What Is GPT and How Does It Work

How GPT Models Work

GPT models are built on the transformer architecture, a neural network design that processes text by paying attention to relationships between words across an entire passage rather than reading one word at a time. During training, the model reads billions of pages of text and learns statistical patterns about how language works, what facts exist, and how to reason through problems.

When you send a message to GPT, the model does not search the internet or look up answers in a database. Instead, it generates a response word by word, choosing each word based on the patterns it learned during training combined with the specific context of your conversation. This is why GPT can write fluently about almost any topic but occasionally produces information that sounds correct but is not, a problem known as hallucination.

GPT Model Versions on the Platform

The platform provides access to several GPT models, each with different capabilities and costs:

GPT-4.1-mini

The default chat model for most tasks. GPT-4.1-mini offers a strong balance of quality, speed, and cost. It handles customer support conversations, content writing, data extraction, and general question answering at 2 to 4 credits per response. For most business chatbots and workflows, this is the right starting choice.

GPT-4.1

The full-size GPT-4.1 model delivers higher quality output on complex tasks, especially long-form writing, detailed analysis, and nuanced instructions. It costs more per token than the mini version but produces noticeably better results on demanding work.

GPT-4.1-nano

The cheapest GPT model available, designed for simple, high-volume tasks. It works well for classification, yes/no decisions, short data formatting, and routing logic. At a fraction of the cost of larger models, it is ideal for workflow steps that do not require deep reasoning.

GPT o3-mini

A reasoning model that spends extra computation time thinking through problems before answering. It excels at math, logic puzzles, multi-step analysis, and tasks where getting the right answer matters more than responding quickly. It costs significantly more per request but achieves higher accuracy on difficult problems.

What GPT Is Good At

• Conversational AI: Natural, flowing conversations that feel human. GPT models handle context well across long conversations.
• Content generation: Writing articles, emails, product descriptions, and marketing copy with consistent quality.
• Code generation: Writing, explaining, and debugging code in dozens of programming languages.
• Data extraction: Pulling structured data from unstructured text, like extracting names and dates from emails.
• Translation and summarization: Converting text between languages or condensing long documents into key points.

What GPT Is Not Good At

• Real-time information: GPT models have a training cutoff date and do not know about events after that date unless given current information through RAG or tool use.
• Math-heavy tasks on cheap models: The smaller GPT models sometimes make arithmetic errors. Use a reasoning model for calculations that need to be exact.
• Guaranteed accuracy: GPT can confidently state incorrect information. Always pair it with your own knowledge base using RAG for business-critical answers.

GPT vs Other Models

GPT is one of two major model families available on the platform, the other being Claude by Anthropic. Both produce high-quality results, but they have different strengths. GPT tends to be faster and offers more model tiers at different price points. Claude tends to follow complex instructions more precisely and produces more natural-sounding prose. See the full GPT vs Claude comparison for details on which is better for specific tasks.