According to Andrej Karpathy, the DeepSeek-OCR paper is a strong OCR model and more importantly highlights why pixels might be superior to text tokens as inputs to large language models, emphasizing model efficiency and input fidelity, source: Andrej Karpathy on X, Oct 20, 2025. He states that rendering text to images and feeding pixels can deliver greater information compression, enabling shorter context windows and higher efficiency, source: Andrej Karpathy on X, Oct 20, 2025. He adds that pixel inputs provide a more general information stream that preserves formatting such as bold and color and allows arbitrary images alongside text, source: Andrej Karpathy on X, Oct 20, 2025. He argues that image inputs enable bidirectional attention by default instead of autoregressive attention at the input stage, which he characterizes as more powerful for processing, source: Andrej Karpathy on X, Oct 20, 2025. He advocates removing the tokenizer at input due to the complexity and risks of Unicode and byte encodings, including security or jailbreak issues such as continuation bytes and semantic mismatches for emojis, source: Andrej Karpathy on X, Oct 20, 2025. He frames OCR as one of many vision-to-text tasks and suggests many text-to-text tasks can be reframed as vision-to-text, while the reverse is not generally true, source: Andrej Karpathy on X, Oct 20, 2025. He proposes a practical setup where user messages are images while the assistant response remains text and notes outputting pixels is less obvious, and he mentions an urge to build an image-input-only version of nanochat while referencing the vLLM project, source: Andrej Karpathy on X, Oct 20, 2025.