Advanced Visual Reasoning with DeepSeek-VL and InternVL3

Open-source vision-language models, such as DeepSeek-VL2 and InternVL3, provide a cost-effective, flexible alternative to proprietary models for AI tasks. DeepSeek-VL2, developed by DeepSeek AI, excels in document understanding and OCR tasks, utilizing a Mixture-of-Experts architecture that activates only a fraction of its parameters during inference, making it efficient and competitive against larger models. It performs well in text extraction, document question answering, and chart analysis, with commercial usability and full weight access. Meanwhile, InternVL3, from OpenGVLab, is designed for multimodal reasoning and video analysis, employing "Native Multimodal Pre-Training" to integrate vision and language learning from the start. This model excels in reasoning and video understanding, outperforming proprietary alternatives on benchmarks like MMMU and MathVista. Both models can be deployed locally or on cloud infrastructure, offering predictable costs, full data privacy, and the ability to optimize for latency needs. Deployment on platforms like Modal allows for efficient use of GPU resources, supporting tasks like video analysis and document translation, with options for real-time processing and integration into existing systems.