[whatmodel.sh](../../README.md) / Llama 3.2 11B Vision Instruct # Llama 3.2 11B Vision Instruct Meta · released 2024-09 · 10.7B params · llama3.2 **score 44.2** ±1.7 · #492 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 Llama 3.2 11B Vision Instruct among every model on that task or test, worst to best.}

task                vs the others          rank  counts  tests
coding              ▁▁▁▃▅●█▇▅▄▃▁   #324 of 469*    7.6%      1
math                ▁▁▃▅●▆▄█▄▂▁▁   #309 of 431*     14%      2
vision & documents  ▁▁●▂▁▃▄▂▆▇█▄    #145 of 153     45%      6

coding · #324 of 469 · 7.6% of its score

Beats [Solar Open 100B (102B-A12B)](solar-open-100b.md), [Qwen3 4B Instruct 2507](qwen3-4b-instruct-2507.md), [Qwen3-VL 8B Instruct](qwen3-vl-8b-instruct.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md) here, though they rank lower overall.

test       result  vs the others in its run    difficulty      counts  why
HumanEval   62.8%  ▁▁▁▁▂▂▆▆●▅▂▁ beats 118/205  easy · avg 67%    7.6%  sharp · at its level · noisy

math · #309 of 431 · 14% of its score

Beats [Molmo 2 8B](molmo-2-8b.md), [Llama 3.1 Nemotron 70B Instruct](llama-3-1-nemotron-70b.md), [GPT-4 (0613)](gpt-4-0613.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)   48.3%  ▂▄▄▃●▇▅▆▄▃▄▁ beats 25/80  medium · avg 51%    8.4%  at its level
MathVista (testmini)        24.3%  ▂····●···▂█▆ beats 1/9    medium · avg 51%    5.2%  at its level · noisy

vision & documents · #145 of 153 · 45% of its score

Beats [Gemma 3 4B](gemma-3-4b.md), [CogVLM2 Llama3 Chat 19B](cogvlm2-llama3-chat-19b.md) here, though they rank higher overall. Trails [Qwen3.5 0.8B](qwen3-5-0-8b.md), [North Micro Vision Instruct (2.4B)](north-micro-vision-instruct.md), [Molmo 7B-D (0924)](molmo-7b-d.md) here, though they rank lower overall.

test                                                         result  vs the others in its run  difficulty                counts  why
Arena vision (style controlled)                               992.7  ▁▁▂●▂▂▃▄▅▅█▇ beats 8/152  avg 1061                     19%  blunt
MM-MT-Bench (Mistral, GPT-4o judg…  4.7900 MM-MT-Bench score (1-10)  ▅··●·█·▃▃··▅ beats 2/9    avg 5.605 MM-MT-Bench s…    8.9%  sharp · noisy
MMMU (val)                                                    23.0%  ●·▂······█▄▄ beats 0/9    medium · avg 51%            5.5%  at its level
VQAv2 (val)                                                   67.1%  ▃···●▃··▅▃▃▃ beats 2/9    easy · avg 73%              5.5%  blunt · at its level
MathVista (testmini)                                          24.3%  ▂····●···▂█▆ beats 1/9    medium · avg 51%            5.2%  at its level · noisy
ChartQA                                                       14.8%  ●·▄▄▄··▄▄▄▄█ beats 0/9    easy · avg 74%              0.6%  blunt · at its level · noisy

other tests (3)

test                                result  vs the others in its run    difficulty        counts  why
MMLU                                 68.5%  ▁▁▁▁▁▂▂▅█●▆▄ beats 126/214  easy · avg 75%       20%  at its level
AraGen-v3 3C3H score (Inception, …   24.4%  ▁▁·▁▂▂▄●▄▅▄▄ beats 19/66    hard · avg 29%       11%  typical
Open PT-LLM Leaderboard average (…   71.0%  ▃▁▁▁▁▁▁▁▂●▅▁ beats 177/276  medium · avg 72%    6.9%  at its level · noisy

## 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
Pixtral 12B                                8   62% ▇▇▇▇▇▇▇
LMArena leaderboard dataset                1   19% ▇▇
Inception Arabic/Hindi leaderboards        1   11% ▇
Open Portuguese LLM Leaderboard            1    7% ▇
prior: pull toward the middle                   1% ▇
weighted average                              = 44.2

_{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/)) · Inception Arabic/Hindi leaderboards ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt))} ## How to run it ### Ollama (recommended) 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run Llama 3.2 11B Vision Instruct (Q4_K_M, needs ~6.92 GB, from [ollama.com](https://ollama.com/library/llama3.2-vision/tags)) ```sh ollama run llama3.2-vision:11b-instruct-q4_K_M ``` Other versions: Q8_0 (~13.34 GB) `ollama run llama3.2-vision:11b-instruct-q8_0` Model repository and documentation: [huggingface.co/meta-llama](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct) [interactive version](https://whatmodel.sh/model/llama-3-2-11b-vision) · [JSON](https://whatmodel.sh/api?format=json&all=1)