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MiniMax · model passport

MiniMax: MiniMax M2.7

minimax-m2-7
  • Open weights
  • Modified MIT licence
  • 228.7B params
  • 204,800 context
  • Released 2026-04-09(HF repo created)
9/27axes covered
Overall · LMArena
1,415#128 of 396
Best percentile
92.6IFBench (Artificial Analysis)
Cohort standing
60.2median 62.7

What the data says

Benchmarked on 9 of 27 tracked axes. Every figure below is the model's own published result on that board.

Strongest axes

  • 92.6 pct — IFBench (Artificial Analysis) (75.7%)

Uneven: 60.6 percentile points between its best and worst axis — 92.6 pct on IFBench (Artificial Analysis) versus 32 pct on Factuality Arena.

  • IFBench (Artificial Analysis)92.6 pct75.7%Verify →
  • Coding74.9 pct1,480Verify →
  • Hard prompts70.7 pct1,441Verify →
  • Instruction following69.7 pct1,408Verify →
  • LiveBench69.4 pct63.5%Verify →
  • Overall67.9 pct1,415Verify →
  • Terminal-Bench56.8 pct45.1%Verify →
  • WebDev Arena38.8 pct1,399Verify →
  • Factuality Arena32 pct1,422Verify →
  • Vision 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
  • 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

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

RepositoryMiniMaxAI/MiniMax-M2.7
Parameters228.7B
ArchitectureMiniMaxM2ForCausalLM
LicenceModified MIT

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "MiniMaxAI/MiniMax-M2.7"
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 IDminimax/minimax-m2.7
Context length204,800 tokens
Max output131,072 tokens
Input price$0.30 per 1M tokens
Cached input price$0.06 per 1M tokens
Output price$1.20 per 1M tokens

API parameters this endpoint supports

Frequency penaltyInclude reasoningLogit biasMax tokensMin-pPresence penaltyReasoningRepetition penaltyResponse formatSeedStop 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":"minimax/minimax-m2.7","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.

  • minimax-m2.7 stands at 60.2 (raw mean 63.7) — measured on 9 of 27 axes.

    standingtemplateVerify →

  • minimax-m2.7 stands at 60.2 versus a cohort median of 62.7 across 115 models released within 180 days — below 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 →