Moonshot AI Releases Kimi K2.7-Code: a Coding Model Reporting +21.8% on Kimi Code Bench v2 Over K2.6

This week, Moonshot AI released Kimi K2.7-Code. It is a coding-focused, agentic model. The model weights ship on Hugging Face under a Modified MIT license. You can also reach it through the Kimi API and Kimi Code.

K2.7-Code targets long-horizon software engineering, not general chat. It plans, edits, runs tools, and debugs across many steps. Moonshot pairs the model with a subscription coding platform around it.

K2.7-Code is a Mixture-of-Experts model. It holds 1T total parameters and activates 32B per token. The design uses 384 experts, with 8 selected per token and 1 shared. It has 61 layers, including 1 dense layer.

Attention uses MLA, and the feed-forward path uses SwiGLU. A MoonViT vision encoder adds 400M parameters for image and video input. The model ships with native INT4 quantization. The context window is 256K tokens (262,144).

Two constraints matters: Thinking mode is mandatory; disabling it returns an API error. Sampling is fixed: temperature 1.0, top_p 0.95, n 1, penalties 0.0. Default max output is 32,768 tokens.