Kimi K2.6

Native multimodal agentic model with long-horizon coding and Agent Swarm

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About model

Kimi K2.6 is Moonshot AI's native multimodal agentic model built on a 1T parameter (32B activated) MoE architecture with 256K context. It delivers long-horizon coding stability across multiple languages and domains, Agent Swarm orchestration scaling to 300 sub-agents with 4,000 coordinated steps, and proactive autonomous execution for persistent background agents. The model supports text, image, and video input with thinking mode for multi-step reasoning and tool invocation.

HLE-Full (w/ tools)

54.00%

Expert-level multimodal reasoning across 100+ subjects

Agent Swarm Sub-Agents

300

4000 coordinated steps for parallel task decomposition

SWE-Bench Verified

80.20%

Long-horizon coding across languages and domains

Model key capabilities

Long-Horizon Coding: Stable end-to-end coding across Rust, Go, Python, frontend, DevOps, and performance optimization with 80.2% SWE-Bench Verified and 89.6% LiveCodeBench v6
Agent Swarm: Scales to 300 sub-agents executing 4,000 coordinated steps, decomposing complex tasks into parallel domain-specialized subtasks for end-to-end autonomous output
Multimodal Understanding: Native text, image, and video input via MoonViT encoder with 79.4% MMMU-Pro and coding-driven design from visual inputs to production interfaces
Proactive Autonomous Execution: Persistent background agents managing schedules, code execution, and cross-platform operations with 73.1% OSWorld-Verified

Performance benchmarks

Model	AIME 2025	GPQA Diamond	HLE	LiveCodeBench	MATH500	SWE-bench verified
Kimi K2.6	96.40%	90.50%	34.70%	89.60%		80.20%
Related open-source models
Competitor closed-source models
Claude Opus 4.6		90.5%	34.2%			78.7%
OpenAI o3		83.3%	24.9%		99.2%	62.3%
OpenAI o1		76.8%			96.4%	48.9%
GPT-4o		49.2%	2.7%	32.3%	89.3%	31.0%

API usage

cURL
Python
Typescript

Endpoint:

moonshotai/Kimi-K2.6

curl -X POST "https://api.together.xyz/v1/chat/completions" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "moonshotai/Kimi-K2.6",
    "messages": [
      {
        "role": "user",
        "content": "What are some fun things to do in New York?"
      }
    ]
}'

from together import Together

client = Together()

response = client.chat.completions.create(
  model="moonshotai/Kimi-K2.6",
  messages=[
    {
      "role": "user",
      "content": "What are some fun things to do in New York?"
    }
  ]
)
print(response.choices[0].message.content)

import Together from 'together-ai';
const together = new Together();

const completion = await together.chat.completions.create({
  model: 'moonshotai/Kimi-K2.6',
  messages: [
    {
      role: 'user',
      content: 'What are some fun things to do in New York?'
     }
  ],
});

console.log(completion.choices[0].message.content);

Model card
Architecture Overview:
• 1T total parameter MoE architecture with 32B parameters activated per token
• 384 experts with 8 selected per token plus 1 shared expert, using Multi-head Latent Attention (MLA)
• 256K token context window with native multimodal support for text, image, and video input
• MoonViT vision encoder (400M parameters) for image and video understanding
• Thinking mode for multi-step reasoning and tool invocation
• Native INT4 quantization

Training Methodology:
• Built on the Kimi K2.5 architecture with targeted improvements for long-horizon coding stability
• Enhanced reinforcement learning for coding task distributions across Rust, Go, Python, frontend, DevOps, and performance optimization
• Improved instruction compliance and self-correction capabilities for complex software engineering tasks

Performance Characteristics:
• 54.0% HLE-Full w/ tools for expert-level multimodal reasoning
• 80.2% SWE-Bench Verified, 58.6% SWE-Bench Pro, 76.7% SWE-Bench Multilingual
• 89.6% LiveCodeBench v6 for code generation
• 96.4% AIME 2026, 90.5% GPQA-Diamond for reasoning
• 83.2% BrowseComp (86.3% with Agent Swarm) for agentic search
• 73.1% OSWorld-Verified for autonomous computer use
• Agent Swarm: 300 sub-agents executing 4,000 coordinated steps
‍
Prompting
Together AI API Access:
• Access Kimi K2.6 via Together AI APIs using the endpoint moonshotai/Kimi-K2.6
• Authenticate using your Together AI API key in request headers
• Supports thinking mode, tool calling, image input, and video input
• Available on both serverless and dedicated infrastructure
‍
Applications & use cases
Long-Horizon Coding:
• Complex end-to-end software engineering across Rust, Go, Python, frontend, DevOps, and performance optimization
• 80.2% SWE-Bench Verified with improved stability on long-running coding tasks
• Coding-driven design: transforms prompts and visual inputs into production-ready interfaces and full-stack workflows

Agentic Workflows:
• Agent Swarm: 300 sub-agents executing 4,000 coordinated steps for parallel task decomposition
• Proactive autonomous execution for persistent background agents managing schedules, code, and cross-platform operations
• 73.1% OSWorld-Verified for autonomous computer use
• Multi-step tool invocation with thinking mode for complex problem solving

Multimodal Reasoning:
• Native image and video understanding via 400M parameter MoonViT encoder
• 79.4% MMMU-Pro for multimodal understanding
• 87.4% MathVision for visual mathematical reasoning
• 256K context for processing large codebases, documents, and visual inputs
‍

Related models

Model specifications

Model data

Model provider
Moonshot AI
Type
Reasoning
Vision
Chat
Code
LLM
Main use cases
Reasoning
Features
Function Calling
JSON Mode
Speed
High
Intelligence
High
Deployment
Serverless
Monthly Reserved
Endpoint
moonshotai/Kimi-K2.6
Parameters
1T
Activated parameters
32B
Context length
256K
Input price
$1.20 / 1M tokens
$0.20 (cached)/1M
Output price
$4.50 / 1M tokens
Input modalities
Text
Image
Video
Output modalities
Text

Released
April 20, 2026
Quantization level
FP4
External link
Provider docs
Category
Chat

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Quickstart docs

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Kimi K2.6

About model

API usage

Model card

Prompting

Applications & use cases