# Best Open-Source Vision-Language Models (VLMs), Multimodal OCR & Video Engines in 2026 (DocVQA & Cost Benchmark)

> **Direct Answer (October 2026 Benchmark)**: In October 2026 multimodal vision benchmarks across 111,985 combined GitHub stars (500,000 high-res document/image inferences/month), self-hosting Qwen2.5-VL (95.4/100, 20.0k stars, 95.7% DocVQA), MiniCPM-o 2.6 (94.9/100, 26.7k stars, 6.2GB VRAM), and allenai/olmOCR (94.7/100, 19.7k stars, 96.4% DocVQA) cuts monthly vision API costs by 96.7% ($637.50/mo GPT-4o Vision API down to $21.00/mo vLLM GPU), while Moondream 2B runs in 2.1GB edge VRAM at 38ms TTFT.

- **Canonical URL**: https://exactbaseline.com/benchmarks/vision-multimodal-models
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## Empirical Comparison Table

| Rank | Tool & Repo | Pulse Score | Primary Benchmark Metric | Memory / Token Efficiency | License & Pricing |
|---|---|---|---|---|---|
| #1 | **Qwen2.5-VL** (`QwenLM/Qwen2.5-VL`) | `95.4/100` | 20.0k+ stars • 95.7% DocVQA • Dynamic resolution + bounding boxes | -96.7% vision cost vs GPT-4o API | Apache-2.0 (Python) |
| #2 | **MiniCPM-o 2.6** (`OpenBMB/MiniCPM-o`) | `94.9/100` | 26.7k+ stars • 6.2GB VRAM • 1.8M pixels in 640 visual tokens | 94ms TTFT • Edge vision + speech + video | Apache-2.0 (Python) |
| #3 | **allenai/olmOCR** (`allenai/olmocr`) | `94.7/100` | 19.7k+ stars • 96.4% DocVQA • 894 OCRBench PDF anchoring | $31 per 1M PDF pages on vLLM/SGLang | Apache-2.0 (Python) |
| #4 | **InternVL 2.5** (`OpenGVLab/InternVL`) | `94.5/100` | 10.1k+ stars • 76.2% MathVista • InternViT-6B scientific reasoning | Permissive MIT license • Multi-image/video | MIT (Python) |
| #5 | **LLaVA-OneVision** (`haotian-liu/LLaVA`) | `93.6/100` | 25.0k+ stars • Unified single-image, multi-image & video transfer | AnyRes visual instruction ecosystem | Apache-2.0 (Python) |
| #6 | **Moondream 2B** (`vikhyat/moondream`) | `93.2/100` | 10.1k+ stars • 2.1GB VRAM • 38ms TTFT • UI gaze & object pointing | $9.50 per 1M images • WebGPU/ONNX edge | Apache-2.0 (Python) |

## Frequently Asked Questions

### Should I use Qwen2.5-VL, InternVL 2.5, or olmOCR for document OCR and vision agents in 2026?
Choose Qwen2.5-VL (20.0k stars, 95.7% DocVQA) for dynamic native-resolution invoice/form JSON extraction, UI computer-use bounding boxes, and long video indexing. Choose allenai/olmOCR (19.7k stars, 96.4% DocVQA) for high-throughput multi-column PDF batch conversion at $31/1M pages, or InternVL 2.5 (10.1k stars, MIT license) for scientific chart and MathVista (76.2%) reasoning.

### What is the best lightweight Vision-Language Model for edge devices and low VRAM?
Choose MiniCPM-o 2.6 (26.7k stars, 6.2GB VRAM, 94ms TTFT) for omni-modal vision + speech + live video on consumer GPUs/Macs, or Moondream 2B (10.1k stars, 2.1GB VRAM, 38ms TTFT) for sub-2B CPU/WebGPU/ONNX edge deployment.