--- title: Kimi K3.1 Pro model weights are out link: https://huggingface.co/moonshotai/Kimi-K3.1-Pro source: hnrss-org published: 2026-11-04T15:30:30Z updated: 2026-11-04T15:30:30Z first_seen: 2026-11-04T21:49:17.908350494Z authors: - abrarthought content: extracted html: 2026-11-04-kimi-k3-1-pro-model-weights-are-out.html remote_preview: url: https://cdn-thumbnails.huggingface.co/social-thumbnails/models/moonshotai/Kimi-K3.1-Pro.png --- ![Kimi K3.1 Pro](https://huggingface.co/moonshotai/Kimi-K3.1-Pro/resolve/main/assets/kimi-logo.png) [![Chat](https://img.shields.io/badge/Chat-Kimi%20K3.1%20Pro-1783ff)](https://www.kimi.ai) [![Homepage](https://img.shields.io/badge/Homepage-Moonshot%20AI-white?logo=Kimi&logoColor=white)](https://www.moonshot.ai) [![Hugging Face](https://img.shields.io/badge/Hugging%20Face-Moonshot%20AI-ffc107)](https://huggingface.co/moonshotai) [![X](https://img.shields.io/badge/X-Kimi.ai-white?logo=x&logoColor=white)](https://twitter.com/kimi_moonshot) [![Discord](https://img.shields.io/badge/Discord-Kimi.ai-white?logo=discord&logoColor=white)](https://discord.gg/TYU2fdJykW) [![ModelScope](https://img.shields.io/badge/ModelScope-Moonshot%20AI-white?labelColor=rgb\(99%2C%2074%2C%255\))](https://modelscope.cn/organization/moonshotai) [![License](https://img.shields.io/badge/License-Kimi_K3.1_Pro-f5de53?&color=f5de53)](https://huggingface.co/moonshotai/Kimi-K3.1-Pro/blob/main/LICENSE) 📰  [Tech Blog](https://www.kimi.ai/blog/kimi-k3-1-pro) |     **🚀  [API Quickstart](https://platform.kimi.ai/docs/guide/kimi-k3-1-pro-quickstart)** ## 1. Model Introduction Kimi K3.1 Pro is an open-weight, natively multimodal agentic model, and our most capable 3T-class model to date. Post-trained from Kimi K3 with large-scale joint reinforcement learning across hundreds of downstream domains, it is the first model in the K3.1 series. With a 1-million-token context window, Kimi K3.1 Pro delivers substantial improvements in coding, 3D creation, financial research, factual accuracy, and other agentic capabilities. ## 2. Model Summary | | | | ------------------------------------- | --------------------------------------------------------------- | | **Architecture** | Mixture-of-Experts (MoE) | | **Total Parameters** | 2.8T | | **Activated Parameters** | 104B | | **Number of Layers** | 93 | | **Number of Dense Layers** | 1 | | **Attention-Layer Composition** | 69 KDA + 24 Gated DSA\* | | **Attention Hidden Dimension** | 7168 | | **Number of Attention Heads** | 96 | | **DSA Index Head Dimension** | 128 | | **DSA Index Number of Heads** | 32 | | **DSA Index Top-K** | 2048 | | **Latent MoE Dimension** | 3584 | | **MoE Hidden Dimension** (per Expert) | 3072 | | **Number of Experts** | 896 | | **Selected Experts per Token** | 16 | | **Number of Shared Experts** | 2 | | **Vocabulary Size** | 160K | | **Context Length** | 1048576 | | **Attention Mechanism** | KDA & Gated DSA | | **Activation Function** | SiTU-GLU | | **Vision Encoder** | MoonViT-V2 | | **Parameters of Vision Encoder** | 401M | | **Quantization** | MXFP4 weights / MXFP8 activations (quantization-aware training) | | **Modality** | Text, Image | \* Compared with Kimi K3, we replace the full-attention Gated MLA layers with Gated DeepSeek Sparse Attention (Gated DSA) layers for more efficient long-context modeling. ## 3. Evaluation Results Unless otherwise specified, all Kimi K3.1 Pro results use reasoning effort `max`, `temperature = 1.0`, and `top-p = 0.95`. A dash (`—`) means that the source table does not report a score. Higher scores are better except where the benchmark name explicitly states `lower is better`; Elo scores and other benchmark-specific metrics are not directly comparable with accuracy or pass-rate scores. | Benchmark | Kimi K3.1 Pro (max) | Claude Fable 5.1 (max, w/ fallback) | GPT-6 Astra (max, w/ fallback) | Claude Opus 5.5 (max, w/ fallback) | DeepSeek-V4.1-Flash-1017 (max) | GLM-5.3 (max) | | ---------------------------------------------------------- | ------------------- | ----------------------------------- | ------------------------------ | ---------------------------------- | ------------------------------ | ------------- | | **Coding** | | | | | | | | Terminal-Bench 4.0 | 65.7 | 57.9 | 58.2 | 66.4 | 42.3 | 41.8 | | Terminal-Bench-Science 0.1 | 38.1 | 40.0 | 68.1 | 63.3 | 15.7 | 8.1 | | SWE Marathon v1.1 | 51.7 | 45.6 | 42.5 | 46.3 | 40.0 | 42.5 | | BenchCAD w/o tools | 72.8 | 60.6 | — | 73.0 | 41.2 | — | | BenchCAD w/ tools | 92.7 | 92.6 | 95.9 | 96.2 | 81.6 | — | | Program Bench | 31.5 | 53.5 | 50.0 | 65.0 | 29.1 | 19.0 | | DeepSWE v1.1 | 75.5 | 67.4 | 74.1 | 74.2 | 77.0 | 66.9 | | PostTrain Bench v1.2 | 36.5 | 44.6 | 41.9 | 43.8 | 30.5 | 34.5 | | Kimi Code Bench 2.0 (Internal) | 77.1 | — | — | — | 76.1 | 61.7 | | Kimi Code Bench 3.0 Long-horizon (Internal) | 61.2 | 58.8 | 66.5 | 58.5 | 51.5 | 45.5 | | **Agentic** | | | | | | | | HLE-Full w/ tools | 70.0 | 65.6 | 57.2 | 67.7 | 58.8 | 62.5 | | BrowseComp | 92.1 | — | 91.5 | 90.8 | — | — | | BioMysteryBench | 80.6 | 81.6 | 78.9 | 81.9 | 80.0 | 79.6 | | CharXiv (RQ) w/ python | 93.4 | 92.7 | 92.8 | 93.8 | 85.6 | — | | MMMU-Pro w/ python | 90.0 | 88.4 | 87.8 | 89.8 | 82.5 | — | | GDP.pdf w/ python | 85.7 | 83.8 | 85.5 | 83.9 | 73.0 | — | | ChartoGraphy w/ python | 90.2 | 88.4 | 91.0 | 89.0 | 83.0 | — | | BabyVision w/ python | 95.4 | 95.1 | 96.6 | 97.7 | 92.5 | — | | ZeroBench\_main w/ python (pass@5) | 63.0 | 59.0 | 58.0 | 61.0 | 51.0 | — | | Agents' Last Exam (ALL) | 43.2 | — | 41.8 | 42.6 | 37.7 | 26.2 | | Agents' Last Exam (CLI) | 44.2 | — | 44.2 | 40.7 | 36.0 | 29.4 | | Automation Bench-AA | 73.3 | 59.4 | 68.5 | 69.5 | 66.6 | 62.2 | | GDPval-AA v2.1 (Elo-score) | 1846 | 1754 | 1573 | 1866 | 1746 | 1650 | | AA-Briefcase v1.1 (Elo-score) | 1678 | 1672 | 1569 | 1803 | 1570 | 1507 | | APEX-Agents | 69.4 | 68.6 | 64.7 | 73.5 | 69.8 | 56.6 | | OfficeQA Pro v2 | 64.4 | 54.5 | 54.4 | 62.2 | 40.4 | 31.1 | | OfficeQA Pro | 67.4 | 68.6 | 66.2 | — | 62.4 | 58.6 | | Frontier Finance | 58.6 | 60.8 | 45.0 | 56.7 | 54.5 | 57.5 | | Harvey Legal Agent | 23.7 | 19.1 | — | — | 21.0 | — | | HANDBOOK.md | 44.6 | 38.8 | — | 41.5 | 28.9 | 20.8 | | OSWorld V2.1 | 79.6 | 80.7 | 75.8 | 81.8 | 69.3 | — | | Business Operation (Internal) | 37.8 | 30.7 | 32.3 | — | 8.9 | 18.6 | | DECK-Arena (Internal, Elo-score) | 1456 | — | 1423 | 1253 | 1231 | 1119 | | Design Bench (Internal, Elo-score) | 957 | — | 1076 | 927 | 863 | 718 | | Kimi Hallu Bench Hard w/ tools (Internal, lower is better) | 8.3 | 23.3 | 5.5 | 15.0 | 23.3 | 22.6 | | **Reasoning & Knowledge & Multimodal** | | | | | | | | GDP.pdf | 34.6 | 27.6 | 34.2 | 30.6 | 17.2 | 11.2 | | HLE-Full | 51.1 | 57.6 | 53.6 | 59.8 | 42.6 | 43.0\* | | HLE-Diamond | 39.4 | 55.1 | 65.8 | 60.9 | 21.6 | 25.1\* | | GPQA-Diamond | 94.2 | 93.7 | 96.1 | 93.9 | 92.2 | 91.7 | | CharXiv (RQ) | 89.0 | 88.8 | 90.0 | 92.1 | 77.0 | — | | MMMU-Pro | 84.1 | 86.3 | 86.9 | 87.7 | 80.6 | — | | ZeroBench\_main (avg@5 / pass@5) | 29.8 / 39.0 | 23.0 / 35.0 | 43.2 / 52.0 | 35.0 / 43.0 | 11.8 / 23.0 | — | | PerceptionBench | 68.3 | 63.0 | 77.6 | 72.1 | 47.7 | — | | Kimi Hallu Bench w/o tools (Internal, lower is better) | 5.4 | 11.4 | 4.2 | 6.8 | 13.1 | 14.8 | | **Cyber Security** | | | | | | | | ExploitBench | 74.7 | 83.0 | 100 | 91.0 | — | 54.4 | | ExploitGym | 20.7 | 30.4 | 42.4 | 34.5 | — | 15.0 | | xdev-bench-hard (Internal) | 81.0 | — | — | — | 85.7 | 57.1 | **Evaluation Footnotes** #### Coding benchmarks 1. **Terminal-Bench 4.0.** Kimi K3.1 Pro is evaluated with the Claude Code harness. The DeepSeek-V4.1-Flash-1017 score is taken from its [release blog](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017). All remaining scores are the maximum values reported in model blog posts or on the [official Terminal-Bench leaderboard](https://www.tbench.ai/). 2. **Terminal-Bench-Science 0.1.** Kimi K3.1 Pro is evaluated with Claude Code. Internal evaluation of DeepSeek-V4.1-Flash-1017 yields 9.3; the table instead reports 15.7 for the older DeepSeek-V4.1-Flash from the official leaderboard. All remaining scores are from the [Terminal-Bench-Science leaderboard](https://www.terminal-bench-science.ai/). 3. **SWE-Marathon v1.1.** Kimi K3.1 Pro and DeepSeek-V4.1-Flash-1017 are evaluated with Claude Code. Other scores are from the [official leaderboard](https://www.swe-marathon.org/). 4. **ProgramBench.** The table reports binary scores: a task receives 1 if its ProgramBench score is at least 95%, and 0 otherwise. K3.1 Pro is evaluated with Claude Code. DeepSeek and GLM scores are taken from the [DeepSeek release blog](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017); remaining scores are from [Vals AI](https://www.vals.ai/benchmarks/programbench). 5. **DeepSWE v1.1.** K3.1 Pro is evaluated with Claude Code. Other scores come from the respective technical reports for [GPT-6 Astra](https://openai.com/index/gpt-6-astra/), [Claude Opus 5.5](https://www.anthropic.com/claude-opus-5-5-system-card), [Claude Fable 5.1](https://www.anthropic.com/claude-fable-5-1-mythos-5-1-system-card), [DeepSeek-V4.1-Flash-1017](https://api-docs.deepseek.com/news/news261017/), and [GLM-5.3](https://z.ai/blog/glm-5.3). 6. **PostTrainBench.** K3.1 Pro and DeepSeek-V4.1-Flash-1017 are evaluated internally on v1.2 with Claude Code and H20 GPUs rather than H100 GPUs. For stable evaluation, all judge models use DeepSeek-V4.1-Flash-1017 at maximum effort, which best matched the official GPT-5.6-Terra (xhigh) judge in testing. Other scores are from the [official leaderboard](https://posttrainbench.com/). 7. **Kimi Code Bench 2.0 and Kimi Code Bench 3.0 Long-horizon.** These are internal benchmarks. K3.1 Pro is evaluated with Claude Code; all other models use their maximum-effort setting. Proprietary-model testing may involve fallback behavior. 8. **BenchCAD.** Open models are evaluated on 1,000-case subsets randomly sampled from the 17,900-case Vision2Code dataset at maximum reasoning effort. With-tools results use Claude Code; without-tools results use single-shot generation with official prompts and scoring. Claude scores are from the Opus 5.5 System Card, and GPT-6 Astra's with-tools score is from its official report. GPT-6 Astra has no reported without-tools score. #### Agentic benchmarks 1. **BioMysteryBench.** Uses the [official v11 release](https://huggingface.co/datasets/Anthropic/BioMysteryBench-full/commit/b5a889c), with Kimi K3 as judge. K3.1 Pro uses Kimi Code; GLM-5.3 and DeepSeek-V4.1-Flash-1017 use Claude Code; GPT-6 Astra uses Codex. Claude scores are from official system cards. 2. **Agents' Last Exam.** Uses the [official v1.1 release](https://github.com/rdi-berkeley/agents-last-exam), comprising 122 tasks, including 86 CLI tasks. Runs that bypass stated requirements receive zero. DeepSeek and Kimi are evaluated with Claude Code and Kimi Code, respectively; other results are from the official benchmark website. 3. **OfficeQA Pro and OfficeQA Pro v2.** GPT-6 Astra uses Codex; other models use Claude Code. Each task provides the complete PDF corpus as page images without machine-readable text. External-model trajectories that retrieve reference answers online receive zero for those tasks. 4. **Frontier Finance.** Evaluated with the official Finance Agent v2 harness. 5. **HANDBOOK.md.** Evaluated with the official OpenHands-based harness, using tasks from the benchmark's early open-source v1 release. 6. **Harvey Legal Agent.** Evaluated on 1,251 public tasks with Artificial Analysis's Stirrup harness. The reported score is the all-pass rate: the percentage of tasks passing every rubric criterion. 7. **OSWorld V2.1.** Kimi models use the Kimi Agent harness in the official OSWorld v2 repository. GPT-6 Astra's score is taken from Muse Wonder's technical report. 8. **DECK-Arena and Design Bench.** DECK-Arena evaluates presentation generation on real-world queries; Design Bench covers graphic design through direct and reference-image few-shot prompting. Both evaluate python-pptx, pptxjs, and Kimi's in-house DSL in a harness with integrated search and image generation. The automated grader is calibrated on more than 10,000 human judgments. 9. **BrowseComp.** Uses context compaction triggered at 200K tokens and a 500-step maximum. If the limit is reached, the agent is forced to answer before judging. All runs use `temperature = 1.0` and `top-p = 1.0`. 10. **GDPval-AA v2.1, AA-Briefcase v1.1, and AutomationBench-AA.** Scores are cited from [Artificial Analysis](https://artificialanalysis.ai/). **APEX-Agents.** Scores are cited from the [official leaderboard](https://www.mercor.com/apex/apex-agents-leaderboard/). 11. **Tool-augmented multimodal evaluations.** GDP.pdf w/ python, ZeroBench w/ python, and ChartoGraphy w/ python are averaged over five runs; other multimodal benchmarks with Python are averaged over four. For GDP.pdf w/ python, each original PDF is sent to a sandbox for IPython processing. Responses are graded against each rubric criterion using Gemini-3.5-Flash at medium reasoning effort and the official judging prompt. The reported score is the task-macro mean rubric pass rate. #### Reasoning, knowledge, and multimodal benchmarks 1. **GDP.pdf.** Each PDF is provided as page images resized to a longest edge of 1,152 pixels, interleaved with Docling-extracted text in page order. Responses are graded against each rubric criterion using Gemini-3.5-Flash Medium and the official judging prompt. The table reports the strict pass rate among fully scored responses. GLM-5.3 Max and DeepSeek-V4.1-Flash-1017 Max scores are cited from [Artificial Analysis](https://artificialanalysis.ai/). 2. **HLE-Full and HLE-Diamond.** The full evaluation sets contain 2,500 and 1,000 questions, respectively. For scores marked `*`, accuracy is reported on the text-only subset. 3. **Run counts.** GDP.pdf, ZeroBench, and ChartoGraphy are averaged over five runs; other multimodal benchmarks are averaged over four. 4. **MMMU-Pro.** Evaluated using the official protocol, preserving the original input order and prepending images to the text input. 5. **PerceptionBench.** An in-house benchmark focused on atomic visual perception capabilities. 6. **Kimi Hallucination Benchmark.** Evaluates factual accuracy on an internal set of challenging, de-identified prompts drawn from practical work tasks. The table reports claim-level hallucination rates assessed by fact-checking agents. #### Cybersecurity benchmarks 1. **ExploitBench.** K3.1 Pro is evaluated with the official harness under a 300-turn budget. The table reports the pass@5 maximum-capability score: for each environment, the best of five seeds is scored by capability flags reached out of 16; arbitrary code execution counts as all 16 capabilities. Comparison scores are from [OpenAI](https://openai.com/index/gpt-6-astra/), [Anthropic](https://www.anthropic.com/claude-opus-5-5-system-card), and [GLM-5.3](https://z.ai/blog/glm-5.3). 2. **ExploitGym.** K3.1 Pro is evaluated with Kimi Code under a six-hour wall-clock budget per task. GPT-6 Astra, Claude, and GLM comparison scores are taken from their respective official reports, with Claude results using the same six-hour budget. 3. **xdev-bench-hard.** K3.1 Pro is evaluated with Kimi Code; GLM-5.3 and DeepSeek-V4.1-Flash-1017 use Claude Code. The internal benchmark contains expert-curated, source-code-based vulnerability discovery and exploitation tasks. The final model is evaluated at pass@3 over a random subset of runs, without hard timeouts or output-token limits. ## 4. Deployment and Availability > You can access Kimi K3.1 Pro's API on [https://platform.kimi.ai](https://platform.kimi.ai) by selecting `kimi-k3.1-pro`, and we provide OpenAI/Anthropic-compatible API for you. Currently, Kimi K3.1 Pro is recommended to run on the following inference engines: - [vLLM](https://github.com/vllm-project/vllm) — see [recipes](https://recipes.vllm.ai/moonshotai/Kimi-K3.1-Pro) - [SGLang](https://github.com/sgl-project/sglang) — see [cookbook](https://docs.sglang.io/cookbook/autoregressive/Moonshotai/Kimi-K3.1-Pro) - [TokenSpeed](https://github.com/lightseekorg/tokenspeed) — see [recipes](https://lightseek.org/tokenspeed/recipes/models#kimi-k3-1-pro) * * * ## 5. Model Usage The thinking effort of Kimi K3.1 Pro is configured with the top-level `reasoning_effort` request field, which supports `"low"`, `"high"`, and `"max"` (default `"max"`). For multi-turn conversations and tool calls, Kimi K3.1 Pro requires the complete assistant message returned by the API to be passed back to `messages` as-is — including `reasoning_content` and `tool_calls`, not just `content`: ```python import openai def chat_with_preserved_thinking(client: openai.OpenAI, model_name: str): messages = [ { "role": "user", "content": "Tell me three random numbers." }, { "role": "assistant", "reasoning_content": "I'll start by listing five numbers: 473, 921, 235, 215, 222, and I'll tell you the first three.", "content": "473, 921, 235" }, { "role": "user", "content": "What are the other two numbers you have in mind?" } ] response = client.chat.completions.create( model=model_name, messages=messages, stream=False, max_tokens=4096, reasoning_effort="max", ) # the assistant should mention 215 and 222 that appear in the prior reasoning content print(f"response: {response.choices[0].message.reasoning}") return response.choices[0].message.content ``` For full guides and examples (vision input, structured output, partial mode, tool choice, dynamic tool loading, context caching), see the [Kimi K3.1 Pro Quickstart](https://platform.kimi.ai/docs/guide/kimi-k3-1-pro-quickstart) and [Thinking Effort](https://platform.kimi.ai/docs/guide/use-thinking-effort). ## 6. License Kimi K3.1 Pro is released under the [Kimi K3.1 Pro License](https://huggingface.co/moonshotai/Kimi-K3.1-Pro/blob/main/LICENSE). ## 7. Contact Us If you have any questions, please reach out at [support@moonshot.ai](mailto:support@moonshot.ai). Downloads last month 797 Safetensors ## Model tree for moonshotai/Kimi-K3.1-Pro ## Space using moonshotai/Kimi-K3.1-Pro 1