kimi-k2.7-code

kimi-k2.7-code

29.4K Downloads Updated 1 week ago

Kimi K2.7 Code is Moonshot AI's coding-focused agentic model built upon Kimi K2.6, with substantial improvements on real-world long-horizon coding tasks and roughly 30% lower thinking-token usage.

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vision tools thinking cloud

Usage

high

Context

256K tokens

Size

1.04T parameters

CLI cURL Python JavaScript

Documentation Documentation

ollama run kimi-k2.7-code:cloud

curl http://localhost:11434/api/chat \ -d '{ "model": "kimi-k2.7-code:cloud", "messages": [{"role": "user", "content": "Hello!"}] }'

from ollama import chat

response = chat( model='kimi-k2.7-code:cloud', messages=[{'role': 'user', 'content': 'Hello!'}], ) print(response.message.content)

import ollama from 'ollama'

const response = await ollama.chat({ model: 'kimi-k2.7-code:cloud', messages: [{role: 'user', content: 'Hello!'}], }) console.log(response.message.content)

Applications

Claude Code `ollama launch claude --model kimi-k2.7-code:cloud`

Codex App `ollama launch codex-app --model kimi-k2.7-code:cloud`

OpenClaw `ollama launch openclaw --model kimi-k2.7-code:cloud`

Hermes Agent `ollama launch hermes --model kimi-k2.7-code:cloud`

Codex `ollama launch codex --model kimi-k2.7-code:cloud`

OpenCode `ollama launch opencode --model kimi-k2.7-code:cloud`

Models

View all →

Name

1 model

Size / Usage

Context

Input

kimi-k2.7-code:cloud High Usage · 256K context window · Text, Image · 1 week ago

kimi-k2.7-code:cloud

256K

Text, Image

Readme

Kimi K2.7 Code is a coding-focused agentic model built upon Kimi K2.6. With substantial improvements on real-world long-horizon coding tasks, it strengthens end-to-end task completion across complex software engineering workflows while improving token efficiency, reducing thinking-token usage by approximately 30% compared with Kimi K2.6.

Key Features

• Long-horizon coding: Substantial gains on realistic, end-to-end software engineering tasks across 10+ programming languages and a full production tech stack, spanning backend services, infrastructure, performance engineering, systems programming, security, frontend, and ML/data engineering.
• Improved token efficiency: Reduces thinking-token usage by approximately 30% compared with Kimi K2.6, while improving task completion on complex workflows.
• Stronger agentic tool use: Improved performance on multi-step tool calling and MCP-based environments, with interleaved thinking preserved across turns (`preserve_thinking`) for coherent multi-step coding sessions.
• Native multimodal: Supports image and video input via the MoonViT vision encoder, with a 256K token context window.

Benchmarks

| Benchmark | Kimi K2.6 | Kimi K2.7 Code | GPT-5.5 | Claude Opus 4.8 | | -------------------- | --------- | -------------- | ------- | --------------- | | Coding | | | | | | Kimi Code Bench v2 | 50.9 | 62.0 | 69.0 | 67.4 | | Program Bench | 48.3 | 53.6 | 69.1 | 63.8 | | MLS Bench Lite | 26.7 | 35.1 | 35.5 | 42.8 | | Agentic | | | | | | Kimi Claw 24⁄7 Bench | 42.9 | 46.9 | 52.8 | 50.4 | | MCP Atlas | 69.4 | 76.0 | 79.4 | 81.3 | | MCP Mark Verified | 72.8 | 81.1 | 92.9 | 76.4 |

Reference

• Hugging Face

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