BERT vs GPT Models: Differences, Examples

BERT base BERT Large neural network architectures

Are you intrigued by the world of natural language processing (NLP) and the cutting-edge machine learning models that power it? Have you ever wondered what sets apart two of the most prominent models in the field, Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT)? These models have revolutionized the way machines understand and generate human language, but what exactly differentiates them? In this blog, we will delve into the core architecture, training objectives, real-world applications, examples and more. By exploring these aspects, we’ll learn about the unique strengths and use cases of both models, providing you with insights that can guide your next project or research endeavor.

Differences between BERT vs GPT Models

The following represents the key differences between BERT and GPT models.

ArchitectureUtilizes a bidirectional Transformer architecture, meaning it processes the input text in both directions simultaneously. This allows BERT to capture the context around each word, considering all the words in the sentence.Employs a unidirectional Transformer architecture, processing the text from left to right. This design enables GPT to predict the next word in a sequence but limits its understanding of the context to the left of a given word.
Training ObjectiveTrained using a masked language model (MLM) task, where random words in a sentence are masked, and the model predicts masked words based on the surrounding context. This helps in understanding the relationships between words.Trained using a causal language model (CLM) task, where the model predicts the next word in a sequence. This objective helps GPT in generating coherent and contextually relevant text.
Pre-trainingInvolves two main tasks: masked language modeling and next sentence prediction (NLP). This combination helps BERT in understanding both the intra-sentence and inter-sentence relationships.Pre-trained solely on a causal language model task, focusing on understanding the sequential nature of the text.
Fine-tuningCan be fine-tuned for various specific NLP tasks like question answering, named entity recognition, etc., by adding task-specific layers on top of the pre-trained model.Can be fine-tuned for specific tasks like text generation and translation by adapting the pre-trained model to the particular task.
Bidirectional UnderstandingCaptures the context from both left and right of a word, providing a more comprehensive understanding of the sentence structure and semantics.Understands context only from the left of a word, which may limit its ability to fully grasp the relationships between words in some cases.
Use CasesSuitable for tasks like question answering, named entity recognition, etc.Suitable for tasks like text generation, translation, etc.
Real-World ExampleUsed in Google Search to understand the context of search queries, enhancing the relevance and accuracy of search results.Models like GPT-3 are employed to generate human-like text responses in various applications, including chatbots, content creation, and more.

BERT & GPT Neural Network Architectures

In order to truly understand the differences between BERT and GPT models, it is important to get a good understanding of how their neural network architectures look like.

BERT Neural Network Architectures

The neural network architecture of BERT is categorized into the two main implementations: BERT (Base) and BERT (Large).

BERT base BERT Large neural network architectures

BERT (Base) consists of 12 encoder layers. Each encoder layer contains a multi-head self-attention mechanism and a position-wise fully connected feed-forward network. There are 12 bidirectional self-attention heads in each encoder layer, allowing the model to focus on different parts of the input simultaneously. BERT (Base) has a total of 110 million parameters, making it a sizable model, but still computationally more manageable than BERT (Large).

BERT (Large) is a more substantial model with 24 encoder layers, enhancing its ability to capture complex relationships within the text. With 16 bidirectional self-attention heads in each encoder layer, BERT (Large) can pay attention to even more nuanced aspects of the input. BERT (Large) totals 340 million parameters, making it a highly expressive model capable of understanding intricate language structures.

Both BERT (Base) and BERT (Large) have been pre-trained on the Toronto BookCorpus (800M words) and English Wikipedia (2,500M words), providing a rich and diverse linguistic foundation.

GPT Neural Network Architectures

The foundational GPT model (GPT-1) was constructed with a 12-level Transformer decoder architecture. Unlike the original Transformer model, which consists of both an encoder and a decoder, GPT-1 only utilizes the decoder part. The decoder is designed to process text in a unidirectional manner, making it suitable for tasks like text generation. Within each of the 12 levels, GPT-1 employs a 12-headed attention mechanism. This multi-head self-attention allows the model to focus on different parts of the input simultaneously, capturing various aspects of the sequential text.

Following the Transformer decoder, GPT-1 includes a linear layer followed by a softmax activation function. This combination is used to generate the probability distribution over the vocabulary, enabling the model to predict the next word in a sequence.

The following is the architecture diagram of the GPT foundational model:

GPT model architecture

GPT-1 consists of a total of 117 million parameters. This size makes it a substantial model capable of understanding complex language structures, but still more manageable compared to later versions like GPT-2 and GPT-3.

GPT-1 was pre-trained on the BookCorpus, which includes 4.5 GB of text from 7000 unpublished books of various genres. This diverse and extensive dataset provided a rich linguistic foundation for the model.

Here is the summary of the differences in BERT & GPT neural network architectures:

  • Architecture: BERT utilizes only the encoder part of the Transformer architecture, processing the text in a bidirectional manner. GPT-1, on the other hand, uses only the decoder part of the Transformer architecture, processing the text in a unidirectional manner from left to right.
  • Directionality: BERT is bidirectional, meaning it processes text in both directions simultaneously, whereas GPT-1 is unidirectional, processing text from left to right.
  • Attention Heads and Layers: Both models use multi-head attention, but they differ in the number of layers and heads. BERT has two versions with different configurations, while GPT-1 has a 12-level, 12-headed structure.
  • Training Objective: BERT is trained using a masked language model task and next sentence prediction, while GPT-1 is trained to predict the next word in a sequence.
  • Pre-training Data: Both models are pre-trained on extensive text corpora, but they differ in the specific datasets used.
  • Output Layer: BERT is fine-tuned with task-specific layers, while GPT-1 uses a linear-softmax layer for word prediction.


In the ever-evolving landscape of natural language processing, BERT and GPT stand as two monumental models, each with its unique strengths and applications. Through our exploration of their architecture, training objectives, real-world examples, and use cases, we’ve uncovered the intricate details that set them apart. BERT’s bidirectional understanding makes it a powerful tool for tasks requiring deep contextual insights, while GPT’s unidirectional approach lends itself to creative text generation. Whether you’re a researcher, data scientist, or AI enthusiast, understanding these differences can guide your choice in model selection for various projects.

Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. For latest updates and blogs, follow us on Twitter. I would love to connect with you on Linkedin. Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking. Check out my other blog,
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