Overview

BERT and GPT are both transformer-based language models, but they’re built from opposite halves of the transformer and trained for opposite jobs. BERT uses an encoder trained to fill in masked words using context from both directions, making it suited to understanding text, while GPT uses a decoder trained to predict the next word from only what came before, making it suited to generating text.

Comparison Diagram

BERTGPTBidirectional EncoderAutoregressive Decoderthecat[MASK]onmatsees full sentence context(left + right) to fill the mask-> classification, embeddings, NERthecatsatonmat?each token sees only itself+ prior tokens (causal mask)-> generation, chat, completion

Comparison Table

AspectBERTGPT
ArchitectureEncoder-only transformer stackDecoder-only transformer stack
Pretraining objectiveMasked language modeling: predict randomly hidden tokens, plus next-sentence predictionCausal language modeling: predict the next token given all prior tokens
Attention patternBidirectional self-attention; every token attends to the full sequenceCausal (masked) self-attention; each token attends only to itself and earlier tokens
Output generationOne contextual embedding per input token, produced in a single forward passText generated autoregressively, one token at a time, each output fed back as input
Typical adaptationFine-tuned with a task-specific head on top of the pretrained encoderAdapted via prompting, instruction tuning, or fine-tuning to continue text
Primary use casesClassification, named entity recognition, semantic search, sentence embeddingsOpen-ended generation, chat, code completion, summarization
Inference cost per queryFixed: one pass regardless of desired outputScales with number of generated tokens, each requiring a forward pass

Key Differences

  • BERT’s encoder attends to both left and right context; GPT’s decoder attends only to prior tokens.
  • BERT trains on masked language modeling; GPT trains on next-token prediction.
  • BERT produces embeddings in a single pass; GPT produces text through autoregressive decoding.
  • BERT is optimized for understanding tasks; GPT is optimized for generation tasks.

When to Use Each

BERT

  • Semantic Search & Embeddings: BERT’s bidirectional context produces dense sentence/document vectors well suited to similarity search and retrieval.
  • Classification & NER: A lightweight task head fine-tuned on top of BERT’s embeddings handles sentiment, intent, or entity extraction efficiently.
  • Extractive Question Answering: Seeing the full passage in both directions helps BERT locate the exact answer span within a reference text.

GPT

  • Open-Ended Text Generation: GPT’s autoregressive design is built to produce novel, coherent multi-sentence text like drafts, code, or articles.
  • Conversational Agents: Sequential next-token prediction naturally extends to multi-turn dialogue and chat-style interaction.
  • Few-Shot & Zero-Shot Prompting: Large GPT models can adapt to new tasks purely through prompting, without task-specific fine-tuning.