[whatmodel.sh](../../README.md) / Qwen2.5-VL 72B Instruct
# Qwen2.5-VL 72B Instruct
Alibaba · released 2025-01 · 73.4B params · qwen
**score 55.9** ±0.9 · #261 of 732 · from 29 benchmark results across 9 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Qwen2.5-VL 72B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
math ▁▁▃▅█●▄█▄▂▁▁ #194 of 431* 7.5% 1
vision & documents ▁▁▂▂▁▃●▂▆▇█▄ #109 of 153 92% 20
math · #194 of 431 · 7.5% of its score
Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 0324](deepseek-v3-0324.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md) here, though they rank higher overall. Trails [Mixtral 8x7B v0.1 (base)](mixtral-8x7b-v0-1.md), [Phi-4-mini-reasoning 3.8B](phi-4-mini-reasoning.md), [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MathVista (testmini) (maker) 74.8% ▃··▃▅·▃█●▅▅█ beats 8/16 medium · avg 69% 7.5% at its level
vision & documents · #109 of 153 · 92% of its score
Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md), [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md), [GPT-4 Turbo (2024-04-09)](gpt-4-turbo-2024-04-09.md) here, though they rank higher overall. Trails [Llama 4 Scout (109B-A17B)](llama-4-scout.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CharXiv (reasoning questions) (ma… 49.7% ▄▂▄●▄▄▆··▄█▂ beats 7/21 medium · avg 38% 14% at its level
MMMU (val) (maker) 70.2% ▂···▆▂▂▂▄●▄█ beats 9/16 medium · avg 61% 11% at its level
MMStar (maker) 70.8% ▂··▄▂▂·▄●▂▄▆ beats 8/16 medium · avg 65% 9.4% at its level · several runs
CharXiv descriptive questions (ma… 87.4% ▄··▂▂▂·▂▄▄●█ beats 10/14 easy · avg 74% 9.3% typical
MMMU Pro (maker) 51.1% ▃·█·▅▃●▃▅▅▃█ beats 7/16 medium · avg 46% 9.1% at its level · several runs
MathVista (testmini) (maker) 74.8% ▃··▃▅·▃█●▅▅█ beats 8/16 medium · avg 69% 7.5% at its level
Arena vision (style controlled) 1121.1 ▁▁▂▂▂▂●▄▅▅█▇ beats 36/152 avg 1061 5.1% blunt
ChartArena chart parsing (English… 36.6% ▁·▁·▃▃▁●▃█·▄ beats 10/22 hard · avg 27% 3.8% blunt · at its level
CapArena-Auto detailed image capt… 35.3300 ▃▃▃·▃█·▅▃●▃▃ beats 10/12 avg -4.498 3.2% typical
HallusionBench (maker) 55.2% ▃·▃·▅▃●▅▃▅█▅ beats 6/16 medium · avg 54% 3.0% blunt · at its level
DocVQA (maker) 96.4% ▃··▅▃▅▃█▃●▅▅ beats 13/17 very easy · avg 94% 2.8% blunt · maxed out · several runs
Kaleidoscope (in-language multimo… 52.9% █·█···█·●·██ beats 3/5 medium · avg 46% 2.8% at its level
ChartQA (maker) 89.5% ▂··▂·▂▃·▅▂▆● beats 14/17 easy · avg 85% 2.2% blunt
RealWorldQA (maker) 75.7% ▅▃·▃▅▅·█·●▅█ beats 11/16 easy · avg 70% 2.1% blunt · at its level
CC-OCR (maker) 79.8% ▃··▃▅·▅▅·●▅▃ beats 9/13 easy · avg 74% 1.9% blunt · several runs
VMMU (Vietnamese multimodal under… 51.7% ▅······▃·▅·● beats 6/7 very hard · avg 36% 1.6% noisy
MSEarth-Bench Earth-science figur… 50.6% █·●█····██·█ beats 1/5 hard · avg 48% 1.2% blunt · at its level
ScreenSpot-Pro (maker) 43.6% ▂●▅▆██▅▂···▂ beats 2/24 medium · avg 43% 1.0% blunt · at its level · small run
OCRBench (maker) 88.5% ▅█·▃▃█▃●▅▃·▃ beats 12/16 easy · avg 84% 0.7% blunt
TextVQA (maker) 83.5% █······●·▄▄▄ beats 2/5 easy · avg 80% 0.4% blunt
other tests (5)
test result vs the others in its run difficulty counts why
Video-MME (with subtitles) (maker) 79.1% ▁▁▁▁▁·▁▃●▁▁█ beats 8/16 medium · avg 73% 1.8% at its level · small run
Open PT-LLM Leaderboard average (… 79.1% ▃▁▁▁▁▁▁▁▂█●▁ beats 251/276 medium · avg 72% 1.6% at its level · noisy
LVBench (maker) 47.3% ●·█▅▅▄▄··▃▁▁ beats 1/28 hard · avg 48% 1.4% at its level · small run
Video-MME (without subtitles) (ma… 73.3% ▁▃▁▃▁·█●▄▅·▇ beats 15/27 medium · avg 67% 1.3% blunt · at its level · small run
AndroidWorld (maker) 35.0% ▁·●▁▂▃█▅▂▂·▂ beats 1/24 hard · avg 32% 1.2% at its level · small run
source results share of the score
Qwen (submitted / maker-reported) 13 67% ▇▇▇▇▇▇▇▇
Qwen2.5-VL-32B-Instruct model card (sub… 9 13% ▇▇
LMArena leaderboard dataset 1 5% ▇
ChartArena: Benchmarking Chart Parsing … 1 4% ▇
CapArena: Benchmarking and Analyzing De… 1 3% ▇
Kaleidoscope: In-language Exams for Mas… 1 3% ▇
VMMU: A Vietnamese Multitask Multimodal… 1 2% ▇
Open Portuguese LLM Leaderboard 1 2% ▇
MSEarth: A Multimodal Benchmark for Ear… 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 55.9
Licences: Qwen2.5-VL-32B-Instruct model card ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · ChartArena: Benchmarking Chart Parsing across Languages ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Kaleidoscope: In-language Exams for Massively Multilingual Vision Evaluation ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · VMMU: A Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · MSEarth: A Multimodal Benchmark for Earth Science Phenomenon Discovery with MLLMs ([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 Qwen2.5-VL 72B Instruct (Q4_K_M, needs ~47.4 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen2.5-VL-72B-Instruct-GGUF/tree/8d2c2fca8ae6a6cf8249f2ab85407352285c5e37))
```sh
llama cli -hf lmstudio-community/Qwen2.5-VL-72B-Instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf lmstudio-community/Qwen2.5-VL-72B-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~77.3 GB) `llama cli -hf lmstudio-community/Qwen2.5-VL-72B-Instruct-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 Qwen2.5-VL 72B Instruct (Q4_K_M, needs ~47.4 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Qwen2.5-VL-72B-Instruct-GGUF/tree/8d2c2fca8ae6a6cf8249f2ab85407352285c5e37))
```sh
lms get https://huggingface.co/lmstudio-community/Qwen2.5-VL-72B-Instruct-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 (~77.3 GB) `lms get https://huggingface.co/lmstudio-community/Qwen2.5-VL-72B-Instruct-GGUF@Q8_0 -y` · 4bit (~42.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen2.5-VL-72B-Instruct-4bit -y` · 8bit (~78 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen2.5-VL-72B-Instruct-8bit -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 Qwen2.5-VL 72B Instruct (Q4_K_M, needs ~44.54 GB, from [ollama.com](https://ollama.com/library/qwen2.5vl/tags))
```sh
ollama run qwen2.5vl:72b-q4_K_M
```
Other versions: Q8_0 (~88.58 GB) `ollama run qwen2.5vl:72b-q8_0`
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run Qwen2.5-VL 72B Instruct (4bit, needs ~42.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Qwen2.5-VL-72B-Instruct-4bit/tree/e054d8e056aa77e85538e02e317b2c9cd37071ca))
```sh
mlx_lm.chat --model mlx-community/Qwen2.5-VL-72B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Qwen2.5-VL-72B-Instruct-4bit
```
Other versions: 8bit (~78 GB, Mac only) `mlx_lm.chat --model mlx-community/Qwen2.5-VL-72B-Instruct-8bit`
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2.5-VL-72B-Instruct)
[interactive version](https://whatmodel.sh/model/qwen2-5-vl-72b) · [JSON](https://whatmodel.sh/api?format=json&all=1)