[whatmodel.sh](../../README.md) / Magistral Small 1.1 (2507)
# Magistral Small 1.1 (2507)
Mistral · released 2025-07 · 23.57B params · apache-2.0
**score 54.4** ±1.4 · #297 of 732 · from 4 benchmark results across 1 source · † no independently run results verified
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Magistral Small 1.1 (2507) among every model on that task or test, worst to best.
task vs the others rank counts tests
coding █●········██ #3 of 4* 10% 1
math ▁▁▃▅█●▄█▄▂▁▁ #220 of 431* 58% 2
coding · #3 of 4 · 10% of its score
Beats [Magistral Small 1.0 (2506)](magistral-small-2506.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
LiveCodeBench v5 (window unspecif… 59.2% ▄●▄······▄·▄ beats 1/5 medium · avg 50% 10% at its level
math · #220 of 431 · 58% 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 [Phi-2 (2.7B)](phi-2.md), [Granite 8B Code Base (4K)](granite-8b-code-base-4k.md), [Mixtral 8x7B v0.1 (base)](mixtral-8x7b-v0-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
AIME 2025 (maker) 62.0% ●▃·····▃···▃ beats 1/5 medium · avg 48% 33% sharp · at its level
AIME 2024 (maker) 70.5% ●▃·····▃···▃ beats 0/5 medium · avg 60% 25% sharp · at its level
other tests (1)
test result vs the others in its run difficulty counts why
GPQA Diamond (maker) 65.8% ●·█·███····█ beats 0/5 medium · avg 52% 32% sharp · at its level
source results share of the score
mistralai/Magistral-Small-2509 model ca… 4 99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle 1% ▇
weighted average = 54.4
Licences: mistralai/Magistral-Small-2509 model card ([Apache-2.0](../../licenses/mistralai--Magistral-Small-2509.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 1.1 (2507) (Q4_K_M, needs ~14.3 GB, from [huggingface.co/mistralai](https://huggingface.co/mistralai/Magistral-Small-2507-GGUF/tree/129d7a005ec2180c7b6a56b8f175032d48ff78d9))
```sh
llama cli -hf mistralai/Magistral-Small-2507-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-2507-GGUF:Q4_K_M
```
Other versions: Q8_0 (~25.1 GB) `llama cli -hf mistralai/Magistral-Small-2507-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 1.1 (2507) (Q4_K_M, needs ~14.3 GB, from [huggingface.co/mistralai](https://huggingface.co/mistralai/Magistral-Small-2507-GGUF/tree/129d7a005ec2180c7b6a56b8f175032d48ff78d9))
```sh
lms get https://huggingface.co/mistralai/Magistral-Small-2507-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/mistralai/Magistral-Small-2507-GGUF@Q8_0 -y` · 4bit (~13.3 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Magistral-Small-2507-MLX-4bit -y` · 8bit (~25 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Magistral-Small-2507-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 1.1 (2507) (Q4_K_M, needs ~14.3 GB, from [huggingface.co/mistralai](https://huggingface.co/mistralai/Magistral-Small-2507-GGUF/tree/129d7a005ec2180c7b6a56b8f175032d48ff78d9))
```sh
ollama run hf.co/mistralai/Magistral-Small-2507-GGUF:Q4_K_M
```
Other versions: Q8_0 (~25.1 GB) `ollama run hf.co/mistralai/Magistral-Small-2507-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 1.1 (2507) (4bit, needs ~13.3 GB, Mac only, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Magistral-Small-2507-MLX-4bit/tree/5cfe953b7292617b92a01d8a791e2e048d1d6e07))
```sh
mlx_lm.chat --model lmstudio-community/Magistral-Small-2507-MLX-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model lmstudio-community/Magistral-Small-2507-MLX-4bit
```
Other versions: 8bit (~25 GB, Mac only) `mlx_lm.chat --model lmstudio-community/Magistral-Small-2507-MLX-8bit`
### Other ways to run it
1. Run it as published (BF16, needs ~47.14 GB, NVIDIA only, from [huggingface.co/mistralai](https://huggingface.co/mistralai/Magistral-Small-2507/blob/b3583a426ca23186681c4297926dfe9219be6161/README.md))
```sh
vllm serve mistralai/Magistral-Small-2507 --reasoning-parser mistral --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --tensor-parallel-size 2
```
Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Magistral-Small-2507)
[interactive version](https://whatmodel.sh/model/magistral-small-2507) · [JSON](https://whatmodel.sh/api?format=json&all=1)