Code📅 Released: 2026-06-12

Kimi K2.7 Code

Moonshot AI's flagship 1-trillion parameter MoE model, optimized for long-horizon coding and 30% lower thinking-token overhead.

Overview

Kimi K2.7 Code is Moonshot AI's premier programming specialist released in June 2026. Built as a 1-trillion parameter Mixture-of-Experts (MoE) model, it introduces deep reasoning loops for software engineering while optimizing speed and efficiency. A key upgrade is the 30% reduction in thinking-tokens compared to K2.6, allowing developers to execute complex refactoring tasks at lower costs.

Unique Factor

A 1-trillion parameter MoE model that achieves state-of-the-art coding metrics while significantly reducing thinking-token overhead.

Key Capabilities

●

1T Parameter MoE

●

30% Lower Thinking-Token Cost

●

256K context

●

Agentic software refactoring

Benchmarks

MMLU Score

88%

HumanEval (Coding)

96.5%

GPQA Diamond

82%

MATH Benchmark

89%

Top Use Cases

Bilingual Code Refactoring

Refactor complex backend systems using instructions mixed in Chinese and English, preserving AST structure.

Example: “将这个 Express 后端重构为 Fastify，使用 TypeScript，并优化数据库连接池配置。”

Autonomous Log Auditing

Feed large application logs and configuration scripts to locate server errors and generate fixes.

Example: “Audit these 100,000 lines of system logs. Identify why our Kubernetes nodes are restart-looping and write a YAML fix.”

Detailed Features

1T Parameter MoE: Massive scale utilizing 100B active parameters per token for optimized reasoning.

30% Thinking Token Reduction: Reduced token footprint for complex programming loops, cutting costs and latency.

256K Context Window: High recall context suited for multi-file code editing and log audits.

Bilingual Coding Mastery: Equal accuracy parsing English and Chinese programming instructions.

✓ Strengths & Pros

• Exceptional coding intelligence and syntax accuracy
• 30% reduction in token overhead translates directly to cost savings
• Available for open-weights hosting and Cloudflare deployment

✕ Limitations & Cons

• Heavy infrastructure required for local 1T MoE deployment
• No native vision or audio input support (focus is text/code)

Ideal Usage & Target Audience

Best For

Software engineering teams, DevOps managers, and developers looking for cost-effective agentic coding.

Not Recommended For

Users requiring native image generation or multimodal video analysis.

API Implementation

python

import openai

client = openai.OpenAI(base_url='https://api.moonshot.cn/v1', api_key='KIMI_API_KEY')
response = client.chat.completions.create(
    model='kimi-k2.7-code',
    messages=[{'role': 'user', 'content': 'Optimize this SQL query for high concurrency.'}]
)

Check the official documentation for full SDK details.

Frequently Asked Questions

Is Kimi K2.7 Code open source?

Yes, Moonshot AI has released open weights under a community license, and the API is accessible on various cloud hosting solutions.

What is the advantage of the 30% thinking token reduction?

AI reasoning models generate 'thinking tokens' internally before answering. By optimizing this process, K2.7 Code reduces overall cost and latency on API calls.