whatmodel.sh / Olmo 3.1 32B Instruct

Olmo 3.1 32B Instruct

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Olmo 3.1 32B Instruct is an open-weights language model from Ai2, released December 2025. Its published size is 32.2B parameters and a 66k-token context window. Licence: apache-2.0.

Catalogue identity notes

Arena entry 'olmo-3.1-32b-instruct' (text). Maker repo allenai/Olmo-3.1-32B-Instruct @ac0587e: licence tag apache-2.0. 32,233,522,176 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2025-12: month the maker's Hugging Face repo was created (createdAt 2025-12-10).

54.7±2.0#272 of 719 LLMsfrom 4 benchmark results across 3 sources

$ how it compares

The LLMs ranked either side of Olmo 3.1 32B Instruct, out of 828 with a score, then more from Ai2.

#modelscorewhereRAMreleased
324Command A Reasoning (08-2025) Cohere55±2local~34GB
325Bielik 11B v3.0 Instruct SpeakLeash55±2local~8.1GB2025-11
326Olmo 3.1 32B Instruct Ai255±2local~21GB2025-12
327OpenThinker 32B Open Thoughts55±1local~21GB2025-02
328Magistral Small 1.1 (2507) Mistral55±2local~16GB2025-07
more from Ai2
259Olmo 3.1 32B Think Ai258±1local~21GB2025-12
262Olmo 3 32B Think Ai258±1local~21GB2025-11
314Molmo 2 8B Ai255±2local~6.8GB2025-12

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #183 of 393 * · 35% of its score · 1 test

Beats Seed-OSS 36B Instruct, Nemotron-Cascade-14B-Thinking, Granite 4.2 8B here, though they rank higher overall. Trails LFM2.5 1.2B Instruct, LFM2.5 1.2B Thinking, Gemma 3 12B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LMArena (text)1329.2 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 168/379avg 1268▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 35%blunt
other tests3 not in a task · 63% of its score
testresultvs the others in its rundifficultycountswhy
Medmarks-V mean win rate (19 verifiable medical benchmarks, 71 configurations)0.4560 ✎▁▄▃▃▇▄█▃▅▁▁▁ beats 11/30avg 0.423▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 54%sharp
EuroEval HellaSwag (English), test split, 5-shot generative MC, accuracy76.7% ✎▃·▁▁▂▁▂▅█▇▄▁ beats 64/100medium · avg 68%▇▇ 5.5%at its level · noisy
EuroEval Life in the UK (English knowledge MC), test split, 5-shot, accuracy84.5% ✎▁▁▁▁▁▁▁▂▄█▄▃ beats 96/146easy · avg 83%▇ 3.1%noisy

$ where to run it

llama.cpp · recommended

version

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run Olmo 3.1 32B Instruct · needs ~19.5 GB · from huggingface.co/lmstudio-community
    chat in the terminal
    llama cli -hf lmstudio-community/Olmo-3.1-32B-Instruct-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf lmstudio-community/Olmo-3.1-32B-Instruct-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run Olmo 3.1 32B Instruct · needs ~19.5 GB · from huggingface.co/lmstudio-community

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/lmstudio-community/Olmo-3.1-32B-Instruct-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Olmo 3.1 32B Instruct · needs ~19.82 GB · from ollama.com
    chat in the terminal
    ollama run olmo-3.1:32b-instruct-q4_K_M

MLX (Mac)

version

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run Olmo 3.1 32B Instruct · needs ~18.1 GB, Mac only · from huggingface.co/lmstudio-community
    chat in the terminal
    mlx_lm.chat --model lmstudio-community/Olmo-3.1-32B-Instruct-MLX-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model lmstudio-community/Olmo-3.1-32B-Instruct-MLX-4bit

Weights and documentation: huggingface.co/allenai ↗

$ how the score was worked out · Medmarks: A Comprehensive Ope… 54%, LMArena leaderboard dataset 35%, EuroEval leaderboards (result… 9%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks — authors' own runs of 61 models1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 54%CC BY 4.0
LMArena leaderboard dataset1▇▇▇▇▇▇▇▇▇▇▇ 35%CC BY 4.0
EuroEval leaderboards (results collection), English generative leaderboard2▇▇▇ 9%MIT
prior · pull toward the middle▇ 2%
weighted average= 54.7

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json