Reasoning & chain of thought
What are reasoning models?
Reasoning models are trained to do the step-by-step thinking on their own, producing an internal chain of thought before the final answer instead of waiting for you to ask.
Chain of thought started as something you had to ask for. Newer models build it in. A reasoning model is trained to think through a problem first, on its own, before it writes the answer you see.
OpenAI describes its reasoning models as using “internal reasoning tokens before producing a response,” which the model spends to “plan, use tools effectively, inspect alternatives, recover from ambiguity, and solve harder multi-step tasks.”[1] Anthropic’s version is called extended thinking, which it describes as giving the model “enhanced reasoning capabilities for complex tasks, while providing varying levels of transparency into its step-by-step thought process before it delivers its final answer.”[2]
The practical difference is who does the prompting. With an ordinary model you add “think step by step” yourself. With a reasoning model the thinking happens automatically, and you often see it labeled separately from the final answer. That thinking is not free: it uses extra tokens and adds time, so these models cost more and respond more slowly than a plain question-and-answer model.
Reasoning models
- OpenAI reasoning models ↗
The o-series and GPT-5 reasoning models think internally before responding.
- Claude extended thinking ↗
Claude produces step-by-step thinking blocks before its final answer.
References
- Reasoning models — OpenAI
- Building with extended thinking — Anthropic