0. Changelog
2025.8.11
- Messages with
namefield 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
Base model evaluation results
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 * 0.6for 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
7. Contact Us
If you have any questions, please reach out at support@moonshot.cn.