What is Kimi K2.7 Code?

Kimi K2.7 Code is an open-source agentic coding model from Moonshot AI designed for developers, engineering teams, and AI coding workflows that require long-context understanding and multi-step execution. It is built for real-world software engineering tasks, including code generation, code review, debugging, repository navigation, tool use, and long-horizon development work. The model is described by Moonshot AI as a coding-focused agentic model with stronger performance on complex coding tasks than earlier Kimi K2 releases. Kimi K2.7 Code supports a 256K context window, allowing it to process large codebases, technical requirements, logs, documentation, and multi-file development context in a single workflow. It is available through Kimi Code, which provides developer-oriented tools for using the model in coding tasks. The model can also be accessed through Moonshot’s API platform, where Kimi K2.7 Code and Kimi K2.7 Code Highspeed are offered alongside earlier Kimi models. For developers who want more control, Kimi K2.7 Code is listed on Hugging Face with deployment support for inference engines such as vLLM, SGLang, and KTransformers. It uses OpenAI- and Anthropic-compatible API options, helping teams connect it to existing applications, coding tools, and agent systems more easily. Third-party model listings describe it as using a 1T-parameter mixture-of-experts architecture with 32B active parameters, native INT4 quantization, and reduced thinking-token usage compared with Kimi K2.6. The model is designed to improve efficiency by using fewer reasoning tokens while still supporting demanding programming workflows. Kimi K2.7 Code is a strong fit for developers who want an open, long-context, tool-friendly AI model for software engineering automation and AI-assisted development.

Pricing

Price Starts At:
Free
Price Overview:
Open source
Free Version:
Free Version available.

Integrations

Offers API?:
Yes, Kimi K2.7 Code provides an API
All Kimi K2.7 Code Integrations
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Screenshots and Video

Kimi K2.7 Code Screenshot 1
Kimi K2.7 Code Screenshot 1

Company Facts

Company Name:
Moonshot AI
Date Founded:
2023
Company Location:
China
Company Website:
www.kimi.com

Product Details

Deployment
SaaS
Windows
Mac
Linux
On-Prem
Training Options
Documentation Hub
Support
Web-Based Support

Product Details

Target Company Sizes
Individual
1-10
11-50
51-200
201-500
501-1000
1001-5000
5001-10000
10001+
Target Organization Types
Mid Size Business
Small Business
Enterprise
Freelance
Nonprofit
Government
Startup
Supported Languages
English

Kimi K2.7 Code Categories and Features

AI Models

AI Coding Models

Kimi K2.7 Code Customer Reviews

Write a Review
  • Reviewer Name: A Verified Reviewer
    Position: Developer
    Has used product for: Less than 6 months
    Uses the product: Daily
    Org Size (# of Employees): 100 - 499
    Feature Set
    Layout
    Ease Of Use
    Cost
    Customer Service
    Would you Recommend to Others?
    1 2 3 4 5 6 7 8 9 10

    Epic OSS model

    Date: Jun 15 2026
    Summary

    Kimi K2.7 Code is a 5-star model for developers, AI builders, and anyone working with coding agents. It is fast, capable, and practical for everyday programming as well as more advanced agentic workflows.

    I would highly recommend it to anyone looking for a strong coding model that can support development, automation, and AI agent projects.

    Positive

    Kimi K2.7 Code has been a great model for coding and AI agent development. It handles technical prompts well, understands developer workflows, and gives clear, useful responses for real coding tasks.

    I use it for debugging, writing scripts, improving code structure, and planning agent workflows. It is especially helpful when I need clean reasoning, practical suggestions, and code that is easy to adapt.

    For AI agents, Kimi K2.7 Code feels reliable and capable. It does a strong job following instructions, working through multi-step tasks, and producing structured outputs that fit automation and agent-based use cases.

    Negative

    It still works best when the prompt includes enough context. For very complex projects or large codebases, I sometimes need to provide extra details to get the best results.

    I also prefer to review important code before using it, especially for production work, since small mistakes can still happen.

    Read More...
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