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Kimi · model passport

MoonshotAI: Kimi K2 0711

kimi-k2
  • Open weights
  • 1.03T params
  • 131,072 context
  • Released 2025-07-11(curated)
3/27axes covered
Best percentile
80.9MMLU-Pro (Artificial Analysis)
Cohort standing
56.0median 51.6

What the data says

Benchmarked on 3 of 27 tracked axes. Every figure below is the model's own published result on that board.

Uneven: 36.5 percentile points between its best and worst axis — 80.9 pct on MMLU-Pro (Artificial Analysis) versus 44.4 pct on IFBench (Artificial Analysis).

  • MMLU-Pro (Artificial Analysis)80.9 pct82.4%Verify →
  • LiveCodeBench (Artificial Analysis)60.7 pct55.6%Verify →
  • IFBench (Artificial Analysis)44.4 pct41.5%Verify →
  • OverallNot benchmarked
  • WebDev ArenaNot benchmarked
  • Vision ArenaNot benchmarked
  • Factuality ArenaNot benchmarked
  • Search ArenaNot benchmarked
  • Document ArenaNot benchmarked
  • Agent ArenaNot benchmarked
  • GPQA DiamondNot benchmarked
  • AIME (math)Not benchmarked
  • FrontierMathNot benchmarked
  • SimpleQA VerifiedNot benchmarked
  • ARC-AGI-2Not benchmarked
  • Humanity's Last ExamNot benchmarked
  • SimpleBenchNot benchmarked
  • LiveBenchNot benchmarked
  • BFCL v4 (function calling)Not benchmarked
  • SWE-bench Verified (live board)Not benchmarked
  • Terminal-Bench 4.0Not 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-Instruct
Parameters1.03T
ArchitectureDeepseekV3ForCausalLM

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "moonshotai/Kimi-K2-Instruct"
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
Context length131,072 tokens
Max output98,304 tokens
Input price$0.57 per 1M tokens
Output price$2.30 per 1M tokens

API parameters this endpoint supports

Frequency penaltyMax tokensPresence penaltyRepetition penaltySeedStop sequencesTemperatureTool choiceTool callingTop-kTop-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","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 stands at 56 (raw mean 62) — measured on 3 of 27 axes.

    standingtemplateVerify →

  • Kimi K2 stands at 56 versus a cohort median of 51.6 across 143 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 →