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Moonshot AI · Kimi
No. 34

Kimi K2.7

Open-weight coding refresh of Kimi K2 — stronger agentic tool use at low cost.

OpenOpen weightsText, Image, Video
Field notes
Context window
262Ktokens
Max output
33Ktokens
Input price
$0.95/ 1M tokens
Output price
$4/ 1M tokens
Cached input
$0.19/ 1M tokens
Modalities
Text, Image, Video
Knowledge cutoff
Apr 2025
Released
Jun 2026
Parameters
1000B
License
Modified MIT
Approx. speed
—
Overview

What this model is

Kimi K2.7 (shipped as K2.7-Code) is Moonshot's June-2026 coding-focused refresh of K2.6 — the same 1T-parameter open-weight MoE (32B active, Modified MIT) with forced thinking, retuned for agentic software engineering and MCP tool workflows while using roughly 30% fewer thinking tokens. Moonshot reports gains over K2.6 across its coding suites and an MCP-tool score edging Opus 4.8, at roughly one-seventh the token cost. Independent third-party benchmarks were not yet available at launch, so its coding claims are vendor-reported — validate on your own tasks; the Claude/GPT frontier still leads proven end-to-end reliability.

Strengths

  • Among the strongest open-weight agentic coders and MCP tool users
  • Roughly one-seventh the token cost of the closed frontier
  • 256K context; 1T MoE with only 32B active
  • Modified MIT open weights, self-hostable
  • ~30% more token-efficient thinking than K2.6

Trade-offs

  • Only vendor benchmarks published so far (no independent runs)
  • Long-horizon reliability still trails Claude/GPT
  • Large 1T MoE — serving cost and latency
  • 32K output cap

Best for

  • Cost-efficient agentic coding within a supervised loop
  • MCP and tool-use workflows
  • Self-hosted agent stacks
  • Long-context repository work

Not ideal for

  • Unsupervised mission-critical autonomous coding
  • Workloads needing independently-verified benchmarks
Capabilities

Capability profile

Normalized 0–100 scores, comparable across the whole catalog.

Reasoning84Coding78Math86Writing82Knowledge84Speed55Agentic78Vision55Multilingual80Long Context84
Reasoning
84
Coding
78
Math
86
Writing
82
Knowledge
84
Speed
55
Agentic
78
Vision
55
Multilingual
80
Long Context
84
Benchmarks

How it scores

Public benchmark results, with independent third-party results where available. Bars normalize percentages to 100 and Elo ratings to a 1500 ceiling.

No public benchmark scores recorded for this model.

Alternatives

Comparable models

Models in a roughly similar class — worth weighing against this one.

Kimi K2.6Moonshot AI

Open-weight agentic powerhouse with long context and video input.

GLM-5.2Zhipu AI

Open-weight coding flagship at a fraction of frontier cost.

DeepSeek-V4 ProDeepSeek

Open MIT flagship — 1.6T-parameter MoE with native vision and a 1M-token context at throwaway prices.