GPT

ELI5: Explain it like I’m 5

Imagine you’re playing a game where you guess the next word in a sentence. If someone says, “The cat sat on the…”, you’d probably say “mat.”

GPT works the same way, but at a much larger scale. It has read an enormous number of sentences and learned patterns about which words usually come next.

It doesn’t know things like humans do. Furthermore, it doesn’t really think or understand meaning. It simply predicts the most likely next word based on what it has seen before, over and over again, very quickly.

What are GPT Models?

Generative Pre-trained Transformers (GPT) are a class of large language models (LLMs) developed by OpenAI. They are built upon the transformer deep learning architecture introduced by Google.

These models are foundational to many generative AI applications, including chatbots like ChatGPT.

The Generative aspect refers to their capacity to create new content.
Pre-trained highlights their extensive initial training on vast datasets.
Transformer refers to the neural network architecture that allows them to process sequences of data efficiently, recognizing long-range dependencies in text.

OpenAI’s GPT versions

Year	Model / Event	Features
2018	GPT-1	First Generative Pre-trained Transformer introduced by OpenAI (research prototype).
2019	GPT-2	Much larger model; demonstrated strong text generation and raised safety discussions.
2020	GPT-3	First widely used GPT model; OpenAI API launched, enabling developers to build products.
2022 (Nov)	GPT-3.5	Improved instruction following; became the base model for early ChatGPT.
2022 (Nov)	ChatGPT public launch	Conversational interface released; GPT reached mainstream users.
2023 (March)	GPT-4	Major capability leap; improved reasoning and multimodal inputs; Gradual API rollout.
2024	GPT-4 refinements like mini, nano, 4o	Ongoing improvements focused on reliability, cost, and deployment (multiple internal variants).
2026	Latest GPT-5-class models	New generation focused on stronger reasoning, efficiency, and broader real-world usage.

Multimodal evolution in GPT models

Early GPT models were text-only. Their training, inputs, and outputs were all limited to language tokens, which defined GPT’s capabilities for several years.

Multimodality, handling more than one type of input, was introduced gradually.

Text-only era

GPT-1, GPT-2, GPT-3, and GPT-3.5: These models operated entirely on text (including code as structured text). They could not process images, audio, or visual inputs.

Introduction of vision capabilities

GPT-4 (2023): Marked the first major step toward multimodality. GPT-4 introduced the ability to accept images as input (vision), allowing the model to describe, analyze, and reason about visual content. Output, however, remained text-based.

Expansion toward broader multimodality

Later GPT-4-class and GPT-5-class models: These models continued to improve multimodal features, supporting richer combinations of text, images, and integrated tool-based interactions.

Strengths of GPT

Writing, summarizing, and rewriting text
Explaining concepts at different levels
Reasoning through structured problems (with limits)
Acting as a general-purpose language interface

Weaknesses of GPT

Knowing real-time information
Guaranteeing factual accuracy
Making decisions or judgments on its own

Impact of GPT

GPT not only advanced language model research but also played a pivotal role in bringing AI into everyday use through an accessible, conversational interface - ChatGPT.

At-a-Glance