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Xiaomi · model passport

Xiaomi: MiMo-V2.6-Flash

mimo-v2-6-flash
  • Open weights
  • MIT licence
  • 310.8B params
  • 1,050,000 context
  • Released 2026-09-21(HF repo created)
8/27axes covered
Overall · LMArena
1,451#68 of 396
Best percentile
89.9WebDev Arena
Cohort standing
71.1median 60.3

What the data says

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

Strongest axes

  • 89.9 pct — WebDev Arena (1,635)
  • 88.2 pct — Coding (1,513)
  • 85.6 pct — Hard prompts (1,480)

Uneven: 28.1 percentile points between its best and worst axis — 89.9 pct on WebDev Arena versus 61.8 pct on Agent Arena.

  • WebDev Arena89.9 pct1,635Verify →
  • Coding88.2 pct1,513Verify →
  • Hard prompts85.6 pct1,480Verify →
  • Instruction following84.1 pct1,444Verify →
  • Overall83.1 pct1,451Verify →
  • Factuality Arena70.3 pct1,454Verify →
  • Vision Arena68.7 pct1,248Verify →
  • Agent Arena61.8 pct0.026Verify →
  • Search ArenaNot benchmarked
  • Document 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
  • IFBench (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-V2.6-Flash-RL
Parameters310.8B
ArchitectureMiMoV2ForCausalLM
LicenceMIT

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "XiaomiMiMo/MiMo-V2.6-Flash-RL"
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-v2.6-flash
Context length1,050,000 tokens
Max output131,072 tokens
Acceptstext, image, video, audio
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 reasoningLogit biasLogprobsMax tokensMin-pPresence 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":"xiaomi/mimo-v2.6-flash","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-v2.6-flash stands at 71.1 (raw mean 79) — measured on 8 of 27 axes.

    standingtemplateVerify →

  • mimo-v2.6-flash stands at 71.1 versus a cohort median of 60.3 across 176 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 →