[whatmodel.sh](../../README.md) / Pixtral 12B (2409)
# Pixtral 12B (2409)
Mistral · released 2024-09 · 12.4B params · apache-2.0
**score 49.7** ±1.7 · #375 of 732 · from 11 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 Pixtral 12B (2409) among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅▇●▇▅▄▃▁ #284 of 469* 21% 2
math ▁▁▃▅●▆▄█▄▂▁▁ #295 of 431* 15% 2
vision & documents ▁▁▂●▁▃▄▂▆▇█▄ #129 of 153 64% 8
coding · #284 of 469 · 21% of its score
Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [Qwen3-VL 30B-A3B Instruct](qwen3-vl-30b-a3b-instruct.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [Mistral NeMo Instruct (2407)](mistral-nemo-instruct-2407.md), [QwQ-32B-Preview](qwq-32b-preview.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
VisEditBench strict pass rate (1,… 35.3% ▄··●·▄··▄█·▄ beats 2/7 hard · avg 33% 14% at its level
HumanEval (maker) 72.0% ▁▁▁▁▂▂▆▆●▅▂▁ beats 143/205 easy · avg 67% 7.1% sharp · at its level · noisy
math · #295 of 431 · 15% of its score
Beats [Molmo 2 8B](molmo-2-8b.md), [DeepSeek-Coder-V2 Instruct (0724)](deepseek-coder-v2-0724.md), [Llama 3.1 Nemotron 70B Instruct](llama-3-1-nemotron-70b.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 (subset unspecified) (maker) 48.1% ▂▄▄▃●▇▅▆▄▃▄▁ beats 24/80 medium · avg 51% 9.0% at its level
MathVista (testmini) (maker) 58.3% ▂····▂···▂●▆ beats 6/9 medium · avg 51% 6.0% at its level · noisy
vision & documents · #129 of 153 · 64% of its score
Beats [Gemma 3 12B](gemma-3-12b.md), [Llama 3.2 90B Vision Instruct](llama-3-2-90b-vision.md), [Nova Pro](nova-pro.md) here, though they rank higher overall. Trails [Qwen3.5 2B](qwen3-5-2b.md), [Qwen3-VL 2B Thinking](qwen3-vl-2b-thinking.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena vision (style controlled) 1025.2 ▁▁▂▂●▂▃▄▅▅█▇ beats 17/152 avg 1061 20% blunt
VisEditBench strict pass rate (1,… 35.3% ▄··●·▄··▄█·▄ beats 2/7 hard · avg 33% 14% at its level
MM-MT-Bench (Mistral, GPT-4o judg… 6.0500 MM-MT-Bench score (1-10) ▅··▃·█·●▃··▅ beats 6/9 avg 5.605 MM-MT-Bench s… 9.0% sharp · noisy
MMMU (val) (maker) 52.0% ▂·▂······●▄▄ beats 4/9 medium · avg 51% 6.5% at its level
MathVista (testmini) (maker) 58.3% ▂····▂···▂●▆ beats 6/9 medium · avg 51% 6.0% at its level · noisy
VQAv2 (val) (maker) 78.6% ▃···█▃··▅▃●▃ beats 8/9 easy · avg 73% 5.1% blunt · at its level
Ai2 Molmo human preference Elo (S… 1016.0 ▄▄·▄·●▄▄·▄·█ beats 3/8 avg 1032 2.8% blunt
ChartQA (maker) 81.8% ▄·▄▄▄··▄▄▄●█ beats 7/9 easy · avg 74% 0.5% blunt · at its level · noisy
other tests (1)
test result vs the others in its run difficulty counts why
MMLU (maker) 69.2% ▁▁▁▁▁▂▂▅█●▆▄ beats 134/214 easy · avg 75% 19% at its level
source results share of the score
Pixtral 12B (submitted / maker-reported) 8 62% ▇▇▇▇▇▇▇
LMArena leaderboard dataset 1 20% ▇▇
VisEditBench: Can Vision-Language Model… 1 14% ▇▇
Ai2 Molmo-72B-0924 model card: Human Pr… 1 3% ▇
prior: pull toward the middle 1% ▇
weighted average = 49.7
Licences: Pixtral 12B ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · VisEditBench: Can Vision-Language Models Edit Visualization Code from Multimodal Feedback? ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Ai2 Molmo-72B-0924 model card: Human Preference Elo Rating column ([Apache-2.0](https://huggingface.co/allenai/Molmo-72B-0924/blob/ab586dd9640de059566975b8e5bcd0c6fdc73956/README.md))
## 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 Pixtral 12B (2409) (Q4_K_M, needs ~7.7 GB, from [huggingface.co/EnlistedGhost](https://huggingface.co/EnlistedGhost/Pixtral-12B-2409-GGUF/tree/4f01d59fe207ca72071e932c4827dc7fe602c2ac))
```sh
llama cli -hf EnlistedGhost/Pixtral-12B-2409-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf EnlistedGhost/Pixtral-12B-2409-GGUF:Q4_K_M
```
Other versions: Q8_0 (~13 GB) `llama cli -hf EnlistedGhost/Pixtral-12B-2409-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 Pixtral 12B (2409) (Q4_K_M, needs ~7.7 GB, from [huggingface.co/EnlistedGhost](https://huggingface.co/EnlistedGhost/Pixtral-12B-2409-GGUF/tree/4f01d59fe207ca72071e932c4827dc7fe602c2ac))
```sh
lms get https://huggingface.co/EnlistedGhost/Pixtral-12B-2409-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 (~13 GB) `lms get https://huggingface.co/EnlistedGhost/Pixtral-12B-2409-GGUF@Q8_0 -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 Pixtral 12B (2409) (Q4_K_M, needs ~7.7 GB, from [huggingface.co/EnlistedGhost](https://huggingface.co/EnlistedGhost/Pixtral-12B-2409-GGUF/tree/4f01d59fe207ca72071e932c4827dc7fe602c2ac))
```sh
ollama run hf.co/EnlistedGhost/Pixtral-12B-2409-GGUF:Q4_K_M
```
Other versions: Q8_0 (~13 GB) `ollama run hf.co/EnlistedGhost/Pixtral-12B-2409-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (BF16, needs ~25.37 GB, NVIDIA only, from [huggingface.co/mistralai](https://huggingface.co/mistralai/Pixtral-12B-2409/blob/a5ca2b7a9421a35ea24411c46e970758b8d3c6a7/README.md))
```sh
pip install --upgrade vllm mistral_common && vllm serve mistralai/Pixtral-12B-2409 --tokenizer_mode mistral --limit_mm_per_prompt 'image=4'
```
Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Pixtral-12B-2409)
[interactive version](https://whatmodel.sh/model/pixtral-12b) · [JSON](https://whatmodel.sh/api?format=json&all=1)