📰 Tech Blog | 📄 Full Report
1. 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.
2. 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 |
3. 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* |
54.6 |
— |
| Multiple Attempts (Acc) |
71.6 |
— |
— |
80.2 |
79.4* |
— |
— |
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* |
40.1* |
43.4 |
48.2 |
46.5 |
61.3 |
| AIME 2025 |
Avg@64 |
49.5 |
46.7 |
24.7* |
33.1* |
33.9* |
37.0 |
46.6 |
| MATH-500 |
Acc |
97.4 |
94.0* |
91.2* |
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* |
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* |
62.9* |
70.0* |
74.9* |
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* |
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 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.
Kimi K2 是 Moonshot AI 推出的大语言模型系列：一个总参数量达 1 万亿的混合专家（MoE）模型（每个 token 激活 32B 参数），为前沿知识、推理、编程尤其是智能体工具调用任务而训练。