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Xiaomi · model passport

Xiaomi: MiMo-V2.5

mimo-v2-5
  • Open weights
  • MIT licence
  • 310.8B params
  • 1,050,000 context
  • Released 2026-04-27(HF repo created)
8/27axes covered
Overall · LMArena
1,434#96 of 396
Best percentile
79.5Hard prompts
Cohort standing
63.7median 59.5

What the data says

Benchmarked on 8 of 27 tracked axes. Every figure below is the model's own published result on that board.

Uneven: 28.9 percentile points between its best and worst axis — 79.5 pct on Hard prompts versus 50.6 pct on Factuality Arena.

  • Hard prompts79.5 pct1,462Verify →
  • Instruction following79 pct1,431Verify →
  • Coding78.5 pct1,491Verify →
  • IFBench (Artificial Analysis)76.9 pct67.1%Verify →
  • Overall76 pct1,434Verify →
  • Vision Arena59.3 pct1,234Verify →
  • WebDev Arena51.1 pct1,438Verify →
  • Factuality Arena50.6 pct1,439Verify →
  • 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
  • MMLU-Pro (Artificial Analysis)Not benchmarked
  • LiveCodeBench (Artificial Analysis)Not benchmarked
  • HHEM Hallucination (Vectara)Not benchmarked
  • SWE-bench VerifiedNot benchmarked
  • Terminal-BenchNot 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

RepositoryXiaomiMiMo/MiMo-V2.5
Parameters310.8B
ArchitectureMiMoV2ForCausalLM
LicenceMIT

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "XiaomiMiMo/MiMo-V2.5"
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 IDxiaomi/mimo-v2.5
Context length1,050,000 tokens
Max output131,072 tokens
Acceptstext, audio, image, video
Input price$0.14 per 1M tokens
Cached input price$0.00 per 1M tokens
Output price$0.28 per 1M tokens

API parameters this endpoint supports

Frequency penaltyInclude reasoningMax tokensPresence penaltyReasoningRepetition penaltyResponse formatSeedStop sequencesStructured outputsTemperatureTool 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":"xiaomi/mimo-v2.5","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.

  • mimo-v2.5 stands at 63.7 (raw mean 68.9) — measured on 8 of 27 axes.

    standingtemplateVerify →

  • mimo-v2.5 stands at 63.7 versus a cohort median of 59.5 across 221 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 →