Kimi K2.7 Code

Moonshot AI's open-weight coding model (June 2026): a 1T-parameter MoE (32B active) that uses ~30% fewer reasoning tokens than K2.6 and posts strong tool-use scores. Input cache-hit as low as $0.19/1M; third-party hosts (OpenRouter/DeepInfra) ~$0.74 in / $3.50 out. Modified-MIT open weights, deployable via vLLM/SGLang. Benchmarks are vendor-reported — verify.

Kimi K2.7 Code strengths

• Open weights (Modified MIT)
• Repository-scale agentic coding
• ~30% fewer reasoning tokens vs K2.6
• Strong tool-use (MCP)
• Self-hostable (vLLM/SGLang)

Pricing & context

Cost guide: a typical call of about 10K input + 2K output tokens costs roughly $0.018 at list prices. Worth modelling against cheaper tiers before committing high-volume traffic.

When to choose Kimi K2.7 Code

Kimi K2.7 Code is best for repository-scale refactoring and long multi-turn agentic coding with an open-weight, cost-efficient model. If your workload is more cost-sensitive, weigh it against gpt-oss-120b (≈$0.03 input /1M) first.

Kimi K2.7 Code FAQ

How much does Kimi K2.7 Code cost?

Kimi K2.7 Code is priced at $0.95 per 1M input tokens and $4.00 per 1M output tokens (public API list price), with a 256K tokens context window. A typical call of about 10K input and 2K output tokens costs roughly $0.018.

What is Kimi K2.7 Code best for?

Kimi K2.7 Code by Moonshot AI is best for repository-scale refactoring and long multi-turn agentic coding with an open-weight, cost-efficient model.

How does Kimi K2.7 Code pricing compare to Z.ai GLM-5.2?

Kimi K2.7 Code and Z.ai GLM-5.2 have the same input list price ($0.95 per 1M tokens); output pricing is $4.00 vs $3.00.

Is Kimi K2.7 Code multimodal?

Kimi K2.7 Code supports text, image, video.