[whatmodel.sh](../../README.md) / Qwen2.5-VL 32B Instruct
# Qwen2.5-VL 32B Instruct
Alibaba · released 2025-03 · 33.5B params · apache-2.0
**score 53.3** ±1.5 · #312 of 732 · from 16 benchmark results across 7 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 32B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
vision & documents ▁▁▂▂▁●▄▂▆▇█▄ #116 of 153 77% 11
vision & documents · #116 of 153 · 77% 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), [Pixtral Large (2411)](pixtral-large.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
Arena vision (style controlled) 1118.8 ▁▁▂▂▂▂●▄▅▅█▇ beats 34/152 avg 1061 15% blunt
ChartArena chart parsing (English… 31.8% ▁·▁·▃▃●▇▃█·▄ beats 6/22 hard · avg 27% 10% blunt · at its level
CharXiv (reasoning questions) 39.1% ▂▅●▅▅▆▆▅▆█▆▂ beats 16/101 medium · avg 36% 10% at its level · small run
CharXiv descriptive questions 72.5% ▂▄▄●▇▄██▇▂▁▂ beats 16/59 easy · avg 71% 9.0% small run
Kaleidoscope (in-language multimo… 48.2% █·█···●·█·██ beats 2/5 medium · avg 46% 8.3% at its level
MMMU Pro (maker) 49.5% ▂▃▁▃●▇▃▇▇██▅ beats 10/52 hard · avg 43% 7.7% at its level · small run
MMStar (maker) 69.5% ▃▅▂▃▂▂●██▆█▅ beats 12/35 medium · avg 64% 6.1% at its level · small run
VMMU (Vietnamese multimodal under… 52.1% ▅······▃·▅·● beats 7/7 very hard · avg 36% 4.0% noisy
ScreenSpot-Pro (maker) 39.4% ▂●▅▆██▅▂···▂ beats 1/24 medium · avg 43% 2.8% blunt · at its level · small run
DocVQA (maker) 94.8% ▁··▃▁▁▁▇●▇▄▅ beats 15/28 very easy · avg 94% 2.7% blunt · maxed out · small run
CC-OCR (maker) 77.1% ▁·▂··▂▂▃●▂▆█ beats 10/26 easy · avg 72% 1.6% blunt · at its level · small run
other tests (5)
test result vs the others in its run difficulty counts why
Video-MME (with subtitles) (maker) 77.9% ▁▁▁▁▁·▁●▁▁▁█ beats 7/16 medium · avg 73% 5.4% at its level · small run
Open PT-LLM Leaderboard average (… 80.9% ▃▁▁▁▁▁▁▁▂█●▁ beats 263/276 medium · avg 72% 4.9% at its level · noisy
LVBench (maker) 49.0% ●·█▅▅▄▄··▃▁▁ beats 4/28 hard · avg 48% 4.1% at its level · small run
Video-MME (without subtitles) (ma… 70.5% ▁▃▁▃▁·●▄▄▅·▇ beats 10/27 medium · avg 67% 4.0% blunt · at its level · small run
AndroidWorld (maker) 22.0% ●·▁▁▂▃█▅▂▂·▂ beats 0/24 hard · avg 32% 3.4% at its level · small run
source results share of the score
Qwen2.5-VL-32B-Instruct model card (sub… 9 38% ▇▇▇▇▇
Apriel-1.5-15b-Thinker 2 18% ▇▇
LMArena leaderboard dataset 1 15% ▇▇
ChartArena: Benchmarking Chart Parsing … 1 10% ▇
Kaleidoscope: In-language Exams for Mas… 1 8% ▇
Open Portuguese LLM Leaderboard 1 5% ▇
VMMU: A Vietnamese Multitask Multimodal… 1 4% ▇
prior: pull toward the middle 1% ▇
weighted average = 53.3
Licences: Qwen2.5-VL-32B-Instruct model card ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Apriel-1.5-15b-Thinker ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · 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/)) · Kaleidoscope: In-language Exams for Massively Multilingual Vision Evaluation ([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)) · VMMU: A Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark ([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 32B Instruct (Q4_K_M, needs ~19.9 GB, from [huggingface.co/ggml-org](https://huggingface.co/ggml-org/Qwen2.5-VL-32B-Instruct-GGUF/tree/94e4f98b5df15a6198401ad1dce38897b38c41a6))
```sh
llama cli -hf ggml-org/Qwen2.5-VL-32B-Instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf ggml-org/Qwen2.5-VL-32B-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~34.8 GB) `llama cli -hf ggml-org/Qwen2.5-VL-32B-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 32B Instruct (Q4_K_M, needs ~19.9 GB, from [huggingface.co/ggml-org](https://huggingface.co/ggml-org/Qwen2.5-VL-32B-Instruct-GGUF/tree/94e4f98b5df15a6198401ad1dce38897b38c41a6))
```sh
lms get https://huggingface.co/ggml-org/Qwen2.5-VL-32B-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 (~34.8 GB) `lms get https://huggingface.co/ggml-org/Qwen2.5-VL-32B-Instruct-GGUF@Q8_0 -y` · 4bit (~19.8 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen2.5-VL-32B-Instruct-4bit -y` · 8bit (~36.2 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen2.5-VL-32B-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 32B Instruct (Q4_K_M, needs ~20.6 GB, from [ollama.com](https://ollama.com/library/qwen2.5vl/tags))
```sh
ollama run qwen2.5vl:32b-q4_K_M
```
Other versions: Q8_0 (~40.7 GB) `ollama run qwen2.5vl:32b-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 32B Instruct (4bit, needs ~19.8 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Qwen2.5-VL-32B-Instruct-4bit/tree/926bd4bb494a7d0e187f9b81b3924a31227bd1c7))
```sh
mlx_lm.chat --model mlx-community/Qwen2.5-VL-32B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Qwen2.5-VL-32B-Instruct-4bit
```
Other versions: 8bit (~36.2 GB, Mac only) `mlx_lm.chat --model mlx-community/Qwen2.5-VL-32B-Instruct-8bit`
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2.5-VL-32B-Instruct)
[interactive version](https://whatmodel.sh/model/qwen2-5-vl-32b) · [JSON](https://whatmodel.sh/api?format=json&all=1)