GPT-3 vs. BERT: Ending The Controversy

Natural language models BERT and GPT-3 thrive in the AI industry. Although GPT-3 has become extremely popular among users, and its latest version has already caused a stir, BERT is still their main competitor. Both models display high accuracy levels in performing NLP tasks due to their similar architectures and powerful performance. However, if you’re unsure which is better, this article compares BERT vs. GPT-3 and analyzes their strengths and weaknesses.

GPT-3 vs. BERT: Ending The Controversy

What is GPT-3?

ChatGPT (and its updated version GPT-3) is an autoregressive language model that stands for Generative Pre-trained Transformer 3 developed by OpenAI. Put simply, this autoregressive model predicts the next element in the text based on the previous ones. It uses transformer architecture (more about it later) to generate human-like text based on inputs. ChatGPT is widely used for natural language processing (NLP) tasks, such as text summarization or language translation.

ChatGPT belongs to the Large Language Models (LLM) category as it is pre-trained on 570 gigabytes of text from the Internet, enabling it to learn grammar, syntax, facts, and logic. It typically has 175 billion parameters. That sounds like a lot, isn’t it? What else makes GTP-3 so impressive is its ability to adapt to changes in a conversation, regardless of how complex the request is. 

Due to its versatility and endless capabilities, ChatGPT- 3 quickly captured the hearts of users around the world. Also, ChatGPT is used by brands like Salesforce, Duolingo, Microsoft, or Slack, and by a number of AI content-writing tools, including Jasper, Simplified, or Kafkai.

Some of the most common use cases of GPT-3 are:

  • Building or debugging a part of the code;
  • Generating machine learning code;
  • Providing customer support via an AI conversation platform;
  • Generating content;
  • Creating mockups of websites;
  • Performing malicious prompt engineering;
  • Translating and interpreting languages;
  • Providing data analysis and insights generation;

However, Open AI did not stop on that. Soon after the launch of GPT-3, the company rolled out another version of the model, and here is its brief overview.

Extension of GPT: the newest GPT-4 version 

Generative Pre-trained Transformer 4, or GPT-4, is a multimodal large language model released by OpenAI on March 14, 2023. Its main innovation is that the GPT-4 model can transform an image into text and understand it. Another interesting thing to know about GPT-4 is the number of training parameters used. While OpenAI has not disclosed how many parameters GPT-4 was trained on, some sources suggest it is close to 100 trillion. Thus, this model can handle more complex tasks with greater accuracy and precision and be more creative, intelligent, and reliable. 

Some of the most outstanding features of GPT-4 are:

  • Multimodal AI model: GPT-4 can analyze both texts and images as inputs. In contrast, GTP-3 processes only text inputs;
  • More training data: GPT-4 was trained on an enormous amount of data, including texts from books, Wikipedia, articles, and other online resources;
  • More input and output: GPT-4 has a maximum word count of 25.000 for both input and output. While GPT-3 is limited to 3000 words for input and output;

After the GPT-4 launch, users have shared some amazing things they’ve done with it, such as the creation of new languages or complex app animations. So far, some companies, including Duolingo and Khan Academy, have already integrated GPT-4 into their operations.

What Is BERT?

BERT, short for Bidirectional Encoder Representations from Transformers, is a language model developed in 2018 by Google. It was pre-trained on many unlabeled texts, including Wikipedia (2.500 million words) and Book Corpus (800 million words). But what makes this exact model stand out?

Traditional NLP models often analyze text one-way, either left-to-right or right-to-left, which may limit their understanding of context. BERT differs because it simultaneously reads both directions, which is known as bi-directionality. Considering the words before and after a target word in a sentence can help the model better capture the context. Thus, BERT is best suited to sentiment analysis and natural language understanding (NLU).

Also, you can use BERT for a large variety of other language tasks:

  • Question answering;
  • Text prediction;
  • Text generation;
  • Summarization.

How does BERT work?

BERT is based on the transformer architecture. The key element of this architecture (in the case of BERT) is an encoder that consists of multiple layers, such as the self-attention mechanism and the feed-forward neural network. The self-attention mechanism allows the model to understand the relationships between all the words in an input sequence. The feed-forward neural network processes each input word independently and in parallel, thus enabling bidirectional understanding. As a result, the architecture based on the encoder allows BERT to capture the context of the text more accurately, leading to better performance in various tasks.

Another important aspect of BERT is its pre-training method. Using massive amounts of text data, the model is pre-trained on masked language modeling and next-sentence prediction tasks:

  • Masked Language Model (MLM): a model hides (masks) a word and predicts the hidden word based on its context;
  • Next Sentence Prediction: predicts if two given sentences have an underlying logical link or are random.

These algorithms aim to mask a word in a sentence and then have the program predict which word is masked (hidden) based on context. The process is essential for the model’s success, enabling it to understand natural language just like humans do. After pre-training, BERT adapts to the growing search content and queries and can be later fine-tuned to meet the user’s needs. This process is called transfer learning.

Before we compare BERT vs. GPT-3 more closely, let’s discuss one more AI language giant – BART. 

BART: a brief overview

BART (bidirectional and auto-regressive transformers) is a language model developed by Facebook in 2019. It generates high-quality natural language text and performs well on various NLP tasks. BART combines both GPT and BERT components: encoder (BERT) + decoder (GPT) + noise transformations. 

BART uses a transformer-based architecture with a bidirectional (like BERT) and unidirectional (like GPT) text process. During pre-training, BART follows a two-step process: corrupts the input using various noise transformations and reconstructs the original text from the corrupted version. Using noise transformations, BART is exposed to multiple forms of input corruption, allowing it to adapt to incomplete data and learn robust patterns. Training BART to find the original input will enable it to generate high-quality text that captures the underlying language structure and meaning.

BART can be used for:

  • Machine translation;
  • Question-answering;
  • Text summarization;
  • Sequence classification.

BART can be fine-tuned for specific NLP tasks, such as creating a medical conversational chatbot or SQL queries. Because the model has 140 million parameters and has already been pre-trained on large text data, it does not require fine-tuning on large datasets. 

Now, let’s get back to ChatGPT and BERT.

Differences between GPT-3 and BERT

There are quite a few differences between BERT and GPT-3, and the most obvious are:

Main goal

ChatGPT-3 generates text based on the context and is designed for conversational AI and chatbot applications. In contrast, BERT is primarily designed for tasks that require understanding of the meaning and context of words. So, it is used for such NLP tasks as sentiment analysis and question answering.


Both language models use a transformer architecture that consists of multiple layers. GPT-3 has an autoregressive transformer decoder. It means the model generates text sequentially from left to right and in one direction, predicting the next word based on the previous one.

BERT, on the contrary, has a transformer encoder and is designed for bidirectional context representation. It means that it processes text both left-to-right and right-to-left, thus capturing context in both directions.

Model size

GPT-3 is made up of 175 billion parameters, while BERT has 340 million parameters. It means GPT-3 is significantly larger than its competitor due to its much more extensive training dataset size.


GPT-3 is typically fine-tuned on specific tasks during training with task-specific examples. It can be fine-tuned for various tasks by using small datasets.

BERT is pre-trained on a large dataset and then fine-tuned on specific tasks. It requires training datasets tailored to particular tasks for effective performance.

GPT-3 vs. BERT: capabilities comparison

To answer the question which model is better, BERT vs. GPT-3, we’ve compiled all the main information in a brief comparison table. 

ObjectiveGenerates human-like textRecognizes sentiment
ArchitectureUnidirectional: it processes text in one direction using a decoderBidirectional: it processes text in both directions using an encoder 
Size175 billion parameters340 million parameters
Training dataIt is trained on language modeling by using hundreds of billions of wordsIt is trained on masked language modeling and next sentence prediction by using 3.3 billion words
Pre-trainingUnsupervised pre-training on a large data
Unsupervised pre-training on a large corpus of text
Fine-turningDoes not require but can be fine-tuned for specific tasksRequires fine-tuning for specific tasks
Uses casesCoding
ML code generation
Chatbots and virtual assistants
Creative storytelling
Language translation
Sentiment analysis
Text classification
Question answering
Machine translation
AccuracyBased on the SuperGLUE benchmark, 86.9%Based on the GLUE benchmark, 80.5%

Final thoughts

BERT and GPT-3 language models are tangible examples of what AI is capable of and we have already benefited from them in real life. However, as these models evolve and become more intelligent, it is critical to keep in mind their limitations and pitfalls, which are and will be present. Hence, people can delegate some of their responsibilities to AI and use language models as business assistants, but these models will highly unlikely replace humans completely.

Thus, the competition of BERT vs. GPT-3 is not based on one model being better than the other. Rather, it is about understanding each model’s unique characteristics and choosing the right tool for your own needs.


What is GPT-3?

GPT-3, which stands for Generative Pre-trained Transformer 3, is an autoregressive language model developed by OpenAI. Put simply, this autoregressive model predicts the next element in the text based on the previous ones. It uses transformer architecture (more about it later) to generate human-like text based on inputs. Moreover, it is widely used for natural language processing (NLP) tasks, such as text summarization or language translation.

How does GPT-3 work?

Due to the generative nature of GPT-3, it uses a machine learning model based on neural networks to predict what text it will likely interpret. The system first goes through an unsupervised pre-training period using a large dataset from the Internet. Then the model goes through a supervised fine-tuning period to guide the model. During training, trainers ask a language model a question with a correct output in mind. If the model provides the wrong answer, the trainer adjusts the model to ensure it provides the right answer.

How many parameters are in GPT-3?

GPT-3 used 175 billion parameters and was trained on different internet sources using hundreds of billions of words. For this reason, GPT-3 has a much larger training dataset than its previous version, making it a more intelligent and versatile tool.

What is Google Bert?

BERT, short for Bidirectional Encoder Representations from Transformers, is an advanced natural language processing (NLP) model developed in 2018 by Google. The model pre-trained on many unlabeled texts, including Wikipedia and Book Corpus. BERT uses bidirectional training and transformer architecture to understand a context better. It is best suited for sentiment analysis and natural language understanding (NLU) tasks. 

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