Model
Model explorer

Kimi K2.7 Code

OPEN
Moonshot AI · Kimi K2 family · released Jun 12, 2026

Coding/agent-specialized variant built on the Kimi K2.6 base weights, tuned for long-horizon software-engineering workflows and thousands of sequential tool calls, with ~30% fewer thinking tokens than K2.6 for equivalent tasks. A same-weights 'HighSpeed' serving SKU (kimi-k2.7-code-highspeed) also shipped, at roughly 2x the price ($1.90/M input cache-miss, $8.00/M output) for ~180-260 tok/s output — this is a serving/throughput tier, not a reasoning-effort tier, so it is not modeled as a separate entry here. All benchmark improvements Moonshot has published for this model to date are on Moonshot's own proprietary/in-house suites (Kimi Code Bench v2, Program Bench, MLS Bench Lite, Kimi Claw 24/7 Bench, MCP Atlas, MCP Mark Verified); no standard public-benchmark numbers (SWE-bench, LiveCodeBench, AIME, GPQA) have been disclosed for K2.7 Code specifically, and no independent third-party re-run results were found as of this research.

ReasoningCodingVisionFunction callingTool useAgentic
2232.7
Elo · rank #42
Parameters
1000B
Active params
32B (MoE)
Context
256K tokens
Architecture
1T-parameter MoE, 32B active/token, 384 experts (8 routed + 1 shared), 61 layers (1 dense), MLA attention, native INT4 quantization support; coding-specialized post-train of the K2.6 base with forced/persistent thinking mode across multi-turn agent sessions
License
Modified MIT License
Languages
API price (in/out)
$0.95 / $4
Modalities
text · vision · video
Benchmark results
Bar shows position within the tracked field; marker = field best
GDPval-AAAgents1186.79#28
best: Claude Fable 5 · 1932
GPQA DiamondReasoning90.0%#27
best: GPT-5.6 · 94.6%
Humanity's Last ExamReasoning33.0%#41
best: Claude Sonnet 5 · 57.4%
IFBenchReasoning63.0%#29
best: MiniMax M3 · 83.0%
MCP AtlasAgents76.0%#3
best: Kimi K3 · 84.2%
τ²-Bench TelecomAgents90.0%#20
best: Claude Opus 4.6 · 99.3%
Terminal-Bench 2.0Coding67.0%#21
best: GPT-5.6 · 88.8%
Run it locally
VRAM @ Q4
VRAM @ FP16
Fits on (Q4)
Multi-node cluster required
Throughput data unavailable.
Quantizations
API price $0.95/$4 · each benchmark row carries its own source badge (see methodology)