) [HUGGING FACE](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) [MODELSCOPE](https://modelscope.cn/models/Qwen/Qwen3.6-35B-A3B) [DISCORD](https://discord.gg/yPEP2vHTu4)\n\nFollowing the launch of [Qwen3.6-Plus](https://qwen.ai/blog?id=qwen3.6), 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- **Qwen3.6-35B-A3B** is a fully open-source MoE model (35B total / 3B active), featuring:\n - exceptional agentic coding capability competitive with much larger models\n - strong multimodal perception and reasoning ability\n- You can chat interactively on [Qwen Studio](https://chat.qwen.ai), call via API as `Qwen3.6-Flash` on [Alibaba Cloud Model Studio API](https://modelstudio.alibabacloud.com/), or download weights from [Hugging Face](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) and [ModelScope](https://modelscope.cn/models/Qwen/Qwen3.6-35B-A3B).\n\n![](https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3.6/Figures/qwen3.6_35b_a3b_score.png)\n\n## Performance\n\nBelow we present comprehensive evaluations of Qwen3.6-35B-A3B against peer-scale models across a wide range of tasks and modalities.\n\n### Language\n\nWith 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| | Qwen3.5-27B | Gemma4-31B | Qwen3.5-35BA3B | Gemma4-26BA4B | Qwen3.6-35BA3B |\n| ---------------------- | ----------- | ---------- | -------------- | ------------- | -------------- |\n| Coding Agent | | | | | |\n| SWE-bench Verified | 75.0 | 52.0 | 70.0 | 17.4 | 73.4 |\n| SWE-bench Multilingual | 69.3 | 51.7 | 60.3 | 17.3 | 67.2 |\n| SWE-bench Pro | 51.2 | 35.7 | 44.6 | 13.8 | 49.5 |\n| Terminal-Bench 2.0 | 41.6 | 42.9 | 40.5 | 34.2 | 51.5 |\n| Claw-Eval Avg | 64.3 | 48.5 | 65.4 | 58.8 | 68.7 |\n| Claw-Eval Pass^3 | 46.2 | 25.0 | 51.0 | 28.0 | 50.0 |\n| SkillsBench Avg5 | 27.2 | 23.6 | 4.4 | 12.3 | 28.7 |\n| QwenClawBench | 52.2 | 41.7 | 47.7 | 38.7 | 52.6 |\n| NL2Repo | 27.3 | 15.5 | 20.5 | 11.6 | 29.4 |\n| QwenWebBench | 1068 | 1197 | 978 | 1178 | 1397 |\n| General Agent | | | | | |\n| TAU3-Bench | 68.4 | 67.5 | 68.9 | 59.0 | 67.2 |\n| VITA-Bench | 41.8 | 43.0 | 29.1 | 36.9 | 35.6 |\n| DeepPlanning | 22.6 | 24.0 | 22.8 | 16.2 | 25.9 |\n| Tool Decathlon | 31.5 | 21.2 | 28.7 | 12.0 | 26.9 |\n| MCPMark | 36.3 | 18.1 | 27.0 | 14.2 | 37.0 |\n| MCP-Atlas | 68.4 | 57.2 | 62.4 | 50.0 | 62.8 |\n| WideSearch | 66.4 | 35.2 | 59.1 | 38.3 | 60.1 |\n| Knowledge | | | | | |\n| MMLU-Pro | 86.1 | 85.2 | 85.3 | 82.6 | 85.2 |\n| MMLU-Redux | 93.2 | 93.7 | 93.3 | 92.7 | 93.3 |\n| SuperGPQA | 65.6 | 65.7 | 63.4 | 61.4 | 64.7 |\n| C-Eval | 90.5 | 82.6 | 90.2 | 82.5 | 90.0 |\n| STEM & Reasoning | | | | | |\n| GPQA | 85.5 | 84.3 | 84.2 | 82.3 | 86.0 |\n| HLE | 24.3 | 19.5 | 22.4 | 8.7 | 21.4 |\n| LiveCodeBench v6 | 80.7 | 80.0 | 74.6 | 77.1 | 80.4 |\n| HMMT Feb 25 | 92.0 | 88.7 | 89.0 | 91.7 | 90.7 |\n| HMMT Nov 25 | 89.8 | 87.5 | 89.2 | 87.5 | 89.1 |\n| HMMT Feb 26 | 84.3 | 77.2 | 78.7 | 79.0 | 83.6 |\n| IMOAnswerBench | 79.9 | 74.5 | 76.8 | 74.3 | 78.9 |\n| AIME26 | 92.6 | 89.2 | 91.0 | 88.3 | 92.7 |\n\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\n### Vision Language\n\nQwen3.6 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| | Qwen3.5-27B | Claude-Sonnet-4.5 | Gemma4-31B | Gemma4-26BA4B | Qwen3.5-35B-A3B | Qwen3.6-35B-A3B |\n| ------------------------------------------- | ----------- | ----------------- | ---------- | ------------- | --------------- | --------------- |\n| STEM and Puzzle | | | | | | |\n| MMMU | 82.3 | 79.6 | 80.4 | 78.4 | 81.4 | 81.7 |\n| MMMU-Pro | 75.0 | 68.4 | 76.9\\* | 73.8\\* | 75.1 | 75.3 |\n| Mathvista(mini) | 87.8 | 79.8 | 79.3 | 79.4 | 86.2 | 86.4 |\n| ZEROBench\\_sub | 36.2 | 26.3 | 26.0 | 26.3 | 34.1 | 34.4 |\n| General VQA | | | | | | |\n| RealWorldQA | 83.7 | 70.3 | 72.3 | 72.2 | 84.1 | 85.3 |\n| MMBenchEN-DEV-v1.1 | 92.6 | 88.3 | 90.9 | 89.0 | 91.5 | 92.8 |\n| SimpleVQA | 56.0 | 57.6 | 52.9 | 52.2 | 58.3 | 58.9 |\n| HallusionBench | 70.0 | 59.9 | 67.4 | 66.1 | 67.9 | 69.8 |\n| Text Recognition and Document Understanding | | | | | | |\n| OmniDocBench1.5 | 88.9 | 85.8 | 80.1 | 74.4 | 89.3 | 89.9 |\n| CharXiv(RQ) | 79.5 | 67.2 | 67.9 | 69.0 | 77.5 | 78.0 |\n| CC-OCR | 81.0 | 68.1 | 75.7 | 74.5 | 80.7 | 81.9 |\n| AI2D\\_TEST | 92.9 | 87.0 | 89.0 | 88.3 | 92.6 | 92.7 |\n| Spatial Intelligence | | | | | | |\n| RefCOCO(avg) | 90.9 | -- | -- | -- | 89.2 | 92.0 |\n| ODInW13 | 41.1 | -- | -- | -- | 42.6 | 50.8 |\n| EmbSpatialBench | 84.5 | 71.8 | -- | -- | 83.1 | 84.3 |\n| RefSpatialBench | 67.7 | -- | -- | -- | 63.5 | 64.3 |\n| Video Understanding | | | | | | |\n| VideoMME(w sub.) | 87.0 | 81.1 | -- | -- | 86.6 | 86.6 |\n| VideoMME(w/o sub.) | 82.8 | 75.3 | -- | -- | 82.5 | 82.5 |\n| VideoMMMU | 82.3 | 77.6 | 81.6 | 76.0 | 80.4 | 83.7 |\n| MLVU | 85.9 | 72.8 | -- | -- | 85.6 | 86.2 |\n| MVBench | 74.6 | -- | -- | -- | 74.8 | 74.6 |\n| LVBench | 73.6 | -- | -- | -- | 71.4 | 71.4 |\n\n\\* Empty cells (--) indicate scores not available or not applicable.\n\n## Build with Qwen3.6-35B-A3B\n\nQwen3.6-35B-A3B is available as open weights on [Hugging Face](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) and [ModelScope](https://modelscope.cn/models/Qwen/Qwen3.6-35B-A3B) for self-hosting, and through the [Alibaba Cloud Model Studio](https://modelstudio.alibabacloud.com/) API as `qwen3.6-flash`. You can also try it instantly on [Qwen Studio](https://chat.qwen.ai).\n\nThe 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\n### API Usage\n\nThis release supports the `preserve_thinking` feature: preserving thinking content from all preceding turns in messages, which is **recommended for agentic tasks**.\n\n#### Alibaba Cloud Model Studio\n\nAlibaba 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\nExample code for chat completions API is provided below:\n\n```python\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\nFor more information, please visit the [API doc](https://modelstudio.console.alibabacloud.com/?tab=doc#/doc/?type=model&url=2840915).\n\n### Coding & Agents\n\nQwen3.6-35B-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\n#### OpenClaw\n\nQwen3.6-35B-A3B is compatible with [OpenClaw](https://openclaw.ai) (formerly Moltbot / Clawdbot), a self-hosted open-source AI coding agent. Connect it to [Model Studio](https://www.alibabacloud.com/help/en/model-studio/openclaw) to get a full agentic coding experience in the terminal. Get started with the following script:\n\n```bash\n# Node.js 22+\ncurl -fsSL https://molt.bot/install.sh | bash # macOS / Linux\n\n# Set your API key\nexport DASHSCOPE_API_KEY=\n\n# Launch OpenClaw\nopenclaw dashboard # web browser\n# openclaw tui # Open a new terminal and start the TUI\n```\n\nOn 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```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\": \"qwen3.6-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\n#### Qwen Code\n\nQwen3.6-35B-A3B is compatible with [Qwen Code](https://qwen.ai/qwencode), an open-source AI agent designed for the terminal and deeply optimized for the Qwen Series. Get started with the following script:\n\n```bash\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\nOn first use, you’ll be prompted to sign in. You can run `/auth` anytime to switch authentication methods.\n\n#### Claude Code\n\nQwen APIs also support the Anthropic API protocol, meaning you can use it with tools like **Claude Code** for elevated coding experience:\n\n```bash\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=\n\n# Launch the CLI\nclaude\n```\n\n## Summary\n\nQwen3.6-35B-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\nLooking 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\n## Citation\n\nFeel free to cite the following article if you find Qwen3.6-35B-A3B helpful:\n\n```bibtex\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", "authors": [ "QwenTeam" ], "captured_at": "2026-10-19T01:26:53.998049939+00:00", "category": "ai", "content_html": "

\"Qwen3.6-35B-A3B

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QWEN STUDIO HUGGING FACE MODELSCOPE DISCORD

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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

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Performance

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

\n

Language

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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
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* 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.

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Build with Qwen3.6-35B-A3B

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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
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For more information, please visit the API doc.

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Coding & Agents

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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

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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

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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", "content_markdown": "![Qwen3.6-35B-A3B Main Image](https://qianwen-res.oss-accelerate.aliyuncs.com/Qwen3.6/Figures/3.6_35b_a3b_banner.png)\n\n[QWEN STUDIO](https://chat.qwen.ai) [HUGGING FACE](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) [MODELSCOPE](https://modelscope.cn/models/Qwen/Qwen3.6-35B-A3B) [DISCORD](https://discord.gg/yPEP2vHTu4)\n\nFollowing the launch of [Qwen3.6-Plus](https://qwen.ai/blog?id=qwen3.6), 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- **Qwen3.6-35B-A3B** is a fully open-source MoE model (35B total / 3B active), featuring:\n - exceptional agentic coding capability competitive with much larger models\n - strong multimodal perception and reasoning ability\n- You can chat interactively on [Qwen Studio](https://chat.qwen.ai), call via API as `Qwen3.6-Flash` on [Alibaba Cloud Model Studio API](https://modelstudio.alibabacloud.com/), or download weights from [Hugging Face](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) and [ModelScope](https://modelscope.cn/models/Qwen/Qwen3.6-35B-A3B).\n\n![](https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3.6/Figures/qwen3.6_35b_a3b_score.png)\n\n## Performance\n\nBelow we present comprehensive evaluations of Qwen3.6-35B-A3B against peer-scale models across a wide range of tasks and modalities.\n\n### Language\n\nWith 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| | Qwen3.5-27B | Gemma4-31B | Qwen3.5-35BA3B | Gemma4-26BA4B | Qwen3.6-35BA3B |\n| ---------------------- | ----------- | ---------- | -------------- | ------------- | -------------- |\n| Coding Agent | | | | | |\n| SWE-bench Verified | 75.0 | 52.0 | 70.0 | 17.4 | 73.4 |\n| SWE-bench Multilingual | 69.3 | 51.7 | 60.3 | 17.3 | 67.2 |\n| SWE-bench Pro | 51.2 | 35.7 | 44.6 | 13.8 | 49.5 |\n| Terminal-Bench 2.0 | 41.6 | 42.9 | 40.5 | 34.2 | 51.5 |\n| Claw-Eval Avg | 64.3 | 48.5 | 65.4 | 58.8 | 68.7 |\n| Claw-Eval Pass^3 | 46.2 | 25.0 | 51.0 | 28.0 | 50.0 |\n| SkillsBench Avg5 | 27.2 | 23.6 | 4.4 | 12.3 | 28.7 |\n| QwenClawBench | 52.2 | 41.7 | 47.7 | 38.7 | 52.6 |\n| NL2Repo | 27.3 | 15.5 | 20.5 | 11.6 | 29.4 |\n| QwenWebBench | 1068 | 1197 | 978 | 1178 | 1397 |\n| General Agent | | | | | |\n| TAU3-Bench | 68.4 | 67.5 | 68.9 | 59.0 | 67.2 |\n| VITA-Bench | 41.8 | 43.0 | 29.1 | 36.9 | 35.6 |\n| DeepPlanning | 22.6 | 24.0 | 22.8 | 16.2 | 25.9 |\n| Tool Decathlon | 31.5 | 21.2 | 28.7 | 12.0 | 26.9 |\n| MCPMark | 36.3 | 18.1 | 27.0 | 14.2 | 37.0 |\n| MCP-Atlas | 68.4 | 57.2 | 62.4 | 50.0 | 62.8 |\n| WideSearch | 66.4 | 35.2 | 59.1 | 38.3 | 60.1 |\n| Knowledge | | | | | |\n| MMLU-Pro | 86.1 | 85.2 | 85.3 | 82.6 | 85.2 |\n| MMLU-Redux | 93.2 | 93.7 | 93.3 | 92.7 | 93.3 |\n| SuperGPQA | 65.6 | 65.7 | 63.4 | 61.4 | 64.7 |\n| C-Eval | 90.5 | 82.6 | 90.2 | 82.5 | 90.0 |\n| STEM & Reasoning | | | | | |\n| GPQA | 85.5 | 84.3 | 84.2 | 82.3 | 86.0 |\n| HLE | 24.3 | 19.5 | 22.4 | 8.7 | 21.4 |\n| LiveCodeBench v6 | 80.7 | 80.0 | 74.6 | 77.1 | 80.4 |\n| HMMT Feb 25 | 92.0 | 88.7 | 89.0 | 91.7 | 90.7 |\n| HMMT Nov 25 | 89.8 | 87.5 | 89.2 | 87.5 | 89.1 |\n| HMMT Feb 26 | 84.3 | 77.2 | 78.7 | 79.0 | 83.6 |\n| IMOAnswerBench | 79.9 | 74.5 | 76.8 | 74.3 | 78.9 |\n| AIME26 | 92.6 | 89.2 | 91.0 | 88.3 | 92.7 |\n\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\n### Vision Language\n\nQwen3.6 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| | Qwen3.5-27B | Claude-Sonnet-4.5 | Gemma4-31B | Gemma4-26BA4B | Qwen3.5-35B-A3B | Qwen3.6-35B-A3B |\n| ------------------------------------------- | ----------- | ----------------- | ---------- | ------------- | --------------- | --------------- |\n| STEM and Puzzle | | | | | | |\n| MMMU | 82.3 | 79.6 | 80.4 | 78.4 | 81.4 | 81.7 |\n| MMMU-Pro | 75.0 | 68.4 | 76.9\\* | 73.8\\* | 75.1 | 75.3 |\n| Mathvista(mini) | 87.8 | 79.8 | 79.3 | 79.4 | 86.2 | 86.4 |\n| ZEROBench\\_sub | 36.2 | 26.3 | 26.0 | 26.3 | 34.1 | 34.4 |\n| General VQA | | | | | | |\n| RealWorldQA | 83.7 | 70.3 | 72.3 | 72.2 | 84.1 | 85.3 |\n| MMBenchEN-DEV-v1.1 | 92.6 | 88.3 | 90.9 | 89.0 | 91.5 | 92.8 |\n| SimpleVQA | 56.0 | 57.6 | 52.9 | 52.2 | 58.3 | 58.9 |\n| HallusionBench | 70.0 | 59.9 | 67.4 | 66.1 | 67.9 | 69.8 |\n| Text Recognition and Document Understanding | | | | | | |\n| OmniDocBench1.5 | 88.9 | 85.8 | 80.1 | 74.4 | 89.3 | 89.9 |\n| CharXiv(RQ) | 79.5 | 67.2 | 67.9 | 69.0 | 77.5 | 78.0 |\n| CC-OCR | 81.0 | 68.1 | 75.7 | 74.5 | 80.7 | 81.9 |\n| AI2D\\_TEST | 92.9 | 87.0 | 89.0 | 88.3 | 92.6 | 92.7 |\n| Spatial Intelligence | | | | | | |\n| RefCOCO(avg) | 90.9 | -- | -- | -- | 89.2 | 92.0 |\n| ODInW13 | 41.1 | -- | -- | -- | 42.6 | 50.8 |\n| EmbSpatialBench | 84.5 | 71.8 | -- | -- | 83.1 | 84.3 |\n| RefSpatialBench | 67.7 | -- | -- | -- | 63.5 | 64.3 |\n| Video Understanding | | | | | | |\n| VideoMME(w sub.) | 87.0 | 81.1 | -- | -- | 86.6 | 86.6 |\n| VideoMME(w/o sub.) | 82.8 | 75.3 | -- | -- | 82.5 | 82.5 |\n| VideoMMMU | 82.3 | 77.6 | 81.6 | 76.0 | 80.4 | 83.7 |\n| MLVU | 85.9 | 72.8 | -- | -- | 85.6 | 86.2 |\n| MVBench | 74.6 | -- | -- | -- | 74.8 | 74.6 |\n| LVBench | 73.6 | -- | -- | -- | 71.4 | 71.4 |\n\n\\* Empty cells (--) indicate scores not available or not applicable.\n\n## Build with Qwen3.6-35B-A3B\n\nQwen3.6-35B-A3B is available as open weights on [Hugging Face](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) and [ModelScope](https://modelscope.cn/models/Qwen/Qwen3.6-35B-A3B) for self-hosting, and through the [Alibaba Cloud Model Studio](https://modelstudio.alibabacloud.com/) API as `qwen3.6-flash`. You can also try it instantly on [Qwen Studio](https://chat.qwen.ai).\n\nThe 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\n### API Usage\n\nThis release supports the `preserve_thinking` feature: preserving thinking content from all preceding turns in messages, which is **recommended for agentic tasks**.\n\n#### Alibaba Cloud Model Studio\n\nAlibaba 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\nExample code for chat completions API is provided below:\n\n```python\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\nFor more information, please visit the [API doc](https://modelstudio.console.alibabacloud.com/?tab=doc#/doc/?type=model&url=2840915).\n\n### Coding & Agents\n\nQwen3.6-35B-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\n#### OpenClaw\n\nQwen3.6-35B-A3B is compatible with [OpenClaw](https://openclaw.ai) (formerly Moltbot / Clawdbot), a self-hosted open-source AI coding agent. Connect it to [Model Studio](https://www.alibabacloud.com/help/en/model-studio/openclaw) to get a full agentic coding experience in the terminal. Get started with the following script:\n\n```bash\n# Node.js 22+\ncurl -fsSL https://molt.bot/install.sh | bash # macOS / Linux\n\n# Set your API key\nexport DASHSCOPE_API_KEY=\n\n# Launch OpenClaw\nopenclaw dashboard # web browser\n# openclaw tui # Open a new terminal and start the TUI\n```\n\nOn 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```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\": \"qwen3.6-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\n#### Qwen Code\n\nQwen3.6-35B-A3B is compatible with [Qwen Code](https://qwen.ai/qwencode), an open-source AI agent designed for the terminal and deeply optimized for the Qwen Series. Get started with the following script:\n\n```bash\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\nOn first use, you’ll be prompted to sign in. You can run `/auth` anytime to switch authentication methods.\n\n#### Claude Code\n\nQwen APIs also support the Anthropic API protocol, meaning you can use it with tools like **Claude Code** for elevated coding experience:\n\n```bash\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=\n\n# Launch the CLI\nclaude\n```\n\n## Summary\n\nQwen3.6-35B-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\nLooking 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\n## Citation\n\nFeel free to cite the following article if you find Qwen3.6-35B-A3B helpful:\n\n```bibtex\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", "dateCreated": "2026-10-19T01:26:53.998049939+00:00", "date_created": "2026-04-15T02:00:00+00:00", "date_modified": null, "date_published": "2026-04-15T02:00:00+00:00", "description": "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…", "external_url": "https://qwen.ai/blog?id=qwen3.6-35b-a3b", "git": { "commit": "ebb0941c5c06a2ab2377342d607535262a3e969e", "path": "items/qwen-ai/2026/04/2026-04-15-qwen3-6-35b-a3b-agentic-coding-power-now-open-to-all.md", "permalink": "https://github.com/aymericbeaumet/aggr-instance/blob/ebb0941c5c06a2ab2377342d607535262a3e969e/items/qwen-ai/2026/04/2026-04-15-qwen3-6-35b-a3b-agentic-coding-power-now-open-to-all.md" }, "id": "urn:aggr:item:5f0a510db03908b6d1c8875c8d9a95054a6d4e18", "isBasedOn": { "@id": "https://qwen.ai/blog?id=qwen3.6-35b-a3b" }, "mainEntity": { "@id": "https://qwen.ai/blog?id=qwen3.6-35b-a3b", "@type": "CreativeWork", "archivedAt": { "@id": "https://aggr.aymericbeaumet.com/items/qwen-ai/2026-04-15-qwen3-6-35b-a3b-agentic-coding-power-now-open-to-all/#webpage" }, "author": [ { "@type": "Person", "name": "QwenTeam" } ], "datePublished": "2026-04-15T02:00:00+00:00", "headline": "Qwen3.6-35B-A3B: Agentic Coding Power, Now Open to All", "image": { "@type": "ImageObject", "caption": null, "height": 90, "url": "https://img.alicdn.com/imgextra/i3/O1CN01cL6tG71xn6PgeRuj6_!!6000000006487-2-tps-1590-954.png", "width": 160 }, "timeRequired": "PT8M", "url": "https://qwen.ai/blog?id=qwen3.6-35b-a3b", "wordCount": 1733 }, "name": "Archived snapshot: Qwen3.6-35B-A3B: Agentic Coding Power, Now Open to All", "preview": { "alt": null, "color": null, "height": 90, "placeholder": { "data_url": "data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mNk+A8AAQUBAScY42YAAAAASUVORK5CYII=", "hash": "" }, "url": "https://img.alicdn.com/imgextra/i3/O1CN01cL6tG71xn6PgeRuj6_!!6000000006487-2-tps-1590-954.png", "width": 160 }, "reading_minutes": 8, "replicated_at": null, "source": "qwen-ai", "source_name": "Qwen", "summary": null, "tags": [ "open-source" ], "temporalCoverage": "2026-10-19T01:26:53.998049939+00:00", "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/", "word_count": 1733 }