[whatmodel.sh](../../README.md) / Qwen2.5-VL 7B Instruct
# Qwen2.5-VL 7B Instruct
Alibaba · released 2025-01 · 8.29B params · apache-2.0
**score 50.0** ±1.0 · #371 of 732 · from 14 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 Qwen2.5-VL 7B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
math ▁▁▃▅█●▄█▄▂▁▁ #250 of 431* 11% 1
vision & documents ▁▁▂▂●▃▄▂▆▇█▄ #125 of 153 100% 14
math · #250 of 431 · 11% 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) 68.2% ▃··▃▅·●█▅▅▅█ beats 4/16 medium · avg 69% 11% at its level
vision & documents · #125 of 153 · 100% of its score
Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md), [Gemma 3 12B](gemma-3-12b.md), [Llama 3.2 90B Vision Instruct](llama-3-2-90b-vision.md) here, though they rank higher overall. Trails [Qwen3.5 2B](qwen3-5-2b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
CharXiv (reasoning questions) (ma… 42.5% ▄▂●▆▄▄▆··▄█▂ beats 4/21 medium · avg 38% 17% at its level
CharXiv descriptive questions (ma… 73.9% ▄··▂●▂·▂▄▄▂█ beats 3/14 easy · avg 74% 16% at its level
MMMU (val) (maker) 58.6% ▂···▆●▂▂▄▄▄█ beats 4/16 medium · avg 61% 15% at its level
MathVista (testmini) (maker) 68.2% ▃··▃▅·●█▅▅▅█ beats 4/16 medium · avg 69% 11% at its level
MMStar (maker) 63.9% ▂··▄▂●·▄█▂▄▆ beats 4/16 medium · avg 65% 10% at its level
MMMU Pro (maker) 38.3% ▃·●·▅▃▃▃▅▅▃█ beats 3/16 medium · avg 46% 7.9% at its level
ChartArena chart parsing (English… 23.3% ▁·▁·●▃▁▇▃█·▄ beats 3/22 hard · avg 27% 4.3% blunt · at its level
HallusionBench (maker) 52.9% ▃·▃·▅●▅▅▃▅█▅ beats 4/16 medium · avg 54% 4.1% blunt · at its level
ChartQA (maker) 87.3% ▂··▂·▂▃·●▂▆█ beats 7/17 easy · avg 85% 3.5% blunt
DocVQA (maker) 95.7% ▃··▅▃▅▃█●█▅▅ beats 10/17 very easy · avg 94% 3.2% blunt · maxed out
RealWorldQA (maker) 68.5% ▅▃·●▅▅·█·▃▅█ beats 3/16 easy · avg 70% 3.0% blunt · at its level
CC-OCR (maker) 77.8% ▃··▃▅·▅●·█▅▃ beats 7/13 easy · avg 74% 2.0% blunt · at its level
OCRBench (maker) 86.4% ▅█·▃▃●▃▅▅▃·▃ beats 8/16 easy · avg 84% 1.1% blunt
TextVQA (maker) 84.9% █······▄·▄●▄ beats 4/5 easy · avg 80% 0.6% blunt
source results share of the score
Qwen (submitted / maker-reported) 13 95% ▇▇▇▇▇▇▇▇▇▇▇
ChartArena: Benchmarking Chart Parsing … 1 4% ▇
prior: pull toward the middle 0% ▇
weighted average = 50.0
Licences: ChartArena: Benchmarking Chart Parsing across Languages ([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 7B Instruct (Q4_K_M, needs ~4.7 GB, from [huggingface.co/ggml-org](https://huggingface.co/ggml-org/Qwen2.5-VL-7B-Instruct-GGUF/tree/508edd0afaa66bb9e9f40587acc2184f02daf1f6))
```sh
llama cli -hf ggml-org/Qwen2.5-VL-7B-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-7B-Instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.1 GB) `llama cli -hf ggml-org/Qwen2.5-VL-7B-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 7B Instruct (Q4_K_M, needs ~4.7 GB, from [huggingface.co/ggml-org](https://huggingface.co/ggml-org/Qwen2.5-VL-7B-Instruct-GGUF/tree/508edd0afaa66bb9e9f40587acc2184f02daf1f6))
```sh
lms get https://huggingface.co/ggml-org/Qwen2.5-VL-7B-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 (~8.1 GB) `lms get https://huggingface.co/ggml-org/Qwen2.5-VL-7B-Instruct-GGUF@Q8_0 -y` · 4bit (~5.6 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen2.5-VL-7B-Instruct-4bit -y` · 8bit (~8.9 GB, Mac only) `lms get https://huggingface.co/mlx-community/Qwen2.5-VL-7B-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 7B Instruct (Q4_K_M, needs ~6 GB, from [ollama.com](https://ollama.com/library/qwen2.5vl/tags))
```sh
ollama run qwen2.5vl:7b-q4_K_M
```
Other versions: Q8_0 (~9.4 GB) `ollama run qwen2.5vl:7b-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 7B Instruct (4bit, needs ~5.6 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Qwen2.5-VL-7B-Instruct-4bit/tree/fdcc572e8b05ba9daeaf71be8c9e4267c826ff9b))
```sh
mlx_lm.chat --model mlx-community/Qwen2.5-VL-7B-Instruct-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/Qwen2.5-VL-7B-Instruct-4bit
```
Other versions: 8bit (~8.9 GB, Mac only) `mlx_lm.chat --model mlx-community/Qwen2.5-VL-7B-Instruct-8bit`
Model repository and documentation: [huggingface.co/Qwen](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)
[interactive version](https://whatmodel.sh/model/qwen2-5-vl-7b) · [JSON](https://whatmodel.sh/api?format=json&all=1)