[whatmodel.sh](../../README.md) / Pixtral Large (2411) # Pixtral Large (2411) Mistral · released 2024-11 · 124B params · mistral-research **score 54.7** ±2.6 · #289 of 732 · from 4 benchmark results across 2 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks Pixtral Large (2411) among every model on that task or test, worst to best.}

task                vs the others          rank  counts  tests
math                ▁▁▃▅█●▄█▄▂▁▁   #239 of 431*     21%      1
vision & documents  ▁▁▂▂▁●▄▂▆▇█▄    #118 of 153     97%      4

math · #239 of 431 · 21% of its score

Beats [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md), [Command A (03-2025)](command-a.md), [GPT-4o (2024-05-13)](gpt-4o-2024-05-13.md) here, though they rank higher overall. Trails [Gemma 7B (base)](gemma-7b-base.md), [CodeGemma 7B (v1.0)](codegemma-7b.md), [Phi-2 (2.7B)](phi-2.md) here, though they rank lower overall.

test                          result  vs the others in its run  difficulty        counts  why
MathVista (testmini) (maker)   69.4%  ▁··▁▄▄▆▇●▅█▄ beats 25/48  medium · avg 62%     21%  at its level · small run

vision & documents · #118 of 153 · 97% of its score

Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md), [GPT-4 Turbo (2024-04-09)](gpt-4-turbo-2024-04-09.md) here, though they rank higher overall. Trails [Gemma 4 E4B](gemma-4-e4b.md), [Qwen2.5-VL 32B Instruct](qwen2-5-vl-32b.md), [Gemma 3 27B](gemma-3-27b.md) here, though they rank lower overall.

test                             result  vs the others in its run   difficulty        counts  why
Arena vision (style controlled)  1095.4  ▁▁▂▂▂▂●▄▅▅█▇ beats 29/152  avg 1061             49%  blunt
MathVista (testmini) (maker)      69.4%  ▁··▁▄▄▆▇●▅█▄ beats 25/48   medium · avg 62%     21%  at its level · small run
MMMU (val) (maker)                64.0%  ▁····▁▁▃█●▆▅ beats 26/53   medium · avg 56%     21%  at its level · small run
ChartQA (maker)                   88.1%  ▁··▁▁▁▁▁▂▅▅● beats 34/44   easy · avg 82%      6.8%  blunt · small run

## 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
LMArena leaderboard dataset                     1   49% ▇▇▇▇▇▇
Mistral AI vendor-reported benchmark sc…        3   48% ▇▇▇▇▇▇
prior: pull toward the middle                        3% ▇
weighted average                                   = 54.7

_{Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))} ## 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 Pixtral Large (2411) (Q2_K, needs ~46.7 GB, from [huggingface.co/EnlistedGhost](https://huggingface.co/EnlistedGhost/Pixtral-Large-Instruct-2411-GGUF/tree/eb0bab060427583f90d6edca2536d1be6e3d088d)) ```sh llama cli -hf EnlistedGhost/Pixtral-Large-Instruct-2411-GGUF:Q2_K ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf EnlistedGhost/Pixtral-Large-Instruct-2411-GGUF:Q2_K ``` ### 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 Large (2411) (Q2_K, needs ~46.7 GB, from [huggingface.co/EnlistedGhost](https://huggingface.co/EnlistedGhost/Pixtral-Large-Instruct-2411-GGUF/tree/eb0bab060427583f90d6edca2536d1be6e3d088d)) ```sh lms get https://huggingface.co/EnlistedGhost/Pixtral-Large-Instruct-2411-GGUF@Q2_K -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name ### Other ways to run it 1. Run it as published (BF16, needs ~248.27 GB, NVIDIA only, from [huggingface.co/mistralai](https://huggingface.co/mistralai/Pixtral-Large-Instruct-2411/blob/c1e51f6f11974a1199685d35c62f5a425c2d001e/README.md)) ```sh pip install --upgrade vllm mistral_common && vllm serve mistralai/Pixtral-Large-Instruct-2411 --config-format mistral --load-format mistral --tokenizer_mode mistral --limit_mm_per_prompt 'image=10' --tensor-parallel-size 8 ``` Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Pixtral-Large-Instruct-2411) [interactive version](https://whatmodel.sh/model/pixtral-large) · [JSON](https://whatmodel.sh/api?format=json&all=1)