BERT vs. GPT-3: Comparing Two Powerhouse Language Models


In this blog post, we'll dive deep into both BERT and GPT-3, comparing their architectures, applications, and impact on the world of AI.

In the realm of Natural Language Processing (NLP), two language models have garnered significant attention in recent years: BERT (Bidirectional Encoder Representations from Transformers) and GPT-3 (Generative Pre-trained Transformer 3). These models represent remarkable advancements in machine learning and have the potential to revolutionize how we interact with and understand language. In this blog post, we’ll dive deep into both BERT and GPT-3, comparing their architectures, applications, and impact on the world of AI. So here we go, BERT vs. GPT-3!

Understanding the Basics

Before we delve into the detailed comparison of BERT Vs. GPT-3, let’s establish a foundational understanding of the two models; and don’t forget, if you need a refresher on Transformers models, you can have one here.

BERT: A Bidirectional Pioneer

BERT, developed by Google AI in 2018, is a bidirectional transformer model. It’s designed to understand context by considering both the left and right words in a sentence or paragraph. This bidirectionality allows BERT to capture the nuances of language comprehensively. It has achieved state-of-the-art results in various NLP tasks, including text classification, named entity recognition, and question-answering.

GPT-3: The Generative Powerhouse

GPT-3, on the other hand, is the third iteration of OpenAI’s Generative Pre-trained Transformer. It’s an autoregressive model, meaning it generates text sequentially, one word at a time. OpenAI’s model is renowned for its impressive generative capabilities. It can write coherent essays, compose poetry, and even mimic the writing style of various authors. With 175 billion parameters, GPT-3 is one of the largest language models in existence.

Architecture Comparison: BERT Vs. GPT-3

BERT Architecture

BERT employs a bidirectional architecture with two main components: the encoder and the transformer. The encoder reads the input text and transforms it into contextualized embeddings, while the transformer processes these embeddings to predict missing words in a masked language modeling task.

Key Features of BERT:

  • Bidirectional context understanding.
  • Masked language modeling pre-training.
  • Transformer architecture with self-attention mechanisms.

GPT-3 Architecture

Inversely, GPT-3 uses a unidirectional autoregressive architecture. It consists of a stack of transformer decoders. During training, it predicts the next word in a sentence given the preceding words. This process is repeated to generate coherent, context-aware text.

Key Features of GPT-3:

  • Autoregressive text generation.
  • Large-scale transformer decoder architecture.
  • Zero-shot and few-shot learning capabilities.


Both BERT and GPT-3 have made significant impacts on various NLP applications, but they excel in different areas. Let’s have a look a BERT Vs. GPT-3 applications.

BERT Applications

BERT is renowned for its capabilities in understanding context and semantics, making it ideal for tasks like:

  • Sentiment analysis.
  • Named entity recognition.
  • Text classification.
  • Question answering.
  • Semantic search.
This model has been widely adopted in search engines, chatbots, and content recommendation systems.

GPT-3 Applications

GPT-3’s strength lies in generative tasks and creative text generation. Its applications include:

  • Content generation.
  • Text completion.
  • Language translation.
  • Chatbots and conversational AI.
  • Text-based games and creative writing.

The model’s ability to mimic various writing styles and generate human-like text has sparked innovative use cases across industries.

BERT vs. GPT-3: Training Data and Scale

The scale of training data and model size plays a crucial role in the performance of these models.

Here you can take a look at BERT Vs. GPT-3 in terms of training data and scale.


  • BERT-base: 110 million parameters.
  • BERT-large: 340 million parameters.

This Large Language Model was trained on the BooksCorpus (11,038 books) and English Wikipedia (2,500M words).


  • GPT-3: 175 billion parameters.

The model was trained on a vast corpus of 300 Billion tokens collected from a weighted combination of the following datasets: Common Crawl (filtered by quality, comprised of 180.4B tokens), WebText2 (55.1B), Books1 (22.8B), Books2 (23.65B), Wikipedia (10.2B).


While both BERT and GPT-3 are groundbreaking, they come with their own set of limitations.

BERT Limitations

  • BERT does not generate text.
  • It relies on task-specific fine-tuning for optimal performance.
  • Slower inference times due to its bidirectional architecture.
  • Limited context window (512 tokens).

GPT-3 Limitations

  • GPT-3 can generate plausible-sounding but incorrect or nonsensical answers.
  • It may exhibit biases present in its training data.
  • The large number of parameters makes it computationally expensive to fine-tune.

Ethical and Practical Considerations

As we compare BERT and GPT-3, it’s essential to consider the ethical and practical implications of using these models.

Ethical Concerns

  • Bias in AI-generated content.
  • Misuse of AI for malicious purposes.
  • Impact on human employment in content creation and customer support.

Practical Considerations

  • Computational resources required for training and inference.
  • Privacy concerns when handling user-generated data.
  • The need for robust fine-tuning and evaluation processes.

The Future of Language Models

The advancements represented by BERT and GPT-3 are only the beginning. The field of NLP is rapidly evolving, with researchers and organizations continually pushing the boundaries of what’s possible.


In the battle of BERT vs. GPT-3, there is no clear winner. These language models cater to different NLP needs, with BERT excelling in understanding context and semantics, and GPT-3 dominating generative tasks. The choice between them depends on the specific application and requirements.

As the field of NLP continues to evolve, it’s exciting to imagine the possibilities that future language models will bring. For now, BERT and GPT-3 remain two of the most influential and powerful tools in the world of AI and natural language processing.

In the end, the real power lies not in choosing one over the other but in harnessing the capabilities of both to create more intelligent and human-like AI systems.