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MoonshotAI: Kimi K2.7 Code

kimi-k2-7-code
  • Open weights
  • Modified MIT licence
  • 1.03T params
  • 262,144 context
  • Released 2026-06-11(HF repo created)
7/27axes covered
Best percentile
81.5AIME (math)
Cohort standing
61.9median 59.8

What the data says

Benchmarked on 7 of 27 tracked axes. Every figure below is the model's own published result on that board.

Uneven: 42.5 percentile points between its best and worst axis — 81.5 pct on AIME (math) versus 39 pct on SimpleQA Verified.

  • AIME (math)81.5 pct95.6%Verify →
  • LiveBench78.4 pct68.4%Verify →
  • GPQA Diamond75.6 pct87.9%Verify →
  • IFBench (Artificial Analysis)71.9 pct63.1%Verify →
  • SimpleBench65.5 pct57.9%Verify →
  • WebDev Arena57.6 pct1,474Verify →
  • SimpleQA Verified39 pct36.5%Verify →
  • OverallNot benchmarked
  • Vision ArenaNot benchmarked
  • Factuality ArenaNot benchmarked
  • Search ArenaNot benchmarked
  • Document ArenaNot benchmarked
  • Agent ArenaNot benchmarked
  • FrontierMathNot benchmarked
  • ARC-AGI-2Not benchmarked
  • Humanity's Last ExamNot benchmarked
  • BFCL v4 (function calling)Not benchmarked
  • SWE-bench Verified (live board)Not benchmarked
  • Terminal-Bench 4.0Not benchmarked
  • MMLU-Pro (Artificial Analysis)Not benchmarked
  • LiveCodeBench (Artificial Analysis)Not benchmarked
  • HHEM Hallucination (Vectara)Not benchmarked
  • SWE-bench VerifiedNot benchmarked
  • Terminal-BenchNot benchmarked
  • CodingNot benchmarked
  • Instruction followingNot benchmarked
  • Hard promptsNot benchmarked

Percentiles are the model's standing within each board's own population. Axes marked "not benchmarked" have no published result for this model — that is an absence of data, not a low score.

How to run it

Run it yourself

Repositorymoonshotai/Kimi-K2.7-Code
Parameters1.03T
ArchitectureKimiK25ForConditionalGeneration
LicenceModified MIT

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "moonshotai/Kimi-K2.7-Code"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

Ollama

We don't sync Ollama tags, so we won't print a command we can't verify — a wrong one would pull the wrong model. Search the Ollama library →

Unsloth fine-tuning recipes are model-specific and we don't derive them here. Unsloth docs →

Use it hosted

API model IDmoonshotai/kimi-k2.7-code
Context length262,144 tokens
Max output235,929 tokens
Acceptstext, image
Input price$0.67 per 1M tokens
Cached input price$0.18 per 1M tokens
Output price$3.35 per 1M tokens

API parameters this endpoint supports

Frequency penaltyInclude reasoningLogit biasLogprobsMax tokensMin-pParallel Tool CallsPresence penaltyReasoningRepetition penaltyResponse formatSeedStop sequencesStructured outputsTemperatureTool choiceTool callingTop-kTop logprobsTop-p

Example request

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"moonshotai/kimi-k2.7-code","messages":[{"role":"user","content":"Hello"}]}'

View on OpenRouter →

Identifiers, limits and prices are synced from the upstream source shown above. Any field we haven't synced is left out entirely rather than guessed.

  • Kimi K2.7 Code stands at 61.9 (raw mean 67.1) — measured on 7 of 27 axes.

    standingtemplateVerify →

  • Kimi K2.7 Code stands at 61.9 versus a cohort median of 59.8 across 205 models released within 180 days — above the peer median.

    cohorttemplateVerify →

Every claim is a computed fact from the current board data, phrased by a low-temperature model call and rejected by a validation gate if any number is not in the source row. How the leaderboards work →