Moonshot AI

kimi-k2.7-code:cloud

High usage

Kimi K2.7 Code is a coding-focused agentic model built upon Kimi K2.6, with substantial improvements on real-world long-horizon coding tasks while reducing thinking-token usage by approximately 30%.

visiontoolsthinkingcloudCommunity r/kimi

Context window256K

ModalitiesText, Image

Size1.04T

Pulls226.7K

Tags1

Updated2 months ago

ollama run kimi-k2.7-code:cloud

Key features

Long-horizon coding — gains on realistic, end-to-end software engineering tasks across 10+ programming languages and a full production tech stack: backend services, infrastructure, performance engineering, systems programming, security, frontend, and ML/data engineering.
Improved token efficiency — reduces thinking-token usage by ~30% vs. Kimi K2.6 while improving task completion on complex workflows.
Stronger agentic tool use — improved multi-step tool calling and MCP-based environments, with interleaved thinking preserved across turns (preserve_thinking).
Native multimodal — image and video input via the MoonViT vision encoder, 256K context.

Benchmark highlights — vs. Kimi K2.6

Benchmark	K2.7 Code	K2.6
Kimi Code Bench v2	62.0	50.9
Program Bench	53.6	48.3
MLS Bench Lite	35.1	26.7
Kimi Claw 24/7 Bench	46.9	42.9
MCP Atlas	76.0	69.4
MCP Mark Verified	81.1	72.8

Best practices

Token efficiency — ~30% fewer thinking tokens than K2.6 makes it well-suited to high-volume coding agents.
preserve_thinking — keep interleaved thinking across turns for coherent multi-step coding sessions.
MCP environments — strong multi-step tool calling in MCP-based setups.

Specs sourced from ollama.com/library/kimi-k2.7-code · Retrieved 2026-08-29