Qwen 2 VL 7B
By Alibaba · China
Overview
Alibaba's Qwen 2 VL 7B — a top-tier open-weight vision model with dynamic resolution, multilingual OCR, and short video understanding.
When to pick this model
- Multilingual OCR and document extraction
- High-resolution image analysis up to 16K pixels
- Short video understanding and summarization
- Chart, diagram, and table parsing
- Apache 2.0 commercial vision pipelines
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 6 GB |
| Q5_K_M | 7 GB |
| Q8_0 | 10 GB |
| FP16 (no quantization) | 18 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
Published benchmark scores
| Benchmark | Score |
|---|---|
| MMMU | 54.1 |
| DocVQA | 94.5 |
| OCRBench | 845 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
Strengths
- Dynamic resolution from 20px up to 16K
- Best-in-class OCR and document handling at 7B
- Apache 2.0 license
- Short video input support
Limitations
- 32k combined text+image context
- Outperformed by Qwen3-VL on newer benchmarks
- Memory pressure scales fast at high resolutions
Architecture & training
Architecture: Dense 7B · M-RoPE vision+text · dynamic resolution · Qwen2-VL
Training: Qwen2-VL multimodal pre-training. Strong in OCR, short video, documents.
The strongest open-weight 7B vision model for OCR and documents — upgrade to Qwen3-VL once it fits your stack.
Quick start
ollama run qwen2-vl:7bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.