Everything about Kimi K2.7 Code

Overview

Released on June 12, 2026, Kimi K2.7 Code is one of the 6 Moonshot AI models tracked on this site. It is an open-weight model: you can host it yourself on infrastructure you control, and 60 API providers listed on models.dev also offer it. It has a reasoning mode: it can "think" before answering, which helps with multi-step problems but uses more tokens, and therefore more money. That makes it 4 months old as of October 22, 2026.

We file it under open-weight models (68 models in this category on the site).

Pricing and budget

Kimi K2.7 Code costs $0.95 per million input tokens and $4 per million output tokens (developer's price, checked on October 22, 2026).

In practice, an agent task that reads 20,000 tokens and writes 2,000 costs about $0.027, or $27 for 1,000 such tasks.

That is 4 times the median output price of open-weight models ($1).

Tokens read back from cache cost $0.19 per million, which helps an agent that keeps resending the same context.

To estimate your own budget, use the agent cost calculator.

Measured performance

According to results published by Epoch AI (checked on October 22, 2026), Kimi K2.7 Code has a capability index of 66.7 out of 100, computed on 5 benchmarks from our basket.

It is not among the top 30 of the weekly ranking.

• GPQA Diamond (PhD-level science questions): 87.9% — the best tracked model, Gemini 4 Argon, reaches 96.4%
• OTIS Mock AIME (competition mathematics problems): 95.6% — the best tracked model, GPT-5.5 Pro, reaches 100%
• FrontierMath (very hard research-level mathematics): 54% — the best tracked model, GPT-6 Astra, reaches 93.7%
• FrontierCode (advanced programming tasks): 30.1% — the best tracked model, Claude Opus 5.5, reaches 54.6%

What it can do

Its context window reaches 262,144 tokens, roughly 393 pages of text at 500 words per page: enough for a long report or several contracts at once. A single answer can run to 262,144 tokens.

It accepts as input: text, image and video. Its knowledge stops at January 2025: beyond that, it needs web search or your documents.

It can call tools (functions, APIs) and return structured JSON output, which makes it a possible foundation for an AI agent.

The weights are published on Hugging Face (moonshotai/Kimi-K2.7-Code). See also our list of open-source models.

When to choose it

Given its category, Kimi K2.7 Code is mainly aimed at projects where you want control over your data or hosting, or want to adapt the model to your field.

Before adopting it, test it on your own examples: benchmarks measure standard tasks, not your job.

What could replace it?

In the same category, the models closest to Kimi K2.7 Code are:

• DeepSeek V4 Flash 0731 (DeepSeek, index 68.2)
• GLM-4.7 (Zhipu AI (Z.ai), index 68.4)
• Kimi K2 Thinking (Moonshot AI, index 69.6)
• GLM-5.2 (Zhipu AI (Z.ai), index 70.5)