---
tags:
- compressed-tensors
license: other
license_name: modified-mit
library_name: transformers
pipeline_tag: image-text-to-text
---
🤗 huggingchat
|
📰 Tech Blog
## 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.
## 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 |
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 |
Footnotes
1. **General Testing Details**
- We report results for Kimi K2.6 and Kimi K2.5 with thinking mode enabled, Claude Opus 4.6 with max effort, GPT-5.4 with xhigh reasoning effort, and Gemini 3.1 Pro with a high thinking level.
- Unless otherwise specified, all Kimi K2.6 experiments were conducted with temperature = 1.0, top-p = 1.0, and a context length of 262,144 tokens.
- Benchmarks without publicly available scores were re-evaluated under the same conditions used for Kimi K2.6 and are marked with an asterisk (`*`). Except where noted with an asterisk, all other results are cited from official reports.
2. **Reasoning Benchmarks**
- IMO-AnswerBench scores for GPT-5.4 and Claude 4.6 were obtained from [z.ai/blog/glm-5.1](https://z.ai/blog/glm-5.1).
- Humanity's Last Exam (HLE) and other reasoning tasks were evaluated with a maximum generation length of 98,304 tokens. By default, we report results on the HLE full set. For the text-only subset, Kimi K2.6 achieves 36.4% accuracy without tools and 55.5% with tools.
3. **Tool-Augmented / Agentic Tasks**
- Kimi K2.6 was equipped with search, code-interpreter, and web-browsing tools for HLE with tools, BrowseComp, DeepSearchQA, and WideSearch.
- For HLE-Full with tools, the maximum generation length is 262,144 tokens with a per-step limit of 49,152 tokens. We employ a simple context management strategy: once the context window exceeds the threshold, only the most recent round of tool-related messages is retained.
- For BrowseComp, we report scores obtained with context management using the same discard-all strategy as Kimi K2.5 and DeepSeek-V3.2.
- For DeepSearchQA, no context management was applied to Kimi K2.6 tests, and tasks exceeding the supported context length were directly counted as failed. Scores for Claude Opus 4.6, GPT-5.4, and Gemini 3.1 Pro on DeepSearchQA are cited from the [Claude Opus 4.7 System Card](https://cdn.sanity.io/files/4zrzovbb/website/037f06850df7fbe871e206dad004c3db5fd50340.pdf).
- For WideSearch, we report results under the "hide tool result" context management setting. Once the context window exceeds the threshold, only the most recent round of tool-related messages is retained.
- The test system prompts are identical to those used in the [Kimi K2.5 technical report](https://arxiv.org/pdf/2602.02276).
- Claw Eval was conducted using version 1.1 with max-tokens-per-step = 16384.
- For APEX-Agents, we evaluate 452 tasks from the public 480-task release, as done by [Artificial Analysis](https://artificialanalysis.ai/evaluations/apex-agents-aa)(excluding Investment Banking Worlds 244 and 246, which have external runtime dependencies)
4. **Coding Tasks**
- Terminal-Bench 2.0 scores were obtained with the default agent framework (Terminus-2) and the provided JSON parser, operating in preserve thinking mode.
- For the SWE-Bench series of evaluations (including Verified, Multilingual, and Pro), we used an in-house evaluation framework adapted from SWE-agent. This framework includes a minimal set of tools—bash tool, createfile tool, insert tool, view tool, strreplace tool, and submit tool.
- All reported scores for coding tasks are averaged over 10 independent runs.
5. **Vision Benchmarks**
- Max-tokens = 98,304, averaged over three runs (avg@3).
- Settings with Python tool use max-tokens-per-step = 65,536 and max-steps = 50 for multi-step reasoning.
- MMMU-Pro follows the official protocol, preserving input order and prepending images.
## 4. Native INT4 Quantization
Kimi-K2.6 adopts the same native int4 quantization method as [Kimi-K2-Thinking](https://huggingface.co/moonshotai/Kimi-K2-Thinking#4-native-int4-quantization).
## 5. Deployment
> [!Note]
> 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](https://kimi.com/blog/kimi-vendor-verifier.html).
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](docs/deploy_guidance.md).
---
## 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 K2.6 API in Thinking and Instant modes.
```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}')
# 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:
```python
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:
```python
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:
```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.",
"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](https://platform.moonshot.ai/docs/guide/use-kimi-k2-thinking-model#complete-example).
### 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.
---
## 7. License
Both the code repository and the model weights are released under the [Modified MIT License](LICENSE).
---
## 8. Third Party Notices
See [THIRD PARTY NOTICES](THIRD_PARTY_NOTICES.md)
---
## 9. Contact Us
If you have any questions, please reach out at [support@moonshot.ai](mailto:support@moonshot.ai).
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