Open source vision-language models (VLMs) have advanced rapidly in 2025, bringing state-of-the-art multimodal AI to a wider research and developer audience. VLMs combine visual and textual reasoning, enabling applications across image captioning, visual question answering (VQA), document understanding, OCR, video analysis, and more.
What Are Open Source VLMs?
A Vision-Language Model (VLM) processes and reasons about both visual (images, videos) and textual data. Open source VLMs release code, weights, and often training details, usually under permissive licenses (e.g., Apache 2.0), allowing community modification, deployment, and advancement.
Top Open Source VLMs in 2025
| Model Name | Parameter Sizes | Vision Encoder | Key Features | License |
|---|---|---|---|---|
| Qwen 2.5 VL | 7B, 72B | Custom ViT | Video support, object localization, 29 languages | Apache 2.0 |
| Gemma 3 | 4B, 12B, 27B | SigLIP | Pan & scan, high-res, 128k context, multilingual | Open Weights |
| Llama 3.2 Vision | 11B, 90B | Vision Adapter | 128k context, strong OCR, doc understanding, VQA | Community |
| Falcon 2 11B VLM | 11B | CLIP ViT-L/14 | Fine detail, dynamic encoding, multilingual | Apache 2.0 |
| DeepSeek-VL2 | 1B, 4.5B | SigLIP-L (MoE) | Scientific reasoning, small/efficient, edge ready | Open Source |
| Pixtral | 12B | Not specified | Multi-image input, native resolution, strong instructions | Apache 2.0 |
| Phi-4 Multimodal | 1.3B+ | Not specified | Reasoning, lightweight, edge device potential | Open Source |
| InternVL3-78B | 78B | Not specified | 3D reasoning, top scores on multimodal benchmarks | Open Weights |
| Ovis2-34B | 34B | Not specified | Computation-efficient, competitive MMBench performance | Not specified |
Key Research Themes
- Rapid Capacity Growth: Open VLMs now range from compact (1-4B) to powerful (70B+) models, supporting tasks like scientific reasoning, detailed OCR, and multilingual VQA.
- Video Capabilities: Qwen 2.5 VL and recent models support video input and temporally-aware VQA, a major leap for open source.
- Benchmark Performance: Leading open VLMs achieve strong performance on MathVista, MMMU, and MMBench, closing the gap with leading proprietary models like GPT-4o and Gemini 2.5 Pro.
- Flexibility: Many models now offer very long context windows (up to 128k tokens) for processing large documents or batch image/video data.
- Real-world Use: Small, resource-efficient models (e.g., DeepSeek-VL2, Phi-4) extend VLMs to edge and on-device deployment.
- Licensing: Most top open VLMs are released under open or "community" licenses, though individual terms and restrictions can vary.
Impact and Future Directions
Open source VLMs are democratizing access to advanced multimodal AI, supporting:
- Research transparency, reproducibility, and community benchmarking.
- Greater customization, domain adaptation, and edge deployment.
- Lower cost, vendor independence, and privacy for sensitive applications.
Continued innovation is expected in fine-tuning tools, agentic (interactive) multimodal systems, and enhanced video/document analysis. The open source VLM ecosystem has become highly competitive and is now a leader—not a follower—within AI research and industry.