Kimi K2

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
—
(Agentic Coding) -->
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
—
TerminalBench -->
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.