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BERT represents the encoder model, while GPT represents self-vectorization. These two models symbolize distinct approaches within the Transformer architecture, and their core difference lies in whether they look at the entire sentence or only the past. Encoders process the whole sentence at once, analyzing the relationships between words in both directions to deeply understand structure and meaning. This makes them ideal for tasks like classification or search, where precise understanding of input is key. On the other hand, models like GPT rely solely on previously seen words. They work by predicting the next word one at a time, without access to future context. As a result, they excel at generating text in a natural, step-by-step manner, especially when the goal is to continue a passage or conversation.