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SergeyKlevan/
Kimi-K2.7-Code-GGUF

Name: Kimi-K2.7-Code-GGUF
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moonshotai/Kimi-K2.7-Code

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Read our How to Run Kimi K2.7 Code Guide!

See Unsloth Dynamic 2.0 GGUFs for our quantization benchmarks.

To run Kimi K2.7 Code in full precision lossless, run Q8 (UD-Q8_K_XL), which is 595GB and only 10GB bigger than Q4 (UD-Q4_K_XL).
See our Kimi K2.7 Code guide for quantization analysis and instructions.

Kimi-K2.7-Code

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1. Model Introduction

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.

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	256K
Attention Mechanism	MLA
Activation Function	SwiGLU
Vision Encoder	MoonViT
Parameters of Vision Encoder	400M

3. Evaluation Results

Benchmark	Kimi K2.6	Kimi K2.7 Code	GPT-5.5	Claude Opus 4.8
Coding
Kimi Code Bench v2	50.9	62.0	69.0	67.4
Program Bench	48.3	53.6	69.1	63.8
MLS Bench Lite	26.7	35.1	35.5	42.8
Agentic
Kimi Claw 24/7 Bench	42.9	46.9	52.8	50.4
MCP Atlas	69.4	76.0	79.4	81.3
MCP Mark Verified	72.8	81.1	92.9	76.4

Footnotes

General Testing Details
- Unless stated otherwise, Kimi K2.7 Code and K2.6 were tested with thinking mode enabled via Kimi Code CLI at temperature = 1.0, top-p = 0.95, and a 262,144-token context length; GPT-5.5 ran in Codex with xhigh mode, and Opus 4.8 in Claude Code with xhigh mode. Aside from these differences, all benchmarks were evaluated under the same conditions.
Coding Benchmarks
- Kimi Code Bench V2 is our in-house benchmark designed to evaluate coding agents on realistic tasks. It has diversed software engineering tasks across 10+ mainstream programming languages and a full production tech stack covering tasks from internal engineering use cases, production incidents, and real-world open-source projects, with emphasis on backend services, infrastructure, performance engineering, systems programming, security, frontend development, and ML/data engineering.
- Program Bench evaluates code-generation agents by asking them to recreate a program’s behavior from only a compiled binary and its documentation. It spans 200 tasks, from small CLI tools to large systems like FFmpeg and SQLite. Submissions are judged against over 248,000 fuzz-generated behavioral tests. In each task, the agent is given an executable and its documentation, but no source code, decompilation, or internet access. It must choose its own implementation language, build the full program from scratch, and pass a behavioral test suite comparing its output against the original binary.
- MLS-Bench evaluates whether AI systems can invent generalizable and scalable ML methods. MLS-Bench-Lite is the official 30-task subset of MLS-Bench, covering LLM pretraining and post-training, robotics, world models, computer vision, reinforcement learning, optimization, ML systems, AI for Science, and more. Agents are given 5 hours to explore before submitting their solutions. Opus 4.8 is evaluated with the max effort setting in Claude Code.
Agentic Benchmarks
- Kimi Claw 24/7 Bench is our in-house benchmark for evaluating long-horizon agentic performance in persistent, multi-day coworking tasks. It spans 17 professional scenarios across 610 evaluation points, covering domains such as software engineering, ML research, recruiting, trading, marketing. All tasks are executed through the OpenClaw harness. The final score is the average pass rate across all evaluation points, and is averaged over 3 runs.
- MCP-Atlas evaluates LLM performance on realistic tool-use tasks through the scalable MCPs. We followed the official MCP-Atlas evaluation configuration with a 100 tool-call budget, and with 32k max tokens per step. The final result is averaged over 3 runs.
- MCPMark-Verified is a human-verified edition of MCPMark, a benchmark for evaluating MCP tool use across five real server environments — Notion, GitHub, Filesystem, Postgres, and Playwright. Each task has been re-checked by our team and the benchmark offical and will be open-sourced soon. We followed the official MCPMark evaluation configuration with a 100-step tool-call budget and 32k max tokens per step. The final result is averaged over 3 runs.

4. Native INT4 Quantization

Kimi-K2.7-Code adopts the same native int4 quantization method as Kimi-K2-Thinking.

6. Model Usage

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 temperature will be 1.0 for Thinking mode.

The recommended top_p is 0.95.

Instant mode is not supported.

Chat Completion

This is a simple chat completion script which shows how to call K2.7-Code API in Thinking mode.

python

import openai
import base64
import requests
def 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}')

Chat Completion with visual 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 openai
import base64
import 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 openai
import base64
import 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}')

Preserve Thinking

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.content

Interleaved Thinking and Multi-Step Tool Call

K2.7-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.

Coding Agent Framework

Kimi K2.7-Code works best with Kimi Code CLI as its agent framework — give it a try at https://www.kimi.com/code.

7. License

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

8. Third Party Notices

See THIRD PARTY NOTICES

9. Contact Us

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

SergeyKlevan/Kimi-K2.7-Code-GGUF

Read our How to Run Kimi K2.7 Code Guide!

Kimi-K2.7-Code

1. Model Introduction

2. Model Summary

3. Evaluation Results

4. Native INT4 Quantization

6. Model Usage

Chat Completion

Chat Completion with visual content

Preserve Thinking

Interleaved Thinking and Multi-Step Tool Call

Coding Agent Framework

7. License

8. Third Party Notices

9. Contact Us

SergeyKlevan/
Kimi-K2.7-Code-GGUF