[whatmodel.sh](../../README.md) / Mistral Small 3 24B (2501) # Mistral Small 3 24B (2501) Mistral · released 2025-01 · 23.57B params · apache-2.0 **score 53.9** ±1.0 · #305 of 732 · from 18 benchmark results across 12 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Mistral Small 3 24B (2501) among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
coding          ▁▁▁▃▅▇●▇▅▄▃▁   #244 of 469*    4.6%      1
chat & writing  ▁▁▁▃▆▄●█▆▇▇▃   #259 of 402*     10%      1
math            ▁▁▃▅●▆▄█▄▂▁▁   #284 of 431*     27%      2
coding · #244 of 469 · 4.6% of its score Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [GLM-4.7-Flash](glm-4-7-flash.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
WebApp1K-React pass@1 (onekq-ai)   55.5%  ▃▁▁█▁●▅▃▄▇▁▄ beats 12/31  medium · avg 40%    4.6%  sharp · at its level · noisy
chat & writing · #259 of 402 · 10% of its score Beats [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md), [Qwen2.5 Coder 32B Instruct](qwen2-5-coder-32b.md), [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md) here, though they rank higher overall. Trails [Flan-T5 XXL](flan-t5-xxl.md), [Granite 4.0 H 1B](granite-4-0-h-1b.md), [Granite 4.0 1B](granite-4-0-1b.md) here, though they rank lower overall. 
test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1274.4  ▁▁▁▂▃▃●▆▆▇█▃ beats 105/385  avg 1260       10%  blunt
math · #284 of 431 · 27% of its score Beats [Molmo 2 8B](molmo-2-8b.md), [Llama 3.3 70B Instruct](llama-3-3-70b.md), [DeepSeek-V2.5](deepseek-v2-5.md) here, though they rank higher overall. Trails [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md), [Gemma 7B (base)](gemma-7b-base.md), [Mistral 7B v0.1 (base)](mistral-7b-v0-1.md) here, though they rank lower overall. 
test                      result  vs the others in its run   difficulty            counts  why
MATH level 5               44.8%  ▂▂▅▆▄●▆▃▅▂▃▄ beats 36/92   hard · avg 28%           22%  sharp · at its level
OTIS mock AIME 2024–2025    6.7%  ▃▃●▃▃▃▇▇█▃▂▅ beats 38/197  very hard · avg 7.2%    4.4%  sharp · noisy
other tests (14) 
test                                result  vs the others in its run    difficulty            counts  why
GPQA Diamond                         47.3%  ▁▁▁▁▂▄●▄▅▇█▅ beats 57/211   hard · avg 39%           14%  sharp · at its level
CPTUB average (SpeakLeash, 0-5 ju…   74.2%  ▁▂▂▂▃█▃▆●▄▂▃ beats 66/95    medium · avg 65%        6.8%  at its level
EQ-Bench 3 (emotional intelligenc…  1058.9  ▁·▂▁●▅▅▃█▄▁▁ beats 7/62     avg 1034                6.1%  typical
BRIDGE clinical text, zero-shot a…   37.5%  ▁▁▁▁▁▂▂▃▄●█▃ beats 57/97    hard · avg 31%          5.9%  blunt · at its level
AraGen-v3 3C3H score (Inception, …   39.8%  ▁▁·▁▂▂▄█●▅▄▄ beats 36/66    hard · avg 29%          5.7%  at its level
HindiGen-v1 3C3H score (Inception…   50.3%  ▁▁▁▁·▂·●▆█▇▁ beats 7/31     medium · avg 46%        5.3%  at its level
Polish EQ-Bench v2 (SpeakLeash)      70.5%  ▁··▁▁▂▂▆▅▆●▆ beats 53/70    medium · avg 62%        5.2%  at its level
Judgemark v2 (LLM-as-judge for cr…   63.6%  ▃▃··▃▅●···█▃ beats 5/10     medium · avg 53%        3.5%  at its level
Open PT-LLM Leaderboard average (…   77.2%  ▃▁▁▁▁▁▁▁▂█●▁ beats 239/276  medium · avg 72%        2.2%  at its level · noisy
EuroEval HellaSwag (English), tes…   91.0%  ▃·▁▁▂▁▂▅█▇●▁ beats 95/100   medium · avg 67%        1.8%  sharp · at its level · noisy
EuroEval Life in the UK (English …   94.3%  ▁▁▁▁▁▁▁▂▄█▄● beats 136/146  easy · avg 82%          1.0%  noisy
Chess Puzzles (Epoch)                 0.0%  ●▁▁▁▁▂▃▃▃▂▁▁ beats 5/137    very hard · avg 5.8%    0.7%  sharp · too hard for it · noisy
Vectara hallucination leaderboard…   96.9%  ▁▁▂▃▄▄█●▃▂▂▁ beats 100/140  very easy · avg 94%     0.5%  blunt · maxed out · noisy
Vectara hallucination leaderboard…   94.9%  ▁▂▁▂▅█▂▃▄●▁▁ beats 89/98    very easy · avg 90%     0.0%  blunt
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
Epoch AI                                        4   41% ▇▇▇▇▇
Inception Arabic/Hindi leaderboards             2   11% ▇
LMArena leaderboard dataset                     1   10% ▇
SpeakLeash CPTUB                                1    7% ▇
EQ-Bench 3 canonical leaderboard results        1    6% ▇
BRIDGE Medical Leaderboard                      1    6% ▇
SpeakLeash Polish EQ-Bench leaderboard          1    5% ▇
WebApp1K models leaderboard                     1    5% ▇
Judgemark v2 results                            1    3% ▇
EuroEval leaderboards                           2    3% ▇
Open Portuguese LLM Leaderboard                 1    2% ▇
Vectara hallucination leaderboard               2    1% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 53.9
Licences: Epoch AI ([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)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · EQ-Bench 3 canonical leaderboard results ([MIT](../../licenses/eqbench3-MIT.txt)) · BRIDGE Medical Leaderboard ([MIT](../../licenses/bridge-medical-leaderboard-MIT.txt)) · SpeakLeash Polish EQ-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · WebApp1K models leaderboard ([MIT](../../licenses/webapp1k-leaderboard-MIT.txt)) · Judgemark v2 results ([MIT](../../licenses/judgemark-v2-MIT.txt)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) ## 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 Mistral Small 3 24B (2501) (Q4_K_M, needs ~14.3 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Mistral-Small-24B-Instruct-2501-GGUF/tree/9b2f156e3fc7f1667ee0f8066d93021a6c4f6aa0)) ```sh llama cli -hf lmstudio-community/Mistral-Small-24B-Instruct-2501-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/Mistral-Small-24B-Instruct-2501-GGUF:Q4_K_M ``` Other versions: Q8_0 (~25.1 GB) `llama cli -hf lmstudio-community/Mistral-Small-24B-Instruct-2501-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 Mistral Small 3 24B (2501) (Q4_K_M, needs ~14.3 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Mistral-Small-24B-Instruct-2501-GGUF/tree/9b2f156e3fc7f1667ee0f8066d93021a6c4f6aa0)) ```sh lms get https://huggingface.co/lmstudio-community/Mistral-Small-24B-Instruct-2501-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/lmstudio-community/Mistral-Small-24B-Instruct-2501-GGUF@Q8_0 -y` · 4bit (~13.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/Mistral-Small-24B-Instruct-2501-4bit -y` · 8bit (~25 GB, Mac only) `lms get https://huggingface.co/mlx-community/Mistral-Small-24B-Instruct-2501-8bit -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 Mistral Small 3 24B (2501) (Q4_K_M, needs ~14.64 GB, from [ollama.com](https://ollama.com/library/mistral-small/tags)) ```sh ollama run mistral-small:24b-instruct-2501-q4_K_M ``` Other versions: Q8_0 (~28.78 GB) `ollama run mistral-small:24b-instruct-2501-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 Mistral Small 3 24B (2501) (4bit, needs ~13.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Mistral-Small-24B-Instruct-2501-4bit/tree/51ed0c6d2f98a25d8a60f19994f0e86c944995e0)) ```sh mlx_lm.chat --model mlx-community/Mistral-Small-24B-Instruct-2501-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model mlx-community/Mistral-Small-24B-Instruct-2501-4bit ``` Other versions: 8bit (~25 GB, Mac only) `mlx_lm.chat --model mlx-community/Mistral-Small-24B-Instruct-2501-8bit` Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501) [interactive version](https://whatmodel.sh/model/mistral-small-3) · [JSON](https://whatmodel.sh/api?format=json&all=1)