tabak25

Affine-MoE-5DnvAgAVykQFmgTSwLXTHpzfmi6W32VtV8L1D9yxSmtThWPb

README

License: modified-mit

1. Model Introduction

Kimi K2.5 is an open-source, native multimodal agentic model built through continual pretraining on approximately 15 trillion mixed visual and text tokens atop Kimi-K2-Base. It seamlessly integrates vision and language understanding with advanced agentic capabilities, instant and thinking modes, as well as conversational and agentic paradigms.

Key Features

Native Multimodality: Pre-trained on vision–language tokens, K2.5 excels in visual knowledge, cross-modal reasoning, and agentic tool use grounded in visual inputs.
Coding with Vision: K2.5 generates code from visual specifications (UI designs, video workflows) and autonomously orchestrates tools for visual data processing.
Agent Swarm: K2.5 transitions from single-agent scaling to a self-directed, coordinated swarm-like execution scheme. It decomposes complex tasks into parallel sub-tasks executed by dynamically instantiated, domain-specific agents.

2. Model Summary

Table

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

General Testing Details
- We report results for Kimi K2.5 and DeepSeek-V3.2 with thinking mode enabled, Claude Opus 4.5 with extended thinking mode, GPT-5.2 with xhigh reasoning effort, and Gemini 3 Pro with a high thinking level. For vision benchmarks, we additionally report results for Qwen3-VL-235B-A22B-Thinking.
- Unless otherwise specified, all Kimi K2.5 experiments were conducted with temperature = 1.0, top-p = 0.95, and a context length of 256k tokens.
- Benchmarks without publicly available scores were re-evaluated under the same conditions used for Kimi K2.5 and are marked with an asterisk (*).
- We could not evaluate GPT-5.2 xhigh on all benchmarks due to service stability issues. For benchmarks that were not tested, we mark them as "-".
Text and Reasoning
- HLE, AIME 2025, HMMT 2025 (Feb), and GPQA-Diamond were evaluated with a maximum completion budget of 96k tokens.
- Results for AIME and HMMT are averaged over 32 runs (avg@32); GPQA-Diamond over 8 runs (avg@8).
- For HLE, we report scores on the full set (text & image). Kimi K2.5 scores 31.5 (text) and 21.3 (image) without tools, and 51.8 (text) and 39.8 (image) with tools. The DeepSeek-V3.2 score corresponds to its text-only subset (marked with †) . Hugging Face access was blocked to prevent potential data leakage. HLE with tools uses simple context management: once the context exceeds a threshold, only the latest round of tool messages is retained.

4. Native INT4 Quantization

Kimi-K2.5 adopts the same native int4 quantization method as Kimi-K2-Thinking.

5. Deployment

[!Note] You can access Kimi-K2.5'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-K2.5 is recommended to run on the following inference engines:

vLLM
SGLang
KTransformers

The minimum version requirement for transformers is 4.57.1.

Deployment examples can be found in the Model Deployment Guide.

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:

[!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 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 K2.5 API in Thinking and Instant modes.

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_content}')    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

K2.5 supports Image and Video input.

The following example demonstrates how to call K2.5 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-K2.5/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_content}')    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 K2.5 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-K2.5/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_content}')    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

Interleaved Thinking and Multi-Step Tool Call

K2.5 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 K2.5 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.cn.

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README

License: modified-mit

1. Model Introduction

Key Features

Native Multimodality: Pre-trained on vision–language tokens, K2.5 excels in visual knowledge, cross-modal reasoning, and agentic tool use grounded in visual inputs.
Coding with Vision: K2.5 generates code from visual specifications (UI designs, video workflows) and autonomously orchestrates tools for visual data processing.
Agent Swarm: K2.5 transitions from single-agent scaling to a self-directed, coordinated swarm-like execution scheme. It decomposes complex tasks into parallel sub-tasks executed by dynamically instantiated, domain-specific agents.

2. Model Summary

Table

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

General Testing Details
- We report results for Kimi K2.5 and DeepSeek-V3.2 with thinking mode enabled, Claude Opus 4.5 with extended thinking mode, GPT-5.2 with xhigh reasoning effort, and Gemini 3 Pro with a high thinking level. For vision benchmarks, we additionally report results for Qwen3-VL-235B-A22B-Thinking.
- Unless otherwise specified, all Kimi K2.5 experiments were conducted with temperature = 1.0, top-p = 0.95, and a context length of 256k tokens.
- Benchmarks without publicly available scores were re-evaluated under the same conditions used for Kimi K2.5 and are marked with an asterisk (*).
- We could not evaluate GPT-5.2 xhigh on all benchmarks due to service stability issues. For benchmarks that were not tested, we mark them as "-".
Text and Reasoning
- HLE, AIME 2025, HMMT 2025 (Feb), and GPQA-Diamond were evaluated with a maximum completion budget of 96k tokens.
- Results for AIME and HMMT are averaged over 32 runs (avg@32); GPQA-Diamond over 8 runs (avg@8).
- For HLE, we report scores on the full set (text & image). Kimi K2.5 scores 31.5 (text) and 21.3 (image) without tools, and 51.8 (text) and 39.8 (image) with tools. The DeepSeek-V3.2 score corresponds to its text-only subset (marked with †) . Hugging Face access was blocked to prevent potential data leakage. HLE with tools uses simple context management: once the context exceeds a threshold, only the latest round of tool messages is retained.

4. Native INT4 Quantization

Kimi-K2.5 adopts the same native int4 quantization method as Kimi-K2-Thinking.

5. Deployment

[!Note] You can access Kimi-K2.5'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-K2.5 is recommended to run on the following inference engines:

vLLM
SGLang
KTransformers

The minimum version requirement for transformers is 4.57.1.

Deployment examples can be found in the Model Deployment Guide.

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:

[!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 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 K2.5 API in Thinking and Instant modes.

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_content}')    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

K2.5 supports Image and Video input.

The following example demonstrates how to call K2.5 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-K2.5/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_content}')    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 K2.5 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-K2.5/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_content}')    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

Interleaved Thinking and Multi-Step Tool Call

K2.5 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 K2.5 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.cn.