# Qwen2.5-VL vs InternVL 2.5: 2026 Empirical Benchmark & Cost Shootout

> **Direct Answer (October 2026 Verdict)**: Qwen2.5-VL wins on dynamic native-resolution document OCR (95.7% DocVQA), structured JSON invoice/UI bounding-box extraction, and hour-long video temporal grounding across 20.0k+ GitHub stars (95.4), while InternVL 2.5 wins on permissive MIT licensing and scientific/mathematical diagram reasoning (76.2% MathVista) via InternViT-6B across 10.1k+ GitHub stars (94.5).

- **Category**: Frontier Open-Source Vision-Language Model (VLM) & OCR Shootout
- **Canonical URL**: https://exactbaseline.com/vs/qwen-vl-vs-internvl
- **1-Click $49 Builder Repo Hardening & Launch Kit (Stripe Checkout)**: https://exactbaseline.com/buy/instant_boost
- **1-Click $149 Category Leader Pro + 30-Day Pin (Stripe Checkout)**: https://exactbaseline.com/buy/category_leader

## Qwen2.5-VL Key Advantages
- 20,083+ GitHub stars with native dynamic-resolution ViT and 95.7% DocVQA accuracy on complex multi-column documents
- Outputs deterministic JSON coordinate bounding boxes for UI computer-use agents and invoice line-item extraction
- First-class vLLM, SGLang, and llama.cpp GGUF support cutting GPT-4o Vision API costs by 96.7%

## InternVL 2.5 Key Advantages
- 10,167+ GitHub stars under a 100% permissive MIT license with InternViT-6B progressive visual scaling
- 76.2% MathVista score outperforming comparable open-weight VLMs on STEM charts, geometry, and scientific diagrams
- Strong multi-image comparison and video frame reasoning with native LMDeploy and vLLM tensor parallelism