# 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://pulse-forge.io/vs/qwen-vl-vs-internvl
- **1-Click $0 Free Starter CLAUDE.md**: https://pulse-forge.io/api/launch-kit/free-claude-md?repo=qwen-vl/qwen-vl
- **1-Click $4.99 Drop-In Migration Config Pack (3 Core Files - Stripe Checkout)**: https://pulse-forge.io/buy/micro_pass?repo=qwen-vl/qwen-vl&source=md_vs
- **1-Click $9.99 Complete 9-File Production Config .ZIP (Stripe Checkout)**: https://pulse-forge.io/buy/repo_pass_999?repo=qwen-vl/qwen-vl&source=md_vs_999
- **1-Click $49 Builder Repo Hardening & Launch Kit (Stripe Checkout)**: https://pulse-forge.io/buy/instant_boost?repo=qwen-vl/qwen-vl
- **1-Click $149 Category Leader Pro + 30-Day Pin (Stripe Checkout)**: https://pulse-forge.io/buy/category_leader?repo=qwen-vl/qwen-vl
- **Specimen #1 Production Config Preview (`CLAUDE.md`)**: `+ [CLAUDE.md] max_context_tokens: 9400 | ignore: node_modules, dist, .git, *.lock (-58% token bloat)`

## 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