[whatmodel.sh](../../README.md) / Magistral Small 1.0 (2506)
# Magistral Small 1.0 (2506)
Mistral · released 2025-06 · 23.57B params · apache-2.0
**score 55.2** ±1.1 · #277 of 732 · from 8 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 Magistral Small 1.0 (2506) among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁●▆█▄▅▅▄▄▂▁ #288 of 313* 2.5% 1
coding ●█········██ #4 of 4* 6.1% 1
math ▁▁▃▅█●▄█▄▂▁▁ #211 of 431 43% 3
agent tasks · #288 of 313 · 2.5% of its score
Trails [Granite 4.0 H 350M](granite-4-0-h-350m.md), [Qwen3 0.6B](qwen3-0-6b.md), [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Galileo Agent Leaderboard v2 (act… 16.0% ●▃·▃▃▅▅█▃█▅▅ beats 1/19 hard · avg 26% 2.5% at its level
coding · #4 of 4 · 6.1% of its score
Trails [Magistral Small 1.1 (2507)](magistral-small-2507.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LiveCodeBench v5 (window unspecif… 55.8% ●█▄······▄·▄ beats 0/5 medium · avg 50% 6.1% at its level
math · #211 of 431 · 43% of its score
Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md), [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md) here, though they rank higher overall. Trails [Granite 8B Code Base (4K)](granite-8b-code-base-4k.md), [Mixtral 8x7B v0.1 (base)](mixtral-8x7b-v0-1.md), [Olmo 3 7B Instruct](olmo-3-7b-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AIME 2025 (maker) 62.8% ●▃·····▃···▃ beats 2/5 medium · avg 48% 21% sharp · at its level
AIME 2024 (maker) 70.7% ●▃·····▃···▃ beats 1/5 medium · avg 60% 15% sharp · at its level
OTIS mock AIME 2024–2025 30.0% ▃▃▄▃▃●▇▇█▃▂▅ beats 64/197 very hard · avg 7.2% 7.1% sharp · noisy
other tests (2)
test result vs the others in its run difficulty counts why
GPQA Diamond 56.1% ▁▁▁▁▂▄▆●▅▇█▅ beats 80/211 medium · avg 52% 40% sharp · at its level · several runs
BRIDGE clinical text, zero-shot a… 37.6% ▁▁▁▁▁▂▂▃▄●█▃ beats 58/97 hard · avg 31% 8.0% blunt · at its level
source results share of the score
mistralai/Magistral-Small-2509 model ca… 4 62% ▇▇▇▇▇▇▇
Epoch AI 2 27% ▇▇▇
BRIDGE Medical Leaderboard 1 8% ▇
Galileo Agent Leaderboard 1 3% ▇
prior: pull toward the middle 1% ▇
weighted average = 55.2
Licences: mistralai/Magistral-Small-2509 model card ([Apache-2.0](../../licenses/mistralai--Magistral-Small-2509.txt)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · BRIDGE Medical Leaderboard ([MIT](../../licenses/bridge-medical-leaderboard-MIT.txt)) · Galileo Agent Leaderboard ([Apache-2.0](../../licenses/galileo-agent-leaderboard-Apache-2.0.txt))
## 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 Magistral Small 1.0 (2506) (Q4_K_M, needs ~14.3 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/mistralai_Magistral-Small-2506-GGUF/tree/dbf2f0bcf878662f61035a89542d10bc8fdca801))
```sh
llama cli -hf bartowski/mistralai_Magistral-Small-2506-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf bartowski/mistralai_Magistral-Small-2506-GGUF:Q4_K_M
```
Other versions: Q8_0 (~25.1 GB) `llama cli -hf bartowski/mistralai_Magistral-Small-2506-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 Magistral Small 1.0 (2506) (Q4_K_M, needs ~14.3 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/mistralai_Magistral-Small-2506-GGUF/tree/dbf2f0bcf878662f61035a89542d10bc8fdca801))
```sh
lms get https://huggingface.co/bartowski/mistralai_Magistral-Small-2506-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 (~25.1 GB) `lms get https://huggingface.co/bartowski/mistralai_Magistral-Small-2506-GGUF@Q8_0 -y` · 4bit (~13.3 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Magistral-Small-2506-MLX-4bit -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 Magistral Small 1.0 (2506) (Q4_K_M, needs ~14.64 GB, from [ollama.com](https://ollama.com/library/magistral/tags))
```sh
ollama run magistral:24b-small-2506-q4_K_M
```
Other versions: Q8_0 (~28.78 GB) `ollama run magistral:24b-small-2506-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 Magistral Small 1.0 (2506) (4bit, needs ~13.3 GB, Mac only, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Magistral-Small-2506-MLX-4bit/tree/a687c32833447c00e782d3dab037ef7c00bad86a))
```sh
mlx_lm.chat --model lmstudio-community/Magistral-Small-2506-MLX-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model lmstudio-community/Magistral-Small-2506-MLX-4bit
```
Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Magistral-Small-2506)
[interactive version](https://whatmodel.sh/model/magistral-small-2506) · [JSON](https://whatmodel.sh/api?format=json&all=1)