Всем, кому интересен ИИ — срочная новость! 🚀 В открытом доступе появилась модель Kimi k2, которую разработчики называют SOTA (State Of The Art). Это значит, что она может превзойти топовые нейросети в решении сложных задач. Хотя подробностей нет, ходят слухи об огромном наборе данных и прорывной архитектуре. 💡 Главный плюс? Модель открытая: качаешь, тюнишь, внедряешь без ограничений. Если всё правда — это глоток свободы для стартапов и гиков! 🤯 Следим за развитием.
📰 Tech Blog | 📄 Paper Link (coming soon)
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- Model Introduction
- Kimi K2 is a state-of-the-art mixture-of-experts (MoE) language model with 32 billion activated parameters and 1 trillion total parameters. Trained with the Muon optimizer, Kimi K2 achieves exceptional performance across frontier knowledge, reasoning, and coding tasks while being meticulously optimized for agentic capabilities.
- Key Features
- Large-Scale Training: Pre-trained a 1T parameter MoE model on 15.5T tokens with zero training instability.
- MuonClip Optimizer: We apply the Muon optimizer to an unprecedented scale, and develop novel optimization techniques to resolve instabilities while scaling up.
- Agentic Intelligence: Specifically designed for tool use, reasoning, and autonomous problem-solving.
- Model Variants
- Kimi-K2-Base
- The foundation model, a strong start for researchers and builders who want full control for fine-tuning and custom solutions. Kimi-K2-Instruct
- The post-trained model best for drop-in, general-purpose chat and agentic experiences. It is a reflex-grade model without long thinking.
- Model Summary Architecture Mixture-of-Experts (MoE) Total Parameters 1T Activated Parameters 32B Number of Layers (Dense layer included) 61 Number of Dense Layers 1 Attention Hidden Dimension 7168 MoE Hidden Dimension (per Expert) 2048 Number of Attention Heads 64 Number of Experts 384 Selected Experts per Token 8 Number of Shared Experts 1 Vocabulary Size 160K Context Length 128K Attention Mechanism MLA Activation Function SwiGLU
- Evaluation Results Instruction model evaluation results Benchmark Metric Kimi K2 Instruct DeepSeek-V3-0324 Qwen3-235B-A22B (non-thinking) Claude Sonnet 4 (w/o extended thinking) Claude Opus 4 (w/o extended thinking) GPT-4.1 Gemini 2.5 Flash Preview (05-20) Coding Tasks LiveCodeBench v6 (Aug 24 — May 25) Pass@1 53.7 46.9 37.0 48.5 47.4 44.7 44.7 OJBench Pass@1 27.1 24.0 11.3 15.3 19.6 19.5 19.5 MultiPL-E Pass@1 85.7 83.1 78.2 88.6 89.6 86.7 85.6 SWE-bench Verified (Agentless Coding) Single Patch w/o Test (Acc) 51.8 36.6 39.4 50.2 53.0 40.8 32.6 SWE-bench Verified (Agentic Coding) Single Attempt (Acc) 65.8 38.8 34.4 72.7
- 72.5
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54.6 — Multiple Attempts (Acc) 71.6 — — 80.2 79.4
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— — SWE-bench Multilingual (Agentic Coding) Single Attempt (Acc) 47.3 25.8 20.9 51.0 — 31.5 — TerminalBench Inhouse Framework (Acc) 30.0 — — 35.5 43.2 8.3 — Terminus (Acc) 25.0 16.3 6.6 — — 30.3 16.8 Aider-Polyglot Acc 60.0 55.1 61.8 56.4 70.7 52.4 44.0 Tool Use Tasks Tau2 retail Avg@4 70.6 69.1 57.0 75.0 81.8 74.8 64.3 Tau2 airline Avg@4 56.5 39.0 26.5 55.5 60.0 54.5 42.5 Tau2 telecom Avg@4 65.8 32.5 22.1 45.2 57.0 38.6 16.9 AceBench Acc 76.5 72.7 70.5 76.2 75.6 80.1 74.5 Math & STEM Tasks AIME 2024 Avg@64 69.6 59.4
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40.1
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43.4 48.2 46.5 61.3 AIME 2025 Avg@64 49.5 46.7 24.7
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33.1
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33.9
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37.0 46.6 MATH-500 Acc 97.4 94.0
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91.2
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94.0 94.4 92.4 95.4 HMMT 2025 Avg@32 38.8 27.5 11.9 15.9 15.9 19.4 34.7 CNMO 2024 Avg@16 74.3 74.7 48.6 60.4 57.6 56.6 75.0 PolyMath-en Avg@4 65.1 59.5 51.9 52.8 49.8 54.0 49.9 ZebraLogic Acc 89.0 84.0 37.7
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73.7 59.3 58.5 57.9 AutoLogi Acc 89.5 88.9 83.3 89.8 86.1 88.2 84.1 GPQA-Diamond Avg@8 75.1 68.4
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62.9
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70.0
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74.9
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66.3 68.2 SuperGPQA Acc 57.2 53.7 50.2 55.7 56.5 50.8 49.6 Humanity’s Last Exam (Text Only)
- 4.7 5.2 5.7 5.8 7.1 3.7 5.6 General Tasks MMLU EM 89.5 89.4 87.0 91.5 92.9 90.4 90.1 MMLU-Redux EM 92.7 90.5 89.2 93.6 94.2 92.4 90.6 MMLU-Pro EM 81.1 81.2
- 77.3 83.7 86.6 81.8 79.4 IFEval Prompt Strict 89.8 81.1 83.2
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87.6 87.4 88.0 84.3 Multi-Challenge Acc 54.1 31.4 34.0 46.8 49.0 36.4 39.5 SimpleQA Correct 31.0 27.7 13.2 15.9 22.8 42.3 23.3 Livebench Pass@1 76.4 72.4 67.6 74.8 74.6 69.8 67.8 • Bold denotes global SOTA, and underlined denotes open-source SOTA. • Data points marked with * are taken directly from the model’s tech report or blog. • All metrics, except for SWE-bench Verified (Agentless), are evaluated with an 8k output token length. SWE-bench Verified (Agentless) is limited to a 16k output token length. • Kimi K2 achieves 65.8% pass@1 on the SWE-bench Verified tests with bash/editor tools (single-attempt patches, no test-time compute). It also achieves a 47.3% pass@1 on the SWE-bench Multilingual tests under the same conditions. Additionally, we report results on SWE-bench Verified tests (71.6%) that leverage parallel test-time compute by sampling multiple sequences and selecting the single best via an internal scoring model. • To ensure the stability of the evaluation, we employed avg@k on the AIME, HMMT, CNMO, PolyMath-en, GPQA-Diamond, EvalPlus, Tau2. • Some data points have been omitted due to prohibitively expensive evaluation costs. Base model evaluation results Benchmark Metric Shot Kimi K2 Base Deepseek-V3-Base Qwen2.5-72B Llama 4 Maverick General Tasks MMLU EM 5-shot 87.8 87.1 86.1 84.9 MMLU-pro EM 5-shot 69.2 60.6 62.8 63.5 MMLU-redux-2.0 EM 5-shot 90.2 89.5 87.8 88.2 SimpleQA Correct 5-shot 35.3 26.5 10.3 23.7 TriviaQA EM 5-shot 85.1 84.1 76.0 79.3 GPQA-Diamond Avg@8 5-shot 48.1 50.5 40.8 49.4 SuperGPQA EM 5-shot 44.7 39.2 34.2 38.8 Coding Tasks LiveCodeBench v6 Pass@1 1-shot 26.3 22.9 21.1 25.1 EvalPlus Pass@1
- 80.3 65.6 66.0 65.5 Mathematics Tasks MATH EM 4-shot 70.2 60.1 61.0 63.0 GSM8k EM 8-shot 92.1 91.7 90.4 86.3 Chinese Tasks C-Eval EM 5-shot 92.5 90.0 90.9 80.9 CSimpleQA Correct 5-shot 77.6 72.1 50.5 53.5 • We only evaluate open-source pretrained models in this work. We report results for Qwen2.5-72B because the base checkpoint for Qwen3-235B-A22B was not open-sourced at the time of our study. • All models are evaluated using the same evaluation protocol.
- Deployment Note You can access Kimi K2’s API on https://platform.moonshot.ai , we provide OpenAI/Anthropic-compatible API for you. The Anthropic-compatible API maps temperature by real_temperature = request_temperature * 0.6 for better compatiblity with existing applications. Our model checkpoints are stored in the block-fp8 format, you can find it on Huggingface . Currently, Kimi-K2 is recommended to run on the following inference engines: vLLM SGLang KTransformers TensorRT-LLM Deployment examples for vLLM and SGLang can be found in the Model Deployment Guide .
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- Model Usage
- Chat Completion
- Once the local inference service is up, you can interact with it through the chat endpoint:
- def
- simple_chat
- (
- client
- OpenAI , model_name
- str ): messages = [ { «role»
- «system» , «content»
- «You are Kimi, an AI assistant created by Moonshot AI.» }, { «role»
- «user» , «content»
- [{ «type»
- «text» , «text»
- «Please give a brief self-introduction.» }]}, ] response = client . chat . completions . create ( model = model_name , messages = messages , stream = False , temperature = 0.6 , max_tokens = 256 ) print ( response . choices [ 0 ]. message . content ) Note The recommended temperature for Kimi-K2-Instruct is temperature = 0.6 . If no special instructions are required, the system prompt above is a good default. Tool Calling Kimi-K2-Instruct has strong tool-calling capabilities. To enable them, you need to pass the list of available tools in each request, then the model will autonomously decide when and how to invoke them. The following example demonstrates calling a weather tool end-to-end: # Your tool implementation def get_weather ( city
- str ) -> dict
- return { «weather»
- «Sunny» } # Tool schema definition tools = [{ «type»
- «function» , «function»
- { «name»
- «get_weather» , «description»
- «Retrieve current weather information. Call this when the user asks about the weather.» , «parameters»
- { «type»
- «object» , «required»
- [ «city» ], «properties»
- { «city»
- { «type»
- «string» , «description»
- «Name of the city» } } } } }] # Map tool names to their implementations tool_map = { «get_weather»
- get_weather } def tool_call_with_client ( client
- OpenAI , model_name
- str ): messages = [ { «role»
- «system» , «content»
- «You are Kimi, an AI assistant created by Moonshot AI.» }, { «role»
- «user» , «content»
- «What’s the weather like in Beijing today? Use the tool to check.» } ] finish_reason = None while finish_reason is None or finish_reason == «tool_calls»
- completion = client . chat . completions . create ( model = model_name , messages = messages , temperature = 0.6 , tools = tools , # tool list defined above tool_choice = «auto» ) choice = completion . choices [ 0 ] finish_reason = choice . finish_reason if finish_reason == «tool_calls»
- messages . append ( choice . message ) for tool_call in choice . message . tool_calls
- tool_call_name = tool_call . function . name tool_call_arguments = json . loads ( tool_call . function . arguments ) tool_function = tool_map [ tool_call_name ] tool_result = tool_function ( ** tool_call_arguments ) print ( «tool_result:» , tool_result ) messages . append ({ «role»
- «tool» , «tool_call_id»
- tool_call . id , «name»
- tool_call_name , «content»
- json . dumps ( tool_result ) }) print ( «-«
- 100 ) print ( choice . message . content ) The tool_call_with_client function implements the pipeline from user query to tool execution. This pipeline requires the inference engine to support Kimi-K2’s native tool-parsing logic. For streaming output and manual tool-parsing, see the Tool Calling Guide .
- License Both the code repository and the model weights are released under the Modified MIT License .
- Contact Us If you have any questions, please reach out at support@moonshot.cn .
