GPT vs BERT

Last Updated : 19 Aug, 2025

GPT and BERT are two of the most influential architectures in natural language processing but they are built with different design goals. GPT is an autoregressive model that generates text by predicting the next word, while BERT is a bidirectional model that understands context from both directions making it better for comprehension tasks.

GPT: Generative Pre-trained Transformer

GPT (Generative Pre-trained Transformer) while having a similar layered architecture but it uses masked multi head attention instead of standard multi head attention.
This masking hides future tokens from the model during training forcing GPT to only look at previous words when making predictions making it an autoregressive approach.
This design makes GPT excel at text generation since it naturally learns to predict the next word in a sequence.
Like BERT, GPT also starts with text and positional embeddings and processes them through transformer layers of attention, Add and Norm and feed forward networks.
GPT can perform text prediction tasks as well as task classification but its real advantage is generating coherent, human like text step by step based on the input prompt.

BERT: Bidirectional Encoder Representations from Transformers

BERT (Bidirectional Encoder Representations from Transformers) starts with text and positional embeddings which are numerical representations of the words along with their positions in the sentence.
These embeddings pass through multiple transformer layers denoted as Lx each containing a multi head attention mechanism that can attend to all tokens in the input both before and after the current word.
This bidirectional attention allows BERT to understand the full context of a sentence at once rather than processing it sequentially.
After attention the data flows through Add and Norm layers and feed forward networks that further refine the representations.
At the top BERT connects directly to a classifier making it effective for understanding oriented tasks such as text classification, sentiment detection and question answering where context from both directions is important.

Difference between BERT and GPT

Feature	BERT	GPT
Architecture Type	Encoder only Transformer	Decoder only Transformer
Attention Type	Multi head Attention	Masked Multi head Attention
Context Handling	Considers both left and right context simultaneously	Considers only left context
Primary Purpose	Understanding and extracting meaning from text	Generating coherent and context relevant text
Training Objective	Masked Language Modeling (MLM) predicts masked words using full context	Causal Language Modeling predicts the next word based on past words
Typical Output	Classifications, embeddings, extracted answers	Generated sentences, paragraphs or code
Best Suited For	Sentiment analysis, question answering, classification	Story writing, chatbots, code generation, creative tasks

Key Points:

GPT excels in tasks that require text generation. Its autoregressive feature makes it ideal for applications where the generation of coherent and contextually appropriate text is important.
BERT is superior for tasks that require understanding the context and different languages making it suitable for NLP tasks like named entity recognition (NER), question answering and language understanding.