GPT (generative pre-trained transformer)
Definition
GPT (generative pre-trained transformer) is OpenAI's family of large language models, spanning from the original GPT-1 to current GPT models. Built on the transformer architecture and pre-trained on vast text and multimodal data, GPT models generate human-like text, power ChatGPT, and offer long context windows, multimodal input, and tool use through OpenAI's API.
How it works
GPT models are built on the transformer architecture and trained to predict the next token in a sequence. Through pre-training on enormous text and, in later versions, image and other multimodal data, they learn patterns of language, knowledge, and reasoning that let them generate coherent, context-aware output from a prompt.
After pre-training, GPT models are refined with techniques such as supervised fine-tuning and reinforcement learning from human feedback to make them more helpful, instruction-following, and safe. They are accessed primarily through OpenAI's API and through ChatGPT, and support capabilities including long context windows, multimodal input, structured outputs, and function calling that lets them use external tools.
Evolution of the GPT family
The GPT family has advanced rapidly. GPT-1 and GPT-2 demonstrated that scaling a pre-trained transformer improved generation quality. GPT-3 showed strong few-shot learning at large scale, and the instruction-tuned models that followed made GPT broadly useful, culminating in the launch of ChatGPT.
GPT-4 added stronger reasoning and multimodal understanding, and subsequent GPT releases have pushed further on context length, multimodality, tool use, and reasoning, alongside smaller, faster, and cheaper variants for high-volume tasks. Each generation has broadly traded off capability, speed, and cost, giving developers a range of models to match different workloads.
Why it matters
GPT is among the most influential model families in AI. ChatGPT, built on GPT, brought generative AI to a mass audience and reshaped expectations for what software can do, while the underlying models power countless applications through OpenAI's API.
For brands and publishers, GPT matters because it sits behind widely used assistants and answer experiences that increasingly mediate how people find information. Content that GPT-powered systems retrieve, understand, and cite gains visibility in those answers, making the GPT family a key surface for AI search and generative engine optimization.
Frequently asked questions
What does GPT stand for?
GPT stands for generative pre-trained transformer. "Generative" means it produces new content, "pre-trained" refers to large-scale training before any task-specific adaptation, and "transformer" is the neural network architecture the models are built on.
Who makes GPT models?
GPT models are developed by OpenAI. They are offered through the OpenAI API for developers and power the ChatGPT product, as well as integrations such as Microsoft Copilot. The GPT name refers specifically to OpenAI's family of generative pre-trained transformer models.
What is the difference between GPT and ChatGPT?
GPT is the underlying family of models. ChatGPT is the consumer-facing application built on top of GPT models, adding a conversational interface, safety tuning, and features like memory and tools. In short, GPT is the engine; ChatGPT is one product that runs on it.
What can GPT models do?
Modern GPT models generate and edit text, answer questions, write and debug code, analyze images and other inputs, and call external tools through function calling. They support long context windows and structured outputs, making them suitable for chat, content generation, agents, and a wide range of application workflows.
OpenAI
OpenAI is an AI research and deployment company best known for ChatGPT, the GPT family of large language models, the o-series reasoning models, and the DALL·E image models. It operates a widely used consumer assistant alongside an API and enterprise products, making it a dominant force in both consumer and business AI.
ChatGPT
ChatGPT is OpenAI's conversational AI assistant, powered by the GPT family of models. It answers questions, writes and edits content, reasons through problems, browses the web, and uses tools. As one of the most widely used mainstream AI assistants, it is a key surface for generative engine optimization (GEO).
Transformer architecture
The transformer is the neural-network architecture behind modern large language models. Introduced in 2017, it uses self-attention to weigh how strongly each token relates to every other token in the context, letting models capture long-range meaning and process sequences in parallel. This design made today's LLMs and multimodal models possible.
Large language model (LLM)
A large language model is an AI system trained on vast amounts of text to understand and generate human language. Built on transformer architecture and containing billions of parameters, LLMs predict the next token in a sequence, enabling them to answer questions, write, summarize, and reason. They power modern chat assistants, AI search, and autonomous agents.
Foundation models
Foundation models are large-scale AI models trained on broad, diverse data that serve as a general-purpose base adapted for many downstream applications. Rather than building a model per task, organizations fine-tune or prompt a single foundation model for translation, summarization, coding, search, and more. Large language models and multimodal models are common examples.
Multimodal AI
Multimodal AI refers to models that process and understand multiple types of input, such as text, images, audio, and video, within a single system. Instead of handling one modality at a time, a multimodal model can read a chart, describe a photo, transcribe speech, and reason across them together, enabling richer interactions and search experiences.