[whatmodel.sh](../../README.md) / Olmo 3.1 32B Instruct
# Olmo 3.1 32B Instruct
Ai2 · released 2025-12 · 32.2B params · apache-2.0
**score 54.7** ±1.7 · #290 of 732 · from 4 benchmark results across 3 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Olmo 3.1 32B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃▆▄▆●▆▇▇▃ #190 of 402* 32% 1
chat & writing · #190 of 402 · 32% of its score
Beats [Seed-OSS 36B Instruct](seed-oss-36b.md), [Gemma 2 9B IT SimPO](gemma-2-9b-simpo.md), [Nemotron-Cascade-14B-Thinking](nemotron-cascade-14b-thinking.md) here, though they rank higher overall. Trails [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md), [LFM2.5 1.2B Thinking](lfm2-5-1-2b-thinking.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1329.3 ▁▁▁▂▃▃▃●▆▇█▃ beats 168/385 avg 1260 32% blunt
other tests (3)
test result vs the others in its run difficulty counts why
Medmarks-V mean win rate (19 veri… 0.4560 ▁▄▃▃●▄█▃▅▁▁▁ beats 11/30 avg 0.418 59% sharp
EuroEval HellaSwag (English), tes… 76.7% ▃·▁▁▂▁▂▅●▇▄▁ beats 64/100 medium · avg 67% 5.4% sharp · at its level · noisy
EuroEval Life in the UK (English … 84.5% ▁▁▁▁▁▁▁▂▄●▄▃ beats 96/146 easy · avg 82% 3.0% noisy
source results share of the score
Medmarks: A Comprehensive Open-Source L… 1 59% ▇▇▇▇▇▇▇
LMArena leaderboard dataset 1 32% ▇▇▇▇
EuroEval leaderboards 2 8% ▇
prior: pull toward the middle 1% ▇
weighted average = 54.7
Licences: Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks ([CC BY 4.0](../../licenses/paper-2605.01417v1-CC-BY-4.0.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Olmo 3.1 32B Instruct (Q4_K_M, needs ~19.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Olmo-3.1-32B-Instruct-GGUF/tree/14816572819f6a5a57f0151b60a6bcb9ebed2f59))
```sh
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)
```sh
llama serve -hf lmstudio-community/Olmo-3.1-32B-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~34.3 GB) `llama cli -hf lmstudio-community/Olmo-3.1-32B-Instruct-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Olmo 3.1 32B Instruct (Q4_K_M, needs ~19.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Olmo-3.1-32B-Instruct-GGUF/tree/14816572819f6a5a57f0151b60a6bcb9ebed2f59))
```sh
lms get https://huggingface.co/lmstudio-community/Olmo-3.1-32B-Instruct-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~34.3 GB) `lms get https://huggingface.co/lmstudio-community/Olmo-3.1-32B-Instruct-GGUF@Q8_0 -y` · 4bit (~18.1 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Olmo-3.1-32B-Instruct-MLX-4bit -y` · 8bit (~34.2 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Olmo-3.1-32B-Instruct-MLX-8bit -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Olmo 3.1 32B Instruct (Q4_K_M, needs ~19.82 GB, from [ollama.com](https://ollama.com/library/olmo-3.1/tags))
```sh
ollama run olmo-3.1:32b-instruct-q4_K_M
```
Other versions: Q8_0 (~39.14 GB) `ollama run olmo-3.1:32b-instruct-q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Olmo 3.1 32B Instruct (4bit, needs ~18.1 GB, Mac only, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Olmo-3.1-32B-Instruct-MLX-4bit/tree/a76cbe10949f56b8b0b2866b3c79167494c2bc60))
```sh
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
```sh
mlx_lm.server --model lmstudio-community/Olmo-3.1-32B-Instruct-MLX-4bit
```
Other versions: 8bit (~34.2 GB, Mac only) `mlx_lm.chat --model lmstudio-community/Olmo-3.1-32B-Instruct-MLX-8bit`
Model repository and documentation: [huggingface.co/allenai](https://huggingface.co/allenai/Olmo-3.1-32B-Instruct)
[interactive version](https://whatmodel.sh/model/olmo-3-1-32b-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)