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Llama 3.2 11B Vision Instruct

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Llama 3.2 11B Vision Instruct is an open-weights language model from Meta, released September 2024. Its published size is 10.7B parameters and a 128k-token context window. Licence: llama3.2.

Catalogue identity notes

Arena entry 'llama-3.2-vision-11b-instruct' (vision). LMArena's leaderboard table (arena-leaderboard space @6f2eeeb) labels 'llama-3.2-vision-11b-instruct' as 'Llama-3.2-11B-Vision-Instruct' (Meta, Llama 3.2), linking https://ai.meta.com/blog/llama-3-2-connect-2024-vision-edge-mobile-devices/. Maker repo meta-llama/Llama-3.2-11B-Vision-Instruct @9eb2daa: licence tag llama3.2. 10,670,220,835 parameters from the Hugging Face safetensors metadata (dense, so active = total). Released 2024-09: month the maker's Hugging Face repo was created (createdAt 2024-09-18).

42.7±1.8#489 of 719 LLMsfrom 11 benchmark results across 4 sources

$ how it compares

The LLMs ranked either side of Llama 3.2 11B Vision Instruct, out of 828 with a score, then more from Meta.

#modelscorewhereRAMreleased
577SmolLM3 3B Hugging Face43±1local~3.3GB2025-07
578Granite 3.1 8B Instruct IBM43±1local~6.6GB2024-12
579Llama 3.2 11B Vision Instruct Meta43±2local~8.8GB2024-09
580Qwen2.5 3B Instruct Alibaba43±1local~3.3GB2024-09
581DeepSeek-Coder 1.3B Instruct DeepSeek43±1local~2.4GB2023-10
more from Meta
24Muse Spark 1.3 Meta77±1api $2.00/M2026-09
40Muse Spark 1.2 Meta75±1api $2.00/M2026-08
43Muse Spark 1.1 Meta75±1api $2.00/M2026-07

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆██▇▆▄▃▁ #317 of 458 * · 8.5% of its score · 1 test

Beats GLM-4.7-Flash, Qwen3-VL 8B Instruct, Qwen3-VL 4B Instruct here, though they rank higher overall. Trails DeepSeek LLM 67B Chat, WizardLM 70B V1.0 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
HumanEval62.8% ✎▁▁▁▁▁▃▅▅█▃▂▁ beats 103/190easy · avg 72%▇▇▇ 8.5%sharp · at its level · noisy
math▁▂▃▅▆▅▄█▄▁▁▁ #298 of 398 * · 14% of its score · 2 tests

Beats GPT-4 (0613), Athene-70B, Gemma 2 27B IT here, though they rank higher overall. Trails Gemma 7B (base), DeepSeek R1 Distill Qwen 1.5B, CodeGemma 7B (v1.0) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
MATH (subset unspecified)48.3% ✎▂▄▄▃█▇▅▆▄▃▄▁ beats 25/80medium · avg 55%▇▇▇ 8.5%at its level
MathVista (testmini)24.3% ✎▂····▃···▂█▆ beats 1/9medium · avg 53%▇▇ 5.7%at its level
vision & documents▁▁▃▂▁▃▄▂▆▇█▄ #139 of 147 · 47% of its score · 6 tests

Beats Gemma 3 4B, CogVLM2 Llama3 Chat 19B, MiniCPM-V 2.6 here, though they rank higher overall. Trails Qwen3.5 0.8B, Molmo 7B-D (0924), North Micro Vision Instruct (2.4B) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Arena vision (style controlled)992.7 ✎▁▁▂▃▂▂▄▅▅▆██ beats 8/145avg 1068▇▇▇▇▇▇▇▇ 19%blunt
MM-MT-Bench (Mistral, GPT-4o judge)4.7900 MM-MT-Bench score (1-10) ✎▅··▃·█·▃▃··▅ beats 2/9avg 5.740 MM-MT-Bench score (1-10)▇▇▇▇ 10%sharp · noisy
VQAv2 (val)67.1% ✎▃···█▃··▅▃▃▃ beats 2/9easy · avg 74%▇▇ 6.2%blunt · at its level
MMMU (val)23.0% ✎▃·▂······█▄▄ beats 0/9medium · avg 52%▇▇ 5.8%at its level
MathVista (testmini)24.3% ✎▂····▃···▂█▆ beats 1/9medium · avg 53%▇▇ 5.7%at its level
ChartQA14.8% ✎▄·▄▄▄··▄▄▄▄█ beats 0/9easy · avg 77%▇ 0.5%blunt · at its level · noisy
other tests3 not in a task · 35% of its score
testresultvs the others in its rundifficultycountswhy
MMLU68.5% ✎▁▁▁▁▁▂▃▅█▃▆▃ beats 103/183easy · avg 76%▇▇▇▇▇▇▇▇ 19%at its level
AraGen-v3 3C3H score (Inception, Claude Sonnet 4 judge)24.4% ✎▁▁·▁▂▂▄█▄▅▄▄ beats 19/66hard · avg 31%▇▇▇▇ 9.2%typical
Open PT-LLM Leaderboard average (9 Portuguese tasks)71.0% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 177/276medium · avg 73%▇▇ 6.2%at its level · noisy

$ where to run it

Ollama · recommended

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run Llama 3.2 11B Vision Instruct · needs ~6.92 GB · from ollama.com
    chat in the terminal
    ollama run llama3.2-vision:11b-instruct-q4_K_M

Weights and documentation: huggingface.co/meta-llama ↗

$ how the score was worked out · Pixtral 12B (Mistral AI, arXi… 64%, LMArena leaderboard dataset 19%, Inception Arabic/Hindi leader… 9% 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
Pixtral 12B (Mistral AI, arXiv 2410.07073v2)8▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 64%CC BY 4.0
LMArena leaderboard dataset1▇▇▇▇▇▇ 19%CC BY 4.0
Inception Arabic/Hindi leaderboards (AraGen-v3, HindiGen-v1, Arabic IFEval)1▇▇▇ 9%Apache-2.0
Open Portuguese LLM Leaderboard (archived)1▇▇ 6%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 42.7

$ 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