Same foundation, opposite directions
GPT and BERT are both transformers, but they use opposite halves of the original design and train on opposite objectives. GPT is decoder-only and reads text strictly left to right, predicting the next token — ideal for generating language. BERT is encoder-only and reads the whole sentence at once in both directions, predicting hidden words — ideal for understanding language. This single split, generation versus comprehension, explains almost every practical difference between the two families.
How each one is trained
GPT trains with autoregressive next-token prediction: given the words so far, guess the next one, then the next. Because future tokens are masked, the model learns to write fluently one step at a time. BERT trains with masked language modelling: random tokens in a sentence are blanked out and the model predicts them using context from both sides. Seeing both left and right gives BERT deep contextual understanding of each word, but it also means BERT was never trained to produce continuous text on its own.
What each is good at
The objectives map directly onto strengths. GPT excels at generation — chat, drafting, summarising, translating, writing code — anywhere you need new text produced fluently. BERT excels at understanding — classification, sentiment analysis, named entity recognition, question answering over a passage, and producing embeddings for search and retrieval. If your task ends with “…and write the answer,” GPT fits; if it ends with “…and label or score this text,” BERT fits.
Bidirectional power, generative limits
BERT’s bidirectional view is its superpower for comprehension: every word’s representation is informed by the entire surrounding sentence, which is why encoders remain a go-to for embeddings and ranking. The trade-off is that BERT cannot generate text naturally, because it has no notion of producing one token after another. GPT accepts a narrower, causal view of context in exchange for the ability to write — a deliberate trade that suits open-ended output.
Which to choose today
For most generative product features, a GPT-style model now does the job and can even handle classification by being prompted. But encoder models stay relevant where efficiency and pure understanding matter: large-scale search, retrieval pipelines, and high-volume text classification where running a small bidirectional encoder is far cheaper than a large generative model. The practical rule: reach for an encoder when you need to understand fixed text at scale, and a decoder when you need to create new text.