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Allenai · model passport

Olmo 3 1 32b Instruct

olmo-3-1-32b-instruct
  • Open weights
  • Apache 2.0 licence
  • 32.2B params
  • Released 2025-12-10(HF repo created)
6/27axes covered
Overall · LMArena
1,329#226 of 396
Best percentile
45.2Instruction following
Cohort standing
41.2median 54.9

What the data says

Benchmarked on 6 of 27 tracked axes. Every figure below is the model's own published result on that board.

Weakest axes

  • 3.5 pct — Factuality Arena (1,342)

Uneven: 41.7 percentile points between its best and worst axis — 45.2 pct on Instruction following versus 3.5 pct on Factuality Arena.

  • Instruction following45.2 pct1,321Verify →
  • Hard prompts45.2 pct1,349Verify →
  • Coding45 pct1,381Verify →
  • Overall43.2 pct1,329Verify →
  • IFBench (Artificial Analysis)38.9 pct39.2%Verify →
  • Factuality Arena3.5 pct1,342Verify →
  • WebDev ArenaNot benchmarked
  • 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
  • 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

Repositoryallenai/Olmo-3.1-32B-Instruct
Parameters32.2B
ArchitectureOlmo3ForCausalLM
LicenceApache 2.0

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "allenai/Olmo-3.1-32B-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 →

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.

  • olmo-3.1-32b-instruct stands at 41.2 (raw mean 36.8) — measured on 6 of 27 axes.

    standingtemplateVerify →

  • olmo-3.1-32b-instruct stands at 41.2 versus a cohort median of 54.9 across 124 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 →