whatmodel.sh / Ministral 3 8B Reasoning 2512

Ministral 3 8B Reasoning 2512

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Ministral 3 8B Reasoning 2512 is an open-weights language model from Mistral, released December 2025. Its published size is 8.8B parameters and a 262k-token context window. Licence: apache-2.0.

Model information sources

· Parameter count ↗

Sum of the two components the card states (8.4B LM + 0.4B vision encoder = 8.8B). The repo's safetensors header gives 8,918,026,240 parameters (HF API); the sibling non-reasoning catalogue entry uses 8.92. Dense model: active_b = params_b.

55.2±1.1#259 of 719 LLMsfrom 11 benchmark results across 4 sources

$ how it compares

The LLMs ranked either side of Ministral 3 8B Reasoning 2512, out of 828 with a score, then more from Mistral.

#modelscorewhereRAMreleased
309Qwen2.5 72B Instruct Alibaba55±1api $0.37/M +local~30GB2024-09
310Mistral Large 2 (2411) Mistral55±1local~78GB2024-11
311Ministral 3 8B Reasoning 2512 Mistral55±1local~6.5GB2025-12
312Magistral Small 1.0 (2506) Mistral55±1local~16GB2025-06
313Qwen3 30B-A3B (Apr 2025, hybrid) Alibaba55±1api $0.35/M +local~19GB2025-04
more from Mistral
79Mistral Large 4 (public preview) Mistral71±2api $2.06/M2026-10
174Mistral Medium 3.5 (128B) Mistral65±1api $3.00/M +local~42GB2026-04
180Mistral Medium 3.1 (2508) Mistral64±2comparison only2025-08

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #147 of 458 * · 10% of its score · 1 test

Beats Gemini 2.5 Pro, Gemini 3.1 Flash-Lite, Qwen3-Coder-Next (80B-A3B) here, though they rank higher overall.

testresultvs the others in its rundifficultycountswhy
LiveCodeBench (version unspecified) lab +161.6% ✎▁···▁▁▃▅▅█▄▅ beats 27/37medium · avg 47%▇▇▇▇ 10%at its level · small run · several runs
math█·····█····█ #2 of 3 · 39% of its score · 2 tests
testresultvs the others in its rundifficultycountswhy
AIME 2025 lab +178.7% ✎▁·▁▁▁▂▅▆█▇▅▁ beats 82/133medium · avg 64%▇▇▇▇▇▇▇▇ 20%sharp · small run · several runs
AIME 2024 lab +186.0% ✎▁▁▁▂▁▃▁▅███▃ beats 62/85easy · avg 73%▇▇▇▇▇▇▇▇ 19%sharp · small run · several runs
other tests4 not in a task · 51% of its score
testresultvs the others in its rundifficultycountswhy
GPQA Diamond lab +166.8% ✎▁▁▁▁▂▄▅▅███▄ beats 141/310hard · avg 51%▇▇▇▇▇▇▇▇▇▇ 24%sharp · at its level · small run · several runs
Medmarks-V mean win rate (19 verifiable medical benchmarks, 71 configurations)0.4312 ✎▁▄▃▃▇▄█▃▅▁▁▁ beats 7/30avg 0.423▇▇▇▇▇▇ 16%sharp
Medmarks-V weighted mean win rate (61 models, 71 configurations)43.1% ✎▂▃▅▂▆▃█▅█▅▃▆ beats 7/34medium · avg 42%▇▇▇ 7.8%blunt · at its level
SurgiQ surgical MCQ (zero-shot, log-probability scoring)58.5% ✎▂···▂▂·▃▂▃██ beats 11/17medium · avg 53%▇ 2.5%at its level

$ where to run it

llama.cpp · recommended

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run Ministral 3 8B Reasoning 2512 · needs 5.2 GB · from huggingface.co/mistralai
    chat in the terminal
    llama cli -hf mistralai/Ministral-3-8B-Reasoning-2512-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf mistralai/Ministral-3-8B-Reasoning-2512-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run Ministral 3 8B Reasoning 2512 · needs 5.2 GB · from huggingface.co/mistralai

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/mistralai/Ministral-3-8B-Reasoning-2512-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Ministral 3 8B Reasoning 2512 · needs 5.2 GB · from huggingface.co/mistralai
    chat in the terminal
    ollama run hf.co/mistralai/Ministral-3-8B-Reasoning-2512-GGUF:Q4_K_M

MLX (Mac)

version

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run Ministral 3 8B Reasoning 2512 · needs ~4.8 GB, Mac only · from huggingface.co/alexgusevski
    chat in the terminal
    mlx_lm.chat --model alexgusevski/Ministral-3-8B-Reasoning-2512-q4-mlx
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model alexgusevski/Ministral-3-8B-Reasoning-2512-q4-mlx

Weights and documentation: huggingface.co/mistralai ↗

$ how the score was worked out · mistralai/Ministral-3-3B-Reas… 37%, mistralai/Ministral-3-8B-Reas… 37%, Medmarks: A Comprehensive Ope… 24% and 1 more

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The 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's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
mistralai/Ministral-3-3B-Reasoning-2512 model card · lab-reported4▇▇▇▇▇▇▇▇▇▇▇ 37%Apache-2.0
mistralai/Ministral-3-8B-Reasoning-2512 model card · lab-reported4▇▇▇▇▇▇▇▇▇▇▇ 37%Apache-2.0
Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks — authors' own runs of 61 models2▇▇▇▇▇▇▇ 24%CC BY 4.0
SurgiQ: A Large-Scale Multi-Domain Benchmark for Evaluating Surgical Understanding in Large Language Models (arXiv:2606.08071v1)1▇ 3%CC BY 4.0
prior · pull toward the middle▇ 1%
weighted average= 55.2

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json