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
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 |
|
3. Evaluation Results
- 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.
- Reasoning Benchmarks
- IMO-AnswerBench scores for GPT-5.4 and Claude 4.6 were obtained from 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.
- Tool-Augmented / Agentic Tasks
4. Native INT4 Quantization
Kimi-K2.6 adopts the same native int4 quantization method as Kimi-K2-Thinking.
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.
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.
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.
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}')
response = client.chat.completions.create(
model=model_name,
messages=messages,
stream=False,
max_tokens=4096,
extra_body={'thinking': {'type': 'disabled'}},
)
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}')
response = client.chat.completions.create(
model=model_name,
messages=messages,
stream=False,
max_tokens=4096,
extra_body={'thinking': {'type': 'disabled'}},
)
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}')
response = client.chat.completions.create(
model=model_name,
messages=messages,
stream=False,
max_tokens=4096,
extra_body={'thinking': {'type': 'disabled'}},
)
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'}},
)
print(f"response: {response.choices[0].message.reasoning}")
return response.choices[0].message.content
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.
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
If you have any questions, please reach out at support@moonshot.ai.