DeepSeek's Vision AI Journey: From VL Research to Vision-Exp Model Explained

DeepSeek's latest vision-capable model, deepseek-v4-flash-vision-exp, is the result of years of structured visual AI research rather than a sudden capability addition. The company's 2024 DeepSeek-VL paper outlined a practical vision-language framework targeting real-world inputs such as PDFs, web screenshots, OCR, and charts, built around a hybrid vision encoder and a staged training process. A key design principle was preserving the language model's reasoning abilities by gradually introducing multimodal data, with final pretraining using roughly 70% text and 30% visual content. DeepSeek-VL2 later added dynamic image tiling and a Mixture-of-Experts language component to handle higher-resolution and varied-aspect-ratio inputs more effectively. The analysis, sourced from DeepSeek's public research papers, distinguishes between what the published work discloses, what current API documentation states, and what remains unverified about the newest model's training data.