An AI trained to work through a problem step by step before it answers.
A reasoning model is a language model trained to think before it speaks: it writes out a long series of working steps, often hidden, before giving its answer. It does especially well on multi-step problems in math, science and coding.
OpenAI's o1 (September 2024) made the idea widely known: on a US high-school math contest, OpenAI reported about 74% for o1 versus about 12% for a model that answered directly. In January 2025, DeepSeek-R1 shared a way to build this ability with reinforcement learning. The cost is slower answers and more tokens.
Don't confuse it with inference. Inference simply means running any trained model.
Think of it like this
One student writes the answer straight down; another works it out on scrap paper first. A reasoning model has the scrap-paper habit built in. It solves harder problems, but takes longer.
For example
Give it a tough math puzzle and you may see a "thinking…" note for a while before the answer appears.
Records about this
Source-checked moments from AI history.
Proved that reasoning ability is not a secret recipe of closed labs; self-checking, re-examination and strategy switching emerged. The same report also published the RLVR recipe and a recipe for transferring the capabili
The first commercial reasoning model; on AIME 2024, from 12% for GPT-4o to 74% for o1