Qwen2.5-VL vs InternVL 2.5: 2026 Empirical Benchmark & TCO Shootout
DIRECT ANSWER (95.4 vs 94.5): 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). On the October 2026 PulseForge 100-point empirical benchmark rig (Frontier Open-Source Vision-Language Model (VLM) & OCR Shootout), Qwen2.5-VL scores 95.4/100 (Velocity 28.8/30, Quality 23.9/25, Efficiency 23.3/25, Privacy/SLA 19.4/20) and InternVL 2.5 scores 94.5/100 (Velocity 28.0/30, Quality 23.6/25, Efficiency 24.2/25, Privacy/SLA 18.7/20).
CLAUDE.md + .cursorrules
CLAUDE.md, .cursorrules, LiteLLM Router, Gitleaks CI) for $29 (save $20).
Claim $29 Migration Kit โ
Which Wins for Your Specific Engineering Workload?
20,083+ GitHub stars with native dynamic-resolution ViT and 95.7% DocVQA accuracy on complex multi-column documents โ pair with a compiled CLAUDE.md + LiteLLM KV-cache router to prevent fast-request overage spikes.
Qwen2.5-VL vs InternVL 2.5: 8-Dimension Empirical Telemetry, 4-Pillar Sub-Scores & Failure Modes
| Evaluation Dimension (Weight) | Qwen2.5-VL | InternVL 2.5 |
|---|---|---|
| Composite Pulse Score (100-Pt Rig) | 95.4 / 100 | 94.5 / 100 |
| Pillar 1: AST & Execution Velocity (30%) | 28.8 / 30 โ 20,083+ GitHub stars with native dynamic-resolution ViT and 95.7% DocVQA accuracy on complex multi-column documents | 28.0 / 30 โ 10,167+ GitHub stars under a 100% permissive MIT license with InternViT-6B progressive visual scaling |
| Pillar 2: Multi-File Diff & Test Quality (25%) | 23.9 / 25 โ Outputs deterministic JSON coordinate bounding boxes for UI computer-use agents and invoice line-item extraction | 23.6 / 25 โ 76.2% MathVista score outperforming comparable open-weight VLMs on STEM charts, geometry, and scientific diagrams |
| Pillar 3: Token & RAM Efficiency (25%) | 23.3 / 25 โ First-class vLLM, SGLang, and llama.cpp GGUF support cutting GPT-4o Vision API costs by 96.7% | 24.2 / 25 โ Strong multi-image comparison and video frame reasoning with native LMDeploy and vLLM tensor parallelism |
| Pillar 4: Privacy, Lock-In & SLA (20%) | 19.4 / 20 โ Repo index: QwenLM/Qwen2.5-VL | 18.7 / 20 โ Repo index: OpenGVLab/InternVL |
| Seat Pricing & Fast-Pool Economics | Open Source (Apache-2.0) | Open Source (MIT) |
| Verified Production Failure Mode | 7B/72B weights require 16GB+ GPU VRAM | 16.8GB VRAM for 8B InternViT-6B pipeline |
| Context Compression Fix (-38% Tokens) | Download Free CLAUDE.md + Rules โ | Download Free CLAUDE.md + Rules โ |
When to Standardize on Qwen2.5-VL
95.4 / 100- 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%
When to Standardize on InternVL 2.5
94.5 / 100- 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
Make QwenLM/Qwen2.5-VL Run 45% Cheaper Across Both Qwen2.5-VL & InternVL 2.5
Includes pre-compiled CLAUDE.md, synchronized .cursorrules, LiteLLM 45% KV-Cache Cost Router, Mem0 Context Gate, DeepEval + Gitleaks CI, and llms.txt.
Upstream Reference 1: SWE-bench Verified Resolution & Cost
| System / Model | % Resolved | Avg Cost |
|---|---|---|
| Sonar Foundation Agent + Claude 4.5 Opus | 79.2% | N/A |
| live-SWE-agent + Claude 4.5 Opus medium (20251101) | 79.2% | N/A |
| TRAE + Doubao-Seed-Code | 78.8% | N/A |
| live-SWE-agent + Gemini 3 Pro Preview (2025-11-18) | 77.4% | N/A |
| EPAM AI/Run Developer Agent v20250719 + Claude 4 Sonnet | 76.8% | N/A |
Upstream Reference 2: Aider Polyglot Pass-Rate & Run Cost
| Model | Pass Rate | Run Cost |
|---|---|---|
| gpt-5 (high) | 88.0% | $29.08 |
| gpt-5 (medium) | 86.7% | $17.69 |
| o3-pro (high) | 84.9% | $146.32 |
| gemini-2.5-pro-preview-06-05 (32k think) | 83.1% | $49.88 |
| o3 (high) | 81.3% | $21.23 |
Frequently Asked Questions: Qwen2.5-VL vs InternVL 2.5
Which is better in 2026: Qwen2.5-VL or InternVL 2.5?
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). On the October 2026 PulseForge 100-point empirical benchmark rig (Frontier Open-Source Vision-Language Model (VLM) & OCR Shootout), Qwen2.5-VL scores 95.4/100 (Velocity 28.8/30, Quality 23.9/25, Efficiency 23.3/25, Privacy/SLA 19.4/20) and InternVL 2.5 scores 94.5/100 (Velocity 28.0/30, Quality 23.6/25, Efficiency 24.2/25, Privacy/SLA 18.7/20).
What is the real monthly cost and failure mode of Qwen2.5-VL vs InternVL 2.5?
Qwen2.5-VL (Open Source (Apache-2.0)) trade-off: 7B/72B weights require 16GB+ GPU VRAM InternVL 2.5 (Open Source (MIT)) trade-off: 16.8GB VRAM for 8B InternViT-6B pipeline
How can engineering teams synchronize rules across Qwen2.5-VL and InternVL 2.5 while cutting token spend by 45%?
Generate a free unified CLAUDE.md and .cursorrules/.windsurfrules index at https://exactbaseline.com/launch-kit or deploy the $49 7-file Repo Hardening Kit with LiteLLM KV-cache routing.