Kimi K2.6

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Collection including moonshotai/Kimi-K2.6

[

Kimi K2.5

Collection

Moonshot's large visual-language model • 4 items • Updated Jul 27 • 85

](https://huggingface.co/collections/moonshotai/kimi-k25)

Paper for moonshotai/Kimi-K2.6

[

Kimi K2.5: Visual Agentic Intelligence

Paper • 2602.02276 • Published Feb 2 • 280

](https://huggingface.co/papers/2602.02276)

9. Contact Us

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

Safetensors

Model size

1T params

Tensor type

F32

·

I32

·

BF16

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8. Third Party Notices

See THIRD PARTY NOTICES

7. License

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

6. Model Usage

The usage demos below demonstrate how to call our official API.

For third-party APIs deployed with vLLM or SGLang, please note that:

• 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 and 0.6 for Instant mode.

• The recommended top_p is 0.95.

• To use instant mode, you need to pass {'chat_template_kwargs': {"thinking": False}} in extra_body.

Chat Completion

This is a simple chat completion script which shows how to call K2.6 API in Thinking and Instant modes.

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}')

    # To use instant mode, pass {"thinking" = {"type":"disabled"}}
    response = client.chat.completions.create(
        model=model_name,
        messages=messages,
        stream=False,
        max_tokens=4096,
        extra_body={'thinking': {'type': 'disabled'}},  # this is for official API
        # extra_body= {'chat_template_kwargs': {"thinking": False}}  # this is for vLLM/SGLang
    )
    print('====== Below is response in Instant Mode ======')
    print(f'response: {response.choices[0].message.content}')

Chat Completion with visual content

K2.6 supports Image and Video input.

The following example demonstrates how to call K2.6 API with image input:

import openai
import base64
import requests

def chat_with_image(client: openai.OpenAI, model_name: str):
    url = 'https://huggingface.co/moonshotai/Kimi-K2.6/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}')

    # Also support instant mode if you pass {"thinking" = {"type":"disabled"}}
    response = client.chat.completions.create(
        model=model_name,
        messages=messages,
        stream=False,
        max_tokens=4096,
        extra_body={'thinking': {'type': 'disabled'}},  # this is for official API
        # extra_body= {'chat_template_kwargs': {"thinking": False}}  # this is for vLLM/SGLang
    )
    print('====== Below is response in Instant Mode ======')
    print(f'response: {response.choices[0].message.content}')

    return response.choices[0].message.content

The following example demonstrates how to call K2.6 API with video input:

import openai
import base64
import requests

def chat_with_video(client: openai.OpenAI, model_name:str):
    url = 'https://huggingface.co/moonshotai/Kimi-K2.6/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}')

    # Also support instant mode if pass {"thinking" = {"type":"disabled"}}
    response = client.chat.completions.create(
        model=model_name,
        messages=messages,
        stream=False,
        max_tokens=4096,
        extra_body={'thinking': {'type': 'disabled'}},  # this is for official API
        # extra_body= {'chat_template_kwargs': {"thinking": False}}  # this is for vLLM/SGLang
    )
    print('====== Below is response in Instant Mode ======')
    print(f'response: {response.choices[0].message.content}')
    return response.choices[0].message.content

Preserve Thinking

Kimi K2.6 supports preserve_thinking mode, which retains full reasoning content across multi-turn interactions and enhances performance in coding agent scenarios.

This feature is disabled by default. The following example demonstrates how to call K2.6 API in preserve_thinking mode:

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.",
            "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,
        extra_body={'thinking': {'type': 'enabled', 'keep': 'all'}},  # this is for official API
        # extra_body={"chat_template_kwargs": {"thinking":True, "preserve_thinking": True}},  # this is for vLLM/SGLang
        # We recommend enabling preserve_thinking only in think mode.
    )
    # 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

K2.6 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 K2.6 works best with Kimi Code CLI as its agent framework — give it a try at https://www.kimi.com/code.

5. Deployment

You can access Kimi-K2.6's API on https://platform.moonshot.ai and we provide OpenAI/Anthropic-compatible API for you. To verify the deployment is correct, we also provide the Kimi Vendor Verifier. Currently, Kimi-K2.6 is recommended to run on the following inference engines:

• vLLM
• SGLang
• KTransformers

Kimi-K2.6 has the same architecture as Kimi-K2.5, 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.

4. Native INT4 Quantization

Kimi-K2.6 adopts the same native int4 quantization method as Kimi-K2-Thinking.

3. Evaluation Results

| Benchmark | Kimi K2.6 | GPT-5.4
(xhigh) | Claude Opus 4.6
(max effort) | Gemini 3.1 Pro
(thinking high) | Kimi K2.5 | | :-: | :-: | :-: | :-: | :-: | :-: | | Agentic | | HLE-Full
(w/ tools) | 54.0 | 52.1 | 53.0 | 51.4 | 50.2 | | BrowseComp | 83.2 | 82.7 | 83.7 | 85.9 | 74.9 | | BrowseComp
(Agent Swarm) | 86.3 | 78.4 | | DeepSearchQA
(f1-score) | 92.5 | 78.6 | 91.3 | 81.9 | 89.0 | | DeepSearchQA
(accuracy) | 83.0 | 63.7 | 80.6 | 60.2 | 77.1 | | WideSearch
(item-f1) | 80.8 | - | - | - | 72.7 | | Toolathlon | 50.0 | 54.6 | 47.2 | 48.8 | 27.8 | | MCPMark | 55.9 | 62.5* | 56.7* | 55.9* | 29.5 | | Claw Eval (pass^3) | 62.3 | 60.3 | 70.4 | 57.8 | 52.3 | | Claw Eval (pass@3) | 80.9 | 78.4 | 82.4 | 82.9 | 75.4 | | APEX-Agents | 27.9 | 33.3 | 33.0 | 32.0 | 11.5 | | OSWorld-Verified | 73.1 | 75.0 | 72.7 | - | 63.3 | | Coding | | Terminal-Bench 2.0
(Terminus-2) | 66.7 | 65.4* | 65.4 | 68.5 | 50.8 | | SWE-Bench Pro | 58.6 | 57.7 | 53.4 | 54.2 | 50.7 | | SWE-Bench Multilingual | 76.7 | - | 77.8 | 76.9* | 73.0 | | SWE-Bench Verified | 80.2 | - | 80.8 | 80.6 | 76.8 | | SciCode | 52.2 | 56.6 | 51.9 | 58.9 | 48.7 | | OJBench (python) | 60.6 | - | 60.3 | 70.7 | 54.7 | | LiveCodeBench (v6) | 89.6 | - | 88.8 | 91.7 | 85.0 | | Reasoning & Knowledge | | HLE-Full | 34.7 | 39.8 | 40.0 | 44.4 | 30.1 | | AIME 2026 | 96.4 | 99.2 | 96.7 | 98.3 | 95.8 | | HMMT 2026 (Feb) | 92.7 | 97.7 | 96.2 | 94.7 | 87.1 | | IMO-AnswerBench | 86.0 | 91.4 | 75.3 | 91.0* | 81.8 | | GPQA-Diamond | 90.5 | 92.8 | 91.3 | 94.3 | 87.6 | | Vision | | MMMU-Pro | 79.4 | 81.2 | 73.9 | 83.0* | 78.5 | | MMMU-Pro (w/ python) | 80.1 | 82.1 | 77.3 | 85.3* | 77.7 | | CharXiv (RQ) | 80.4 | 82.8* | 69.1 | 80.2* | 77.5 | | CharXiv (RQ) (w/ python) | 86.7 | 90.0* | 84.7 | 89.9* | 78.7 | | MathVision | 87.4 | 92.0* | 71.2* | 89.8* | 84.2 | | MathVision (w/ python) | 93.2 | 96.1* | 84.6* | 95.7* | 85.0 | | BabyVision | 39.8 | 49.7 | 14.8 | 51.6 | 36.5 | | BabyVision (w/ python) | 68.5 | 80.2* | 38.4* | 68.3* | 40.5 | | V* (w/ python) | 96.9 | 98.4* | 86.4* | 96.9* | 86.9 |

2. Model Summary

1. Model Introduction

Kimi K2.6 is an open-source, native multimodal agentic model that advances practical capabilities in long-horizon coding, coding-driven design, proactive autonomous execution, and swarm-based task orchestration.

Key Features

• Long-Horizon Coding: K2.6 achieves significant improvements on complex, end-to-end coding tasks, generalizing robustly across programming languages (Rust, Go, Python) and domains spanning front-end, DevOps, and performance optimization.
• Coding-Driven Design: K2.6 is capable of transforming simple prompts and visual inputs into production-ready interfaces and lightweight full-stack workflows, generating structured layouts, interactive elements, and rich animations with deliberate aesthetic precision.
• Elevated Agent Swarm: Scaling horizontally to 300 sub-agents executing 4,000 coordinated steps, K2.6 can dynamically decompose tasks into parallel, domain-specialized subtasks, delivering end-to-end outputs from documents to websites to spreadsheets in a single autonomous run.
• Proactive & Open Orchestration: For autonomous tasks, K2.6 demonstrates strong performance in powering persistent, 24/7 background agents that proactively manage schedules, execute code, and orchestrate cross-platform operations without human oversight.