DeepSeek has introduced a new model, DeepSeek-OCR, which reimagines document processing by using vision as a compression algorithm for textual information, significantly reducing token usage while maintaining high accuracy. The innovative approach compresses 1,000 text tokens to 100 vision tokens with a 97% decoding accuracy and suggests that efficient context provision is crucial for optimizing results from large language models (LLMs). The model's architecture involves a two-stage process of high-resolution perception and intelligent compression, offering various "zoom levels" for different needs, and proposes a vision memory paradigm for document parsing. While traditional parsing focuses on converting complex document formats into machine-readable text, DeepSeek-OCR offers a potential future where visual compression could either complement or replace certain parsing functions, depending on the application's need for structured data. This new approach could enhance existing document parsing systems like LlamaParse by integrating compression to improve efficiency and accuracy.