[whatmodel.sh](../../README.md) / Magistral Small 1.2
# Magistral Small 1.2
Mistral · released 2025-09 · 24B params · apache-2.0
**score 57.6** ±1.0 · #233 of 732 · from 14 benchmark results across 6 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 1.2 among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ██········●█ #2 of 4* 8.4% 2
math ▁▁▃▅█▆●█▄▂▁▁ #185 of 431 35% 3
vision & documents ▁▁▂▂▁▃●▂▆▇█▄ #104 of 153* 8.7% 2
coding · #2 of 4 · 8.4% of its score
test result vs the others in its run difficulty counts why
LiveCodeBench v5 (window unspecif… 70.9% ▄█▄······●·▄ beats 4/5 medium · avg 50% 5.0% at its level
LiveCodeBench (version unspecifie… 60.7% ▁···▁▁▁█●▃▄▁ beats 25/38 medium · avg 42% 3.4% at its level · small run
math · #185 of 431 · 35% of its score
Beats [MiniMax-M2.1](minimax-m2-1.md), [GPT-4.1](gpt-4-1.md), [DeepSeek V3 0324](deepseek-v3-0324.md) here, though they rank higher overall. Trails [Phi-4-mini-reasoning 3.8B](phi-4-mini-reasoning.md), [OpenThinker3 7B](openthinker3-7b.md), [Olmo 3 7B Think](olmo-3-7b-think.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AIME 2025 (maker) 77.3% █▃·····●···▃ beats 4/5 medium · avg 48% 16% sharp · at its level
AIME 2024 (maker) 86.1% █▃·····●···▃ beats 4/5 medium · avg 60% 11% sharp
OTIS mock AIME 2024–2025 28.1% ▃▃▄▃●▃▇▇█▃▂▅ beats 62/197 very hard · avg 7.2% 7.9% sharp · at its level · noisy
vision & documents · #104 of 153 · 8.7% of its score
Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md), [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md) here, though they rank higher overall. Trails [Llama 4 Maverick (402B-A17B)](llama-4-maverick.md), [Qwen3-VL 8B Thinking](qwen3-vl-8b-thinking.md), [Qwen3-VL 30B-A3B Instruct](qwen3-vl-30b-a3b-instruct.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CharXiv (reasoning questions) 52.9% ▂▅▃▅●▆▆▅▆█▆▂ beats 33/101 medium · avg 36% 4.8% at its level · small run
CharXiv descriptive questions 82.3% ▂▄▄▅▇●██▇▂▁▂ beats 28/59 easy · avg 71% 3.9% small run
other tests (6)
test result vs the others in its run difficulty counts why
GPQA Diamond 47.6% ▁▁▁▁▂▄●▄▅▇█▅ beats 60/211 hard · avg 39% 35% sharp · at its level · several runs
AraGen-v3 3C3H score (Inception, … 32.3% ▁▁·▁▂▂▄●▄▅▄▄ beats 29/66 hard · avg 29% 6.8% at its level
COLLIE (constrained text generati… 60.3% ▄·▄█▄··▄▄·●▄ beats 7/8 medium · avg 42% 1.9% at its level · small run
EuroEval HellaSwag (English), tes… 89.4% ▃·▁▁▂▁▂▅█▇●▁ beats 90/100 medium · avg 67% 1.7% sharp · at its level · noisy
Chess Puzzles (Epoch) 3.0% ●▁▁▁▁▂▃▃▃▂▁▁ beats 38/137 very hard · avg 5.8% 1.4% sharp · too hard for it · noisy
EuroEval Life in the UK (English … 95.5% ▁▁▁▁▁▁▁▂▄█▄● beats 142/146 easy · avg 82% 0.9% noisy
source results share of the score
mistralai/Magistral-Small-2509 model ca… 4 48% ▇▇▇▇▇▇
Epoch AI 3 28% ▇▇▇
Apriel-1.5-15b-Thinker 2 9% ▇
Inception Arabic/Hindi leaderboards 1 7% ▇
mistralai/Mistral-Small-4-119B-2603 mod… 2 5% ▇
EuroEval leaderboards 2 3% ▇
prior: pull toward the middle 0% ▇
weighted average = 57.6
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/)) · Apriel-1.5-15b-Thinker ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Inception Arabic/Hindi leaderboards ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · mistralai/Mistral-Small-4-119B-2603 model card ([Apache-2.0](../../licenses/cards-gap-NOTICE.txt)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE))
## How to run it
### Through an API
api (Amazon Bedrock) · $0.75/M blended · $0.5 in / $1.5 out per 1M tokens · [docs.aws.amazon.com](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html)
[price source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/providers/amazon-bedrock/models/mistral.magistral-small-2509.toml): Standard base token rate from models.dev. Provider-specific tiers, long-context surcharges, caching and promotions may differ. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/mistral/magistral-small-2509.toml)
### 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 1.2 (gguf-q4_k_m, needs ~14.9 GB, from [huggingface.co/mistralai](https://huggingface.co/mistralai/Magistral-Small-2509-GGUF/tree/main))
```sh
llama cli -hf mistralai/Magistral-Small-2509-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mistralai/Magistral-Small-2509-GGUF:Q4_K_M
```
Other versions: gguf-q8_0 (~29.3 GB) `llama cli -hf mistralai/Magistral-Small-2509-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 1.2 (gguf-q4_k_m, needs ~14.9 GB, from [huggingface.co/mistralai](https://huggingface.co/mistralai/Magistral-Small-2509-GGUF/tree/main))
```sh
lms get https://huggingface.co/mistralai/Magistral-Small-2509-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: gguf-q8_0 (~29.3 GB) `lms get https://huggingface.co/mistralai/Magistral-Small-2509-GGUF@Q8_0 -y` · 4bit (~14.1 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Magistral-Small-2509-MLX-4bit -y` · 8bit (~25.9 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Magistral-Small-2509-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 Magistral Small 1.2 (gguf-q4_k_m, needs ~14.9 GB, from [huggingface.co/mistralai](https://huggingface.co/mistralai/Magistral-Small-2509-GGUF/tree/main))
```sh
ollama run hf.co/mistralai/Magistral-Small-2509-GGUF:Q4_K_M
```
Other versions: gguf-q8_0 (~29.3 GB) `ollama run hf.co/mistralai/Magistral-Small-2509-GGUF: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 1.2 (4bit, needs ~14.1 GB, Mac only, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Magistral-Small-2509-MLX-4bit/tree/bb18789f03d900dcda5fd62937c9ae6a280cd532))
```sh
mlx_lm.chat --model lmstudio-community/Magistral-Small-2509-MLX-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model lmstudio-community/Magistral-Small-2509-MLX-4bit
```
Other versions: 8bit (~25.9 GB, Mac only) `mlx_lm.chat --model lmstudio-community/Magistral-Small-2509-MLX-8bit`
Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Magistral-Small-2509)
[interactive version](https://whatmodel.sh/model/magistral-small-2509) · [JSON](https://whatmodel.sh/api?format=json&all=1)