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Kimi K2.7 Code is a coding-focused agentic model built upon Kimi K2.6. With substantial improvements on real-world long-horizon coding tasks, it strengthens end-to-end task completion across complex software engineering workflows while improving token efficiency, reducing thinking-token usage by approximately 30% compared with Kimi K2.6.
| 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 | 256K |
| Attention Mechanism | MLA |
| Activation Function | SwiGLU |
| Vision Encoder | MoonViT |
| Parameters of Vision Encoder | 400M |
Kimi-K2.7-Code adopts the same native int4 quantization method as Kimi-K2-Thinking.
[!Note] You can access Kimi-K2.7-Code's API on https://platform.moonshot.ai and we provide OpenAI/Anthropic-compatible API for you. Currently, Kimi-K2.7-Code is recommended to run on the following inference engines:
Kimi-K2.7-Code has the same architecture as Kimi-K2.5/Kimi-K2.6, and the deployment method can be directly reused.
The version requirement for transformers is >=4.57.1, <5.0.0.
Deployment examples can be found in the Model Deployment Guide.
The usage demos below demonstrate how to call our official API. Note that Kimi-K2.7-Code forces thinking and preserve_thinking as True.
For third-party APIs deployed with vLLM or SGLang, please note that:
[!Note]
Chat with video content is an experimental feature and is only supported in our official API for now.
The recommended
temperaturewill be1.0for Thinking mode.The recommended
top_pis0.95.Instant mode is not supported.
This is a simple chat completion script which shows how to call K2.7-Code API in Thinking mode.
python
import openaiimport base64import requestsdef simple_chat(client: openai.OpenAI, model_name: str): messages = [ {'role': 'system', 'content': 'You are Kimi, an AI assistant created by Moonshot AI.'}, { 'role': 'user', 'content': [ {'type': 'text', 'text': 'which one is bigger, 9.11 or 9.9? think carefully.'} ], }, ] response = client.chat.completions.create( model=model_name, messages=messages, stream=False, max_tokens=4096 ) print('====== Below is reasoning content in Thinking Mode ======') print(f'reasoning content: {response.choices[0].message.reasoning}') print('====== Below is response in Thinking Mode ======') print(f'response: {response.choices[0].message.content}')K2.7-Code supports Image and Video input.
The following example demonstrates how to call K2.7-Code API with image input:
python
import openaiimport base64import requests def chat_with_image(client: openai.OpenAI, model_name: str): url = 'https://huggingface.co/moonshotai/Kimi-K2.7-Code/resolve/main/figures/kimi-logo.png' image_base64 = base64.b64encode(requests.get(url).content).decode() messages = [ { 'role': 'user', 'content': [ {'type': 'text', 'text': 'Describe this image in detail.'}, { 'type': 'image_url', 'image_url': {'url': f'data:image/png;base64,{image_base64}'}, }, ], } ] response = client.chat.completions.create( model=model_name, messages=messages, stream=False, max_tokens=8192 ) print('====== Below is reasoning content in Thinking Mode ======') print(f'reasoning content: {response.choices[0].message.reasoning}') print('====== Below is response in Thinking Mode ======') print(f'response: {response.choices[0].message.content}')The following example demonstrates how to call K2.7-Code API with video input:
python
import openaiimport base64import requests def chat_with_video(client: openai.OpenAI, model_name:str): url = 'https://huggingface.co/moonshotai/Kimi-K2.7-Code/resolve/main/figures/demo_video.mp4' video_base64 = base64.b64encode(requests.get(url).content).decode() messages = [ { "role": "user", "content": [ {"type": "text","text": "Describe the video in detail."}, { "type": "video_url", "video_url": {"url": f"data:video/mp4;base64,{video_base64}"}, }, ], } ] response = client.chat.completions.create(model=model_name, messages=messages) print('====== Below is reasoning content in Thinking Mode ======') print(f'reasoning content: {response.choices[0].message.reasoning}') print('====== Below is response in Thinking Mode ======') print(f'response: {response.choices[0].message.content}')Kimi K2.7 Code forces preserve_thinking mode, which retains full reasoning content across multi-turn interactions and enhances performance in coding agent scenarios.
This feature is enabled by default and can't be disabled. The following example demonstrates how to call K2.7-Code API in preserve_thinking mode:
python
def chat_with_preserve_thinking(client: openai.OpenAI, model_name: str): messages = [ { "role": "user", "content": "Tell me three random numbers." }, { "role": "assistant", "reasoning_content": "I'll start by listing five numbers: 473, 921, 235, 215, 222, and I'll tell you the first three.", # Some API (e.g. vLLM) may not support reasoning_content, you can try reasoning instead "content": "473, 921, 235" }, { "role": "user", "content": "What are the other two numbers you have in mind?" } ] response = client.chat.completions.create( model=model_name, messages=messages, stream=False, max_tokens=4096, ) # the assistant should mention 215 and 222 that appear in the prior reasoning content print(f"response: {response.choices[0].message.reasoning}") return response.choices[0].message.contentK2.7-Code shares the same design of Interleaved Thinking and Multi-Step Tool Call as K2 Thinking. For usage example, please refer to the K2 Thinking documentation.
Kimi K2.7-Code works best with Kimi Code CLI as its agent framework — give it a try at https://www.kimi.com/code.
Both the code repository and the model weights are released under the Modified MIT License.
If you have any questions, please reach out at support@moonshot.ai.
import openaiimport base64import requestsdef simple_chat(client: openai.OpenAI, model_name: str): messages = [ {'role': 'system', 'content': 'You are Kimi, an AI assistant created by Moonshot AI.'}, { 'role': 'user', 'content': [ {'type': 'text', 'text': 'which one is bigger, 9.11 or 9.9? think carefully.'} ], }, ] response = client.chat.completions.create( model=model_name, messages=messages, stream=False, max_tokens=4096 ) print('====== Below is reasoning content in Thinking Mode ======') print(f'reasoning content: {response.choices[0].message.reasoning}') print('====== Below is response in Thinking Mode ======') print(f'response: {response.choices[0].message.content}')import openaiimport base64import requests def chat_with_image(client: openai.OpenAI, model_name: str): url = 'https://huggingface.co/moonshotai/Kimi-K2.7-Code/resolve/main/figures/kimi-logo.png' image_base64 = base64.b64encode(requests.get(url).content).decode() messages = [ { 'role': 'user', 'content': [ {'type': 'text', 'text': 'Describe this image in detail.'}, { 'type': 'image_url', 'image_url': {'url': f'data:image/png;base64,{image_base64}'}, }, ], } ] response = client.chat.completions.create( model=model_name, messages=messages, stream=False, max_tokens=8192 ) print('====== Below is reasoning content in Thinking Mode ======') print(f'reasoning content: {response.choices[0].message.reasoning}') print('====== Below is response in Thinking Mode ======') print(f'response: {response.choices[0].message.content}')import openaiimport base64import requests def chat_with_video(client: openai.OpenAI, model_name:str): url = 'https://huggingface.co/moonshotai/Kimi-K2.7-Code/resolve/main/figures/demo_video.mp4' video_base64 = base64.b64encode(requests.get(url).content).decode() messages = [ { "role": "user", "content": [ {"type": "text","text": "Describe the video in detail."}, { "type": "video_url", "video_url": {"url": f"data:video/mp4;base64,{video_base64}"}, }, ], } ] response = client.chat.completions.create(model=model_name, messages=messages) print('====== Below is reasoning content in Thinking Mode ======') print(f'reasoning content: {response.choices[0].message.reasoning}') print('====== Below is response in Thinking Mode ======') print(f'response: {response.choices[0].message.content}')def chat_with_preserve_thinking(client: openai.OpenAI, model_name: str): messages = [ { "role": "user", "content": "Tell me three random numbers." }, { "role": "assistant", "reasoning_content": "I'll start by listing five numbers: 473, 921, 235, 215, 222, and I'll tell you the first three.", # Some API (e.g. vLLM) may not support reasoning_content, you can try reasoning instead "content": "473, 921, 235" }, { "role": "user", "content": "What are the other two numbers you have in mind?" } ] response = client.chat.completions.create( model=model_name, messages=messages, stream=False, max_tokens=4096, ) # the assistant should mention 215 and 222 that appear in the prior reasoning content print(f"response: {response.choices[0].message.reasoning}") return response.choices[0].message.content