[whatmodel.sh](../../README.md) / Ministral 3 3B Reasoning 2512
# Ministral 3 3B Reasoning 2512
Mistral · released 2025-12 · 3.8B params · apache-2.0
**score 50.1** ±1.0 · #368 of 732 · from 12 benchmark results across 4 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Ministral 3 3B Reasoning 2512 among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇█●▅▄▃▁ #201 of 469* 8.1% 1
math ●·····█····█ #3 of 3 39% 2
coding · #201 of 469 · 8.1% of its score
Beats [Gemini 2.5 Flash](gemini-2-5-flash.md), [Qwen3 30B-A3B Instruct 2507](qwen3-30b-a3b-instruct-2507.md), [Qwen3.5 4B](qwen3-5-4b.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LiveCodeBench (version unspecifie… 54.8% ▁···▁▁▁█●▃▄▁ beats 20/38 medium · avg 49% 8.1% at its level · small run · several runs
math · #3 of 3 · 39% of its score
test result vs the others in its run difficulty counts why
AIME 2025 (maker) 72.1% ▁·▁▁▁▂▅▆●▇▅▁ beats 68/133 easy · avg 67% 20% sharp · at its level · small run · seve…
AIME 2024 (maker) 77.5% ▁▁▁▂▁▃▁▅█●█▃ beats 50/85 easy · avg 75% 19% sharp · small run · several runs
other tests (5)
test result vs the others in its run difficulty counts why
GPQA Diamond (maker) 53.4% ▁▁▁▁▂▄▅●███▄ beats 95/322 medium · avg 53% 23% sharp · at its level · small run · seve…
Medmarks-V mean win rate (19 veri… 0.3741 ▁●▃▃▇▄█▃▅▁▁▁ beats 3/30 avg 0.418 18% sharp
Medmarks-V weighted mean win rate… 37.4% ▂▃●▂▆▃█▅█▅▃▆ beats 3/34 medium · avg 41% 8.0% blunt · at its level
EuroEval HellaSwag (English), tes… 75.6% ▃·▁▁▂▁▂▅●▇▄▁ beats 61/100 medium · avg 67% 1.9% sharp · at its level · noisy
EuroEval Life in the UK (English … 78.1% ▁▁▁▁▁▁▁▂●█▄▃ beats 60/146 easy · avg 82% 1.2% noisy
source results share of the score
mistralai/Ministral-3-3B-Reasoning-2512… 4 35% ▇▇▇▇
mistralai/Ministral-3-8B-Reasoning-2512… 4 35% ▇▇▇▇
Medmarks: A Comprehensive Open-Source L… 2 26% ▇▇▇
EuroEval leaderboards 2 3% ▇
prior: pull toward the middle 0% ▇
weighted average = 50.1
Licences: mistralai/Ministral-3-3B-Reasoning-2512 model card ([Apache-2.0](../../licenses/cards-gap-NOTICE.txt)) · mistralai/Ministral-3-8B-Reasoning-2512 model card ([Apache-2.0](../../licenses/cards-gap-NOTICE.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)) · 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 Ministral 3 3B Reasoning 2512 (Q4_K_M, needs 2.15 GB, from [huggingface.co/mistralai](https://huggingface.co/mistralai/Ministral-3-3B-Reasoning-2512-GGUF/blob/b432f0a2c144af8a1937b06e2ea7f75ebb4d0b31/Ministral-3-3B-Reasoning-2512-Q4_K_M.gguf))
```sh
llama cli -hf mistralai/Ministral-3-3B-Reasoning-2512-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mistralai/Ministral-3-3B-Reasoning-2512-GGUF:Q4_K_M
```
### 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 Ministral 3 3B Reasoning 2512 (Q4_K_M, needs 2.15 GB, from [huggingface.co/mistralai](https://huggingface.co/mistralai/Ministral-3-3B-Reasoning-2512-GGUF/blob/b432f0a2c144af8a1937b06e2ea7f75ebb4d0b31/Ministral-3-3B-Reasoning-2512-Q4_K_M.gguf))
```sh
lms get https://huggingface.co/mistralai/Ministral-3-3B-Reasoning-2512-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: 4bit (~1.9 GB, Mac only) `lms get https://huggingface.co/alexgusevski/Ministral-3-3B-Reasoning-2512-q4-mlx -y` · 8bit (~3.6 GB, Mac only) `lms get https://huggingface.co/alexgusevski/Ministral-3-3B-Reasoning-2512-q8-mlx -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 Ministral 3 3B Reasoning 2512 (Q4_K_M, needs 2.15 GB, from [huggingface.co/mistralai](https://huggingface.co/mistralai/Ministral-3-3B-Reasoning-2512-GGUF/blob/b432f0a2c144af8a1937b06e2ea7f75ebb4d0b31/Ministral-3-3B-Reasoning-2512-Q4_K_M.gguf))
```sh
ollama run hf.co/mistralai/Ministral-3-3B-Reasoning-2512-GGUF:Q4_K_M
```
### 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 Ministral 3 3B Reasoning 2512 (4bit, needs ~1.9 GB, Mac only, from [huggingface.co/alexgusevski](https://huggingface.co/alexgusevski/Ministral-3-3B-Reasoning-2512-q4-mlx/tree/74b5f9908cabf7f17b17fbb7a81f7a84d0134163))
```sh
mlx_lm.chat --model alexgusevski/Ministral-3-3B-Reasoning-2512-q4-mlx
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model alexgusevski/Ministral-3-3B-Reasoning-2512-q4-mlx
```
Other versions: 8bit (~3.6 GB, Mac only) `mlx_lm.chat --model alexgusevski/Ministral-3-3B-Reasoning-2512-q8-mlx`
Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Ministral-3-3B-Reasoning-2512)
[interactive version](https://whatmodel.sh/model/ministral-3-3b-reasoning) · [JSON](https://whatmodel.sh/api?format=json&all=1)