Reasoning model
Also called thinking model, extended thinking, reasoning models
An AI model trained to think through a problem at length before answering, which helps on hard maths, code and analysis.
A reasoning model is a language model trained to produce a long internal chain of thought before its final answer. Instead of replying immediately, it works through the problem: breaking it down, trying approaches, checking its work and correcting mistakes. Many providers offer such models or modes, sometimes called extended thinking, often with a setting that controls how much thinking effort to spend.
These models are trained with reinforcement learning on problems with checkable answers, such as maths, coding and logic puzzles, which rewards reasoning that leads to correct results. The result is large gains on complex tasks: multi-step maths, tricky debugging, planning, and analysis where several factors interact.
Reasoning costs time and tokens. A simple factual question gains little from a minute of thinking. Prompting also shifts: reasoning models need less step-by-step instruction because they already reason, but they still benefit from clear goals, full context and explicit success criteria. Some providers show a summary of the reasoning; others hide it.
Example
Asked to find why a financial model's totals do not match, a standard model guesses a likely cause. A reasoning model traces the formulas, notices that one sheet uses gross figures and another net, and checks that the difference equals the tax amount before answering.
Common mistake
Using a reasoning model for everything. For quick rewrites, simple lookups and casual chat, it is slower and more expensive with no better result. Match the model to the task: fast models for routine work, reasoning models for problems where careful thinking pays off.