DeepSeek-OCR, a novel model by DeepSeek, challenges traditional assumptions about AI models by using Contexts Optical Compression to process information visually rather than through text tokens, thereby improving efficiency. Unlike current models that process long sequences of text tokens, DeepSeek-OCR compresses information into dense visual tokens that capture typography, layout, and spatial relationships, allowing the model to achieve the same understanding with significantly fewer computation steps. Featuring a visual encoder and a language decoder, DeepSeek-OCR demonstrates impressive performance, retaining high accuracy even at substantial compression levels and outperforming established benchmarks with fewer tokens. By offering a new paradigm for AI efficiency, the model suggests that visual inputs may become a more effective means of information processing, potentially enabling large language models to handle more extended contexts and conversations efficiently. This approach not only reduces computational costs but also introduces a promising direction for building more efficient long-context AI systems.