Moonshot AI logo

Kimi K2 Instruct

moonshotai/kimi-k2
Chatother
Moonshot AI|
Function Calling
|Released Jul 2025 · Updated Apr 2026

Kimi K2 Instruct (moonshotai/kimi-k2) is a kimi_k2 1026.4B-parameter model from Moonshot AI with a 131,072-token context window and 32,768 max output tokens, priced at $0.57/1M input and $2.30/1M output tokens. Available via the haimaker.ai OpenAI-compatible API.

Parameters
1.0T
Context Window
131K
tokens
Max Output
33K
tokens
Input Price
$0.57
/1M tokens
Output Price
$2.30
/1M tokens

Overview

Kimi K2 is a chat model by Moonshot AI. It has 1026.4B parameters. It supports a 131K token context window. Supports function calling.

Model Card

Kimi K2: Open Agentic Intellignece

Chat github Homepage
Hugging Face Twitter Follow Discord
License

📰  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.
Evaluation Results

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-EPass@185.783.178.288.689.686.785.6
SWE-bench Verified
(Agentless Coding)
Single Patch w/o Test (Acc)51.836.639.450.253.040.832.6
SWE-bench Verified
(Agentic Coding)
Single Attempt (Acc)65.838.834.472.7*72.5*54.6
Multiple Attempts (Acc)71.680.279.4*
SWE-bench Multilingual
(Agentic Coding)
Single Attempt (Acc)47.3 25.820.951.031.5
TerminalBenchInhouse Framework (Acc)30.035.543.28.3
Terminus (Acc)25.0 16.36.630.316.8
Aider-PolyglotAcc60.055.161.856.470.752.444.0
Tool Use Tasks
Tau2 retailAvg@470.669.157.075.081.874.864.3
Tau2 airlineAvg@456.539.026.555.560.054.542.5
Tau2 telecomAvg@465.832.522.145.257.038.616.9
AceBenchAcc76.572.770.576.275.680.174.5
Math & STEM Tasks
AIME 2024Avg@6469.659.4*40.1*43.448.246.561.3
AIME 2025Avg@6449.546.724.7*33.1*33.9*37.046.6
MATH-500Acc97.494.0*91.2*94.094.492.495.4
HMMT 2025Avg@3238.827.511.915.915.919.434.7
CNMO 2024Avg@1674.374.748.660.457.656.675.0
PolyMath-enAvg@465.159.551.952.849.854.049.9
ZebraLogicAcc89.084.037.7*73.759.358.557.9
AutoLogiAcc89.588.983.389.886.188.284.1
GPQA-DiamondAvg@875.168.4*62.9*70.0*74.9*66.368.2
SuperGPQAAcc57.253.750.255.756.550.849.6
Humanity's Last Exam
(Text Only)
-4.75.25.75.87.13.75.6
General Tasks
MMLUEM89.589.487.091.592.990.490.1
MMLU-ReduxEM92.790.589.293.694.292.490.6
MMLU-ProEM81.181.2*77.383.786.681.879.4
IFEvalPrompt Strict89.881.183.2*87.687.488.084.3
Multi-ChallengeAcc54.131.434.046.849.036.439.5
SimpleQACorrect31.027.713.215.922.842.323.3
LivebenchPass@176.472.467.674.874.669.867.8



• 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:

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.


6. License

Both the code repository and the model weights are released under the Modified MIT License.


7. Third Party Notices

See THIRD PARTY NOTICES


7. Contact Us

If you have any questions, please reach out at support@moonshot.cn.

Features & Capabilities

Modechat
Context Window131,072 tokens
Max Output32,768 tokens
Function CallingSupported
VisionNot supported
ReasoningNot supported
Web SearchNot supported
Url ContextNot supported

Technical Details

ArchitectureDeepseekV3ForCausalLM
Model Typekimi_k2
Librarytransformers

API Usage

from openai import OpenAI

client = OpenAI(
    base_url="https://api.haimaker.ai/v1",
    api_key="YOUR_API_KEY",
)

response = client.chat.completions.create(
    model="moonshotai/kimi-k2",
    messages=[
        {"role": "user", "content": "Hello, how are you?"}
    ],
)

print(response.choices[0].message.content)

Frequently Asked Questions

What is the context window of Kimi K2 Instruct?

Kimi K2 Instruct (moonshotai/kimi-k2) has a 131,072-token context window and supports up to 32,768 output tokens per request.

How much does Kimi K2 Instruct cost?

Kimi K2 Instruct is priced at $0.57 per 1M input tokens and $2.30 per 1M output tokens when accessed via the haimaker.ai OpenAI-compatible API.

What features does Kimi K2 Instruct support?

Kimi K2 Instruct supports function calling.

How do I use Kimi K2 Instruct via API?

Send requests to https://api.haimaker.ai/v1/chat/completions with model "moonshotai/kimi-k2" using any OpenAI-compatible SDK. Authentication uses a Bearer API key from https://app.haimaker.ai.

Use Kimi K2 Instruct with the haimaker API

OpenAI-compatible endpoint. Start building in minutes.

Get API Access

haimaker.ai 2026 - All Rights Reserved