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Kimi-K2-Instruct

Name: Kimi-K2-Instruct
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📰 Tech Blog | 📄 Paper

0. Changelog

2025.8.11

Messages with name field are now supported. We’ve also moved the chat template to a standalone file for easier viewing.

2025.7.18

We further modified our chat template to improve its robustness. The default system prompt has also been updated.

2025.7.15

We have updated our tokenizer implementation. Now special tokens like [EOS] can be encoded to their token ids.
We fixed a bug in the chat template that was breaking multi-turn tool calls.

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 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.}

4. 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 compatible 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.

5. Model Usage

Chat Completion

Once the local inference service is up, you can interact with it through the chat endpoint:

python

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: