GPT-3 and GPT-4 are highly advanced language models developed by OpenAI. In this paper, we'll look at their features, similarities, and differences to help customers better understand these two powerful technologies.


  • Introduction to GPT-3 and GPT-4
  • Common features
  • Differences between GPT-3 and GPT-4
  • Limitations

Introduction to GPT-3 and GPT-4


GPT-3 (Generative Pre-trained Transformer 3) is a language model that can process and generate human-like text. It was developed by OpenAI and is currently available via API. GPT-3 is trained on billions of words and is proficient in human speech understanding, word meaning analysis, and independent language generation. GPT-3 supports many languages, not just English.


GPT-4 is the successor of GPT-3 and is based on the GPT-3.5 model. It has been described as OpenAI's most advanced system, producing more confident and useful responses. GPT-4 is currently only available with the paid ChatGPT Plus subscription and as an API for developers.

Common features

Both the GPT-3 and GPT-4 language models offer the following features:

  • Humanoid text generation
  • Natural Language Processing (NLP)
  • Creativity and processing of large amounts of text
  • Extensive use cases, including virtual assistants, chatbots, content creation, and translation
  • API for developers to integrate GPT into their applications

Differences between GPT-3 and GPT-4


GPT-4 is more advanced in creativity than GPT-3. OpenAI claims that GPT-4 is better at creating and collaborating with users on creative projects such as music, screenplays, technical writing, and even learning the user's writing style.

Visual input

GPT-4 can receive images as basic input for interaction, while GPT-3 does not support visual input.

Longer context

GPT-4 can process up to 25,000 words of text from the user, while GPT-3 has a shorter length context.


GPT-4 is significantly safer to use than the previous generation. Produces 40% more correct responses and is 82% less likely to respond to requests for disallowed content.


Both models share some limitations:

  • Bias: Models can generate biased content based on training data
  • Memory: Both models lack long-term memory and cannot maintain context between different interactions
  • Full context: Both models lack full context and natural common sense, which can lead to inconsistent or inaccurate text

Increasing Token Capacity: Comparison of GPT-3 and GPT-4

Tokens are fundamental units of text used to represent and analyze linguistic structure in a natural language processing (NLP) system. Tokens can be thought of as pieces of text, such as words, punctuation or symbols, that are separated and organized during language parsing. 

They are important because they allow the model to work efficiently and accurately on different speech elements and to understand the grammar, meaning and context of the text. Within NLP models such as GPT-3 and GPT-4, a token does not necessarily correspond to a single character or word, but can also represent a substring of a word, a symbol or a combination of characters, depending on the language and of the representation used. 

Counting tokens is essential for monitoring computational resource usage and the limits imposed by AI models, as performance and costs are often based on the number of tokens processed and generated during interactions with the model.

In comparisons between GPT-3 and GPT-4, it is relevant to underline that the maximum limit of manageable tokens differs between the two models. GPT-3 can handle up to 8,000 tokens, while GPT-4 can handle considerably more, going up to 32,000 tokens. 

This difference in token bounds can affect model performance, particularly when working with longer, more complex text. GPT-4, due to its higher limit, can handle and generate larger text sequences, while maintaining context and consistency more effectively than GPT-3.


Both GPT-3 and GPT-4 offer impressive capabilities in text generation and natural language processing. However, GPT-4 surpasses GPT-3 in terms of creativity, support for visual inputs, longer context, and improved security. Note that both models have limitations and may not be perfect for all applications.