[whatmodel.sh](../../README.md) / Olmo 3 7B Think
# Olmo 3 7B Think
Ai2 · released 2025-11 · 7.3B params · apache-2.0
**score 52.9** ±0.8 · #319 of 732 · from 25 benchmark results across 5 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 7B Think among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇●▇▅▄▃▁ #240 of 469 9.5% 4
chat & writing ▁▁▁▃▆▄▆●▆▇▇▃ #202 of 402 4.8% 2
math ▁▁▃▅█▆●█▄▂▁▁ #157 of 431 27% 4
coding · #240 of 469 · 9.5% of its score
Beats [Qwen3.5 4B](qwen3-5-4b.md), [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [InternLM2.5 20B Chat](internlm2-5-20b-chat.md), [Ministral 3 3B Reasoning 2512](ministral-3-3b-reasoning.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MBPP+ (maker) 64.7% ▄▄····●·▄··█ beats 2/5 medium · avg 61% 2.5% at its level
HumanEval+ (maker) 89.9% ▂·······▂·▂● beats 4/6 easy · avg 78% 2.3% sharp · noisy
LiveCodeBench v6 (window unspecif… 59.8% ▂▃▄▄▅●█▆▄▇▄▆ beats 24/67 medium · avg 42% 2.3% at its level
LiveCodeBench v3 (maker) 75.2% ▃▃·····▃·●·█ beats 3/6 medium · avg 64% 2.3% at its level
chat & writing · #202 of 402 · 4.8% of its score
Beats [Gemma 2 9B IT SimPO](gemma-2-9b-simpo.md), [Granite 4.2 8B](granite-4-2-8b.md), [Mistral Small 4 (119B-A6.5B)](mistral-small-4.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
AlpacaEval 2 length-controlled (m… 52.1% █···█·●·█·██ beats 2/5 medium · avg 43% 3.7% sharp · at its level
IFEval (aggregation unspecified) … 88.2% ▄▄·····█●▄·▄ beats 4/6 easy · avg 75% 1.1% noisy · several runs
math · #157 of 431 · 27% of its score
Beats [MiMo-V2.5 (310B-A15B)](mimo-v2-5.md), [MiniMax-M2.7](minimax-m2-7.md), [MiniMax-M2.1](minimax-m2-1.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
AIME 2025 (maker) 64.6% ▄··▄····█●▄▄ beats 4/6 medium · avg 50% 10% sharp · at its level
AIME 2024 (maker) 71.6% ▄·····▄··▄●█ beats 3/6 medium · avg 58% 7.8% sharp · at its level
MATH (subset unspecified) (maker) 95.1% ▃·····▃···▅● beats 5/6 very easy · avg 91% 5.4% maxed out
OMEGA (math generalisation) (make… 37.8% ▄······●▄▄·█ beats 1/6 hard · avg 31% 4.2% at its level
other tests (14)
test result vs the others in its run difficulty counts why
Medmarks-V mean win rate (19 veri… 0.4130 ▁▄▃●▇▄█▃▅▁▁▁ beats 6/30 avg 0.418 12% sharp
GPQA (subset unspecified) (maker) 46.2% ▃······▃·●·█ beats 2/6 hard · avg 43% 9.1% sharp · at its level
BIG-Bench Hard (maker) 86.6% ▄·····▄·▄·●█ beats 4/6 easy · avg 77% 8.2% sharp
MMLU (maker) 77.8% ▄···▄··●··▄█ beats 3/6 easy · avg 76% 6.0% at its level
Medmarks-V weighted mean win rate… 41.3% ▂▃▅●▆▃█▅█▅▃▆ beats 6/34 medium · avg 41% 5.2% blunt · at its level
AGIEval English (maker) 81.5% ▄····▄·●▄··█ beats 2/5 easy · avg 76% 3.4% typical
ZebraLogic (maker) 66.5% █▄····▄·●·▄▄ beats 4/6 medium · avg 52% 2.8% at its level
MMLU-Redux 71.3% ▁▁●▁▂▃▄▅▇▅█▄ beats 2/63 easy · avg 76% 2.7% at its level
GPQA Diamond 29.3% ▁▁▁●▂▄▅▅███▄ beats 17/322 hard · avg 41% 2.6% sharp · at its level · noisy
PopQA (maker) 23.7% ▄··█···●···█ beats 3/5 hard · avg 20% 2.1% typical
IFBench (aggregation unspecified)… 41.6% █·██·●█····█ beats 3/5 medium · avg 36% 1.5% at its level
EuroEval HellaSwag (English), tes… 60.4% ▃·▁▁▂▁▂●█▇▄▁ beats 35/100 medium · avg 67% 1.2% sharp · at its level · noisy
GSM-Plus 88.1% ▁··▁▁▁·▃▁▄●▁ beats 12/16 easy · avg 84% 1.2% blunt
EuroEval Life in the UK (English … 85.7% ▁▁▁▁▁▁▁▂▄●▄▃ beats 100/146 easy · avg 82% 0.7% noisy
source results share of the score
Olmo 3 (submitted / maker-reported) 14 62% ▇▇▇▇▇▇▇
Medmarks: A Comprehensive Open-Source L… 2 17% ▇▇
allenai/Olmo-3-7B-Think model card (sub… 2 10% ▇
JetBrains/Mellum2-12B-A2.5B-Thinking mo… 5 9% ▇
EuroEval leaderboards 2 2% ▇
prior: pull toward the middle 0% ▇
weighted average = 52.9
Licences: Olmo 3 ([CC BY 4.0](../../licenses/report-2512.13961v2.txt)) · Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks ([CC BY 4.0](../../licenses/paper-2605.01417v1-CC-BY-4.0.txt)) · allenai/Olmo-3-7B-Think model card ([Apache-2.0](../../licenses/allenai--Olmo-3-7B-Think.txt)) · JetBrains/Mellum2-12B-A2.5B-Thinking model card ([Apache-2.0](https://huggingface.co/JetBrains/Mellum2-12B-A2.5B-Thinking/blob/a7311550557e93cc706ab5dd3d879c1a11703ab4/README.md)) · 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 7B Think (Q4_K_M, needs ~4.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Olmo-3-7B-Think-GGUF/tree/e3a92c418177c9541a5b51980fcd1a69b33ce2a4))
```sh
llama cli -hf lmstudio-community/Olmo-3-7B-Think-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-3-7B-Think-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.8 GB) `llama cli -hf lmstudio-community/Olmo-3-7B-Think-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 7B Think (Q4_K_M, needs ~4.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Olmo-3-7B-Think-GGUF/tree/e3a92c418177c9541a5b51980fcd1a69b33ce2a4))
```sh
lms get https://huggingface.co/lmstudio-community/Olmo-3-7B-Think-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 (~7.8 GB) `lms get https://huggingface.co/lmstudio-community/Olmo-3-7B-Think-GGUF@Q8_0 -y` · 4bit (~4.1 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Olmo-3-7B-Think-MLX-4bit -y` · 8bit (~7.8 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Olmo-3-7B-Think-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 7B Think (Q4_K_M, needs ~4.88 GB, from [ollama.com](https://ollama.com/library/olmo-3/tags))
```sh
ollama run olmo-3:7b-think
```
Other versions: Q8_0 (~9.26 GB) `ollama run olmo-3:7b-think-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 7B Think (4bit, needs ~4.1 GB, Mac only, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Olmo-3-7B-Think-MLX-4bit/tree/b569670081a34063fc8493cc419524e7773a94a4))
```sh
mlx_lm.chat --model lmstudio-community/Olmo-3-7B-Think-MLX-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model lmstudio-community/Olmo-3-7B-Think-MLX-4bit
```
Other versions: 8bit (~7.8 GB, Mac only) `mlx_lm.chat --model lmstudio-community/Olmo-3-7B-Think-MLX-8bit`
Model repository and documentation: [huggingface.co/allenai](https://huggingface.co/allenai/Olmo-3-7B-Think)
[interactive version](https://whatmodel.sh/model/olmo-3-7b-think) · [JSON](https://whatmodel.sh/api?format=json&all=1)