BERT vs GPT - Understanding Encoder and Decoder Models

BERT and GPT are two of the most influential LLM architectures, each built on the Transformer but designed for different purposes. Knowing the difference helps you choose the right model and understand why models like Gemini and Claude behave the way they do.

BERT (Bidirectional Encoder Representations from Transformers) uses only the encoder stack. It reads text in both directions simultaneously, making it ideal for classification, named entity recognition, and question answering. GPT (Generative Pre-trained Transformer) uses only the decoder stack and reads text left to right, making it ideal for text generation, summarisation, and chat.

The below example uses the Hugging Face transformers library to demonstrate both architectures on a ShopMax India product review task - BERT for sentiment classification and GPT-2 for text generation.

It gives the following output,

BERT Sentiment Analysis (encoder-only):
  [POSITIVE] ShopMax delivered my laptop in 2 days. Excellent...
  [NEGATIVE] The product was damaged and customer support did ...
  [NEGATIVE] Average quality for the price. Nothing special....

GPT Text Generation (decoder-only):
  ShopMax India offers premium electronics at competitive prices
  across Mumbai, Delhi, and Bangalore with same-day delivery
  options for orders above Rs 5000.

Encoder-only models like BERT excel at understanding tasks where the full context is available. Decoder-only models like GPT excel at generating coherent text token by token. Modern frontier models like Gemini 2.0 and Claude 3.5 use encoder-decoder or decoder-only architectures at massive scale with instruction tuning, enabling them to handle both understanding and generation tasks in a single model.