What is a defining trait of models specifically optimized for extended reasoning?

A reasoning model does something extra before it speaks: it generates internal reasoning tokens, which OpenAI describes as helping the model "plan, use tools effectively, inspect alternatives, recover from ambiguity, and solve harder multi-step tasks". You never see those tokens, but you pay for them and you wait for them.

That is the trade, and the documentation states it outright: "Lower effort favors speed and lower token usage, while at higher effort the model thinks more completely to provide higher quality responses". On current models this is a dial you set per request rather than a fixed property, so the same model can answer a trivial task cheaply and grind through a hard one.

The honest description is therefore not "smarter" but "spends more before answering". That is why the instant-response option cannot be right — extra thinking takes time by definition — and why being indistinguishable from a lightweight model cannot be either. Reach for these on debugging, multi-step planning and analysis. Do not put one behind a voice interface, where the pause is the whole problem.