BERT vs. GPT

BERT (Bidirectional Encoder Representations from Transformers) and GPT (Generative Pre-trained Transformer) are both representative language models based on the Transformer.[1][2][3] Both were published in 2018, but their architectural design philosophies are contrasting, and the tasks they excel at differ. Understanding which type of model is being used helps you accurately grasp the characteristics and appropriate use of AI tools.

The Difference in Design Philosophy Between BERT and GPT

Both BERT and GPT are based on the Transformer, but since their purposes differ, the architectures and training methods they use are contrasting.

graph LR
    subgraph BERT["BERT (Encoder-Only)\nUnderstands context bidirectionally"]
        B1["[CLS] Yesterday"] --> B2["in Tokyo"] --> B3["[MASK]"] --> B4["I ate"]
        B3 -.->|"References context from both directions"| B1
        B3 -.-> B4
    end
    subgraph GPT["GPT (Decoder-Only)\nGenerates text left to right"]
        G1["Yesterday"] --> G2["in Tokyo"] --> G3["ramen"] --> G4["I ate"]
        G4 -.->|"References only past tokens"| G1
    end

BERT (Bidirectional Encoder Representations from Transformers)

BERT is an Encoder-Only language model published by Google in 2018.[2]

Key Features

Bidirectional Encoder: When processing each token, BERT uses context from both the left and right of that token.[2]

Pre-training Methods

BERT uses two tasks for pre-training.

Masked Language Model (MLM): The BERT paper describes replacing part of the input with [MASK] tokens and training the model to predict the masked tokens.[2]

Input: "I [MASK] ramen yesterday"
Prediction: "ate", "ordered", "had"...

Next Sentence Prediction (NSP): The BERT paper also used a task where the model predicts whether a second sentence follows the first.[2]

Tasks BERT Excels At

Task	Description	Example
Text classification	Determine a text’s category	Positive/negative sentiment analysis
Named Entity Recognition (NER)	Identify person names, place names, etc. in text	”Tanaka lives in Tokyo”
Extractive QA	Extract the answer location from a passage	Finding specific information in a document
Sentence similarity	Determine how similar two sentences are	Duplicate content detection, improving search accuracy

GPT (Generative Pre-trained Transformer)

GPT is a Decoder-Only language model published by OpenAI in 2018.[3] GPT-3 is a representative later example of scaling the same autoregressive direction.[4]

Key Features

Unidirectional Decoder: When generating each token, GPT uses preceding tokens to predict the next token. This is called autoregressive.[3][4]

Pre-training Method

Next-token prediction (Causal Language Modeling): The model is trained on the task of predicting the next token in a given text.

Input: "Yesterday, in Tokyo"
Prediction: "I had ramen", "I met a friend", "there was a meeting"...

Repeating this training on large text datasets gives the model the ability to generate text that follows the input.[3][4]

Tasks GPT Excels At

Task	Description	Example
Text generation	Generate natural text following an input	Automatic writing of emails, articles, code
Dialogue / chat	Continue a conversation while maintaining context	ChatGPT, Claude and other conversational AIs
Summarization	Condense long text	Summarizing meeting notes or papers
Translation	Convert text to another language	Creating multilingual content
Code generation	Generate code from natural language instructions	Development assistance like GitHub Copilot

Detailed Comparison Table: BERT vs. GPT

Comparison	BERT	GPT-family
Developer	Google (2018)	OpenAI (2018–)
Architecture	Encoder-Only	Decoder-Only
Context direction	Bidirectional (both left and right)	Unidirectional (left only)
Training method	MLM + NSP	Next-token prediction
Best tasks	Text understanding, classification, NER	Text generation, dialogue
Main applications	Search, sentiment analysis, information extraction	Chatbots, code generation
Representative examples	BERT, RoBERTa, ALBERT	GPT, GPT-3, and related autoregressive models

Derivative Models and Their Positioning

Following BERT and GPT, many derivative and successor models were developed.

Key BERT Derivatives

Model	Developer	Features
RoBERTa	Meta AI (2019)	Improved BERT. Removes NSP and trains on more data
ALBERT	Google (2019)	Reduces BERT’s parameters for a lighter footprint
DistilBERT	Hugging Face (2019)	A smaller BERT-style model created with knowledge distillation
ELMo	Allen Institute (2018)	Bidirectional model before BERT. LSTM-based

Key GPT-Family Derivatives

Model	Developer	Features
GPT	OpenAI (2018)	Early model combining unsupervised pre-training and task-specific fine-tuning
GPT-2	OpenAI (2019)	Scaled autoregressive text generation
GPT-3	OpenAI (2020)	175B-parameter model demonstrating few-shot learning

Encoder-Decoder Models (Using Both)

Model	Developer	Features
T5	Google (2020)	Processes all tasks as Text-to-Text
BART	Meta AI (2019)	Strong at summarization and translation

Practical Usage Guide

graph TD
    Task["What kind of task?"]
    Task -->|"Analyze / classify existing text"| BERT_Use["Use BERT-series\nSentiment analysis · NER · Search"]
    Task -->|"Generate / have dialogue"| GPT_Use["Use GPT-series\nChatGPT · Claude · Copilot"]
    Task -->|"Translation / summarization (both long input and output)"| T5_Use["Use Encoder-Decoder\nT5 · BART"]

When BERT-series is appropriate:

• Wanting to classify large amounts of reviews/feedback as positive or negative
• Wanting to automatically extract assignee names, dates, and case numbers from customer emails
• Wanting to improve semantic search accuracy over internal documents

When GPT-series is appropriate:

• Wanting to build a conversational customer support system
• Wanting to automatically generate text or code based on user instructions
• Wanting to summarize, translate, or convert existing content to a different format

Summary

• BERT is Encoder-Only and understands context bidirectionally, excelling at text analysis and classification tasks
• GPT is Decoder-Only and autoregressively generates text, excelling at generation and dialogue tasks
• Both are based on the Transformer, but their purposes and designs are opposite
• In practice, the basic rule is: “analysis/classification” → BERT-series, “generation/dialogue” → GPT-series

Frequently Asked Questions

Q: Is ChatGPT the same as GPT?

A: No. GPT is a language-model family developed by OpenAI.[3][4] ChatGPT is a chat service provided by OpenAI, and its available underlying models can change over time.[5]

Q: Is BERT still used today?

A: BERT-style models remain useful for text classification, named entity recognition, extractive QA, and similar understanding tasks. In practice, compare them with generative models based on task quality, cost, and deployment constraints.

Q: Which model is “smarter”?

A: “Smartness” varies by task. BERT-style models can be appropriate for classification and extraction, while autoregressive generative models can be appropriate for open-ended generation and dialogue. Choosing the right model for the use case is important.

Q: Is Llama BERT-series or GPT-series?

A: Llama-family models are generally treated as decoder-only autoregressive language models, so their design is closer to GPT-style models than to BERT-style models. Check the distributor’s official materials for each specific model.

References

1. Ashish Vaswani et al., Attention Is All You Need, June 12, 2017
2. Jacob Devlin et al., BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding, October 11, 2018
3. Alec Radford et al., Improving Language Understanding by Generative Pre-Training, 2018
4. Tom B. Brown et al., Language Models are Few-Shot Learners, May 28, 2020
5. OpenAI, Introducing ChatGPT, November 30, 2022