Kimi-K3 on HuggingFace

1. Model Introduction Kimi K3 is an open-weight, native multimodal agentic model and our most capable model to date. It is a 2.8T-parameter model built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), with native vision capabilities and a 1-million-token context window. It is the world's first open 3T-class model, designed for frontier intelligence across long-horizon coding, knowledge work, and reasoning.

Key Features New Architecture : Kimi K3 is built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), and scales up MoE sparsity with a Stable LatentMoE framework that activates 16 out of 896 experts — yielding an approximate 2.5× improvement in overall scaling efficiency over Kimi K2. Long-Horizon Coding : Operating with minimal human oversight, Kimi K3 sustains long engineering sessions, navigates massive repositories, and orchestrates terminal tools — from GPU kernel optimization and compiler development to vision-in-the-loop game dev, CAD, and even chip design. Agentic Knowledge Work : Kimi K3 advances end-to-end knowledge work, producing deep research with interactive visualizations, widgets and dashboards, and motion design and video editing, powered by its native multimodal architecture. Native Multimodality & Long Context : Kimi K3 understands text, images, and video within the same model, and supports a 1-million-token context window. Open Frontier Weights : We release the full Kimi K3 model weights under the Kimi K3 License, making frontier intelligence openly available for research, deployment, and further innovation. 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 MLA Attention Hidden Dimension 7168 Number of Attention Heads 96 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 MLA Activation Function SiTU-GLU Vision Encoder MoonViT-V2 Parameters of Vision Encoder 401M Quantization MXFP4 weights / MXFP8 activations (quantization-aware training) Modality Text, Image 3. Evaluation Results Benchmark Kimi K3 (max) Claude Fable 5 (max, w/ fallback) GPT-5.6 Sol (max) Claude Opus 4.8 (max) GPT-5.5 (xhigh) GLM-5.2 (max) Reasoning & Knowledge GPQA Diamond 93.5 92.6 94.1 91.0 93.5 91.2 CritPt 23.4 28.6 32.3 20.9 27.1 20.9 AA-LCR 74.7 70.0 73.7 67.7 74.3 71.3 HLE-Full 43.5 / 56.0 53.3 / 63.0 44.5 / 58.0 49.8 / 57.9 41.4 / 52.2 — Coding DeepSWE 67.5 70.0 73.0 59.0 67.0 46.2 ProgramBench 77.8 76.8 77.6 71.9 70.8 63.7 Terminal-Bench 2.1 88.3 88.0 88.8 84.6 83.4 82.7 FrontierSWE 81.2 86.6 71.3 66.7 64.9 67.3 SWE-Marathon 42.0 35.0 39.0 40.0 14.0 13.0 PostTrainBench 36.6 41.4 34.6 34.1 28.4 34.3 MLS-Bench-Lite 48.3 49.9 46.2 42.8 35.5 40.4 SciCode 58.7 60.2 56.1 53.5 56.1 50.5 Kimi Code Bench 2.0 72.9 76.9 64.8 71.7 69.0 64.2 Agentic BrowseComp 91.2 88.0 90.4 84.3 84.4 — DeepSearchQA (F1) 95.0 94.2 — 93.1 — — ResearchRubrics 76.2 — 73.8 73.5 64.0 71.1 GDPval-AA v2 (Elo) 1686 1747 1736 1593 1491 1510 Toolathlon-Verified 76.5 77.9 74.9 76.2 73.5 59.9 MCPMark-Verified 94.5 87.4 92.9 76.4 92.9 — MCP-Atlas 84.2 84.7 83.6 83.6 82.8 82.6 AutomationBench 30.8 29.1 29.7 27.2 22.7 12.9 JobBench 54.3 57.4 45.4 48.4 38.3 43.4 AA-Briefcase (Elo) 1548 1583 1495 1354 1158 1260 Agents' Last Exam 28.3 25.7 † 29.6 27.0 26.6 20.4 APEX-Agents 41.0 43.3 39.9 39.4 38.5 35.6 OfficeQA Pro 63.3 69.9 63.2 63.9 60.9 41.4 SpreadsheetBench 2 34.8 34.7 32.4 31.6 29.1 28.1 OSWorld-Verified 84.8 85.0 83.0 83.4 79.0 — OSWorld 2.0 58.3 66.1 62.6 55.7 49.5 — SaaS-Bench 60.1 — 61.4 56.1 43.8 — τ³-Banking 33.4 26.8 33.0 27.6 31.3 26.8 Harvey Lab-AA 94.6 93.6 87.2 91.1 86.3 91.0 CorpFin v2 71.6 71.8 64.4 66.7 68.4 66.1 Finance Agent v2 54.4 56.3 53.8 53.9 51.8 49.7 Legal Research Bench 44.2 49.5 48.1 43.8 40.4 31.3 Vision WorldVQA ForceAnswer 51.0 56.7 41.8 39.1 38.5 — OmniDocBench 91.1 89.8 85.8 87.9 89.4 — PerceptionBench 58.5 57.2 59.7 47.2 55.8 — Video-MME (w. sub) 90.0 — 89.5 86.0 89.3 — MMVU 82.1 — 81.2 79.2 81.7 — BabyVision w/ python 85.7 90.5 88.9 81.2 83.6 — MMMU-Pro 81.6 / 83.4 81.2 / 86.5 83.0 / 84.6 78.9 / 82.7 81.2 / 83.2 — CharXiv (RQ) 84.8 / 91.3 88.9 / 93.5 84.6 / 89.1 80.5 / 89.9 84.1 / 89.0 — MathVision 94.3 / 97.8 94.8 / 98.6 95.8 / 97.8 86.7 / 97.1 92.2 / 96.8 — ZeroBench (pass@5) 23.0 / 41.0 23.0 / 46.0 17.0 / 35.0 17.0 / 34.0 22.0 / 41.0 — Footnotes All Kimi K3 results are obtained with reasoning effort set to 'max' and temperature = 1.0. For single-step tasks, such as GPQA Diamond, HLE-Full, and vision benchmarks without tools, we set top-p = 0.95; for agentic tasks, we set top-p = 1.0. For HLE-Full, MMMU-Pro, CharXiv (RQ), MathVision, and ZeroBench, each cell reports the scores without and with tool augmentation (general tools for HLE-Full, Python for the vision benchmarks), in that order.

4. Native MXFP4 Quantization Kimi K3 applies quantization-aware training from the SFT stage onward, using MXFP4 weights with MXFP8 activations for broad hardware compatibility.

5. Deployment You can access Kimi K3's API on https://platform.kimi.ai by selecting kimi-k3 , and we provide OpenAI/Anthropic-compatible API for you. Currently, Kimi K3 is recommended to run on the following inference engines:

6. Model Usage Kimi K3 always has thinking enabled, and will return reasoning_content . Thinking effort is configured with the top-level reasoning_effort request field, which supports "low" , "high" , and "max" (default "max" ).

Kimi K3 was trained in the preserved thinking history mode. For multi-turn conversations and tool calls, Kimi K3 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 :

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 Quickstart and Thinking Effort .

Coding Agent Framework Kimi K3 works best with Kimi Code CLI as its agent framework. We warmly invite you to give it a try — run Kimi Code in your terminal and select Kimi K3 using the /model command. We hope you enjoy building with Kimi K3, and we would love to hear your feedback!

7. License Both the code repository and the model weights are released under the Kimi K3 License .

8. Contact Us If you have any questions, please reach out at support@moonshot.ai .

Safetensors Model size 2.8T params Tensor type F32 · BF16 · U8 · Files info