A reasoning model spends time thinking before it answers. It uses an internal chain of thought to try paths, check its own logic, and backtrack when a step fails. OpenAI's o1 and o3 series and DeepSeek-R1 are the usual examples.
A standard language model writes the next token as it goes. It is improvising. A reasoning model does a private scratchpad first. It may try more than one approach. It may catch an arithmetic error before it shows you an answer. That is why these models are stronger at math, coding, science questions, and other work that needs several linked steps.
The cost is speed. A reasoning model can take 30 seconds or more where a standard model answers in about two. Some products show the thinking trace, the scratchpad, so you can see how it got there. Some hide it.
These models still predict tokens. They are not a new kind of mind. They spend more compute at inference time on hard problems instead of answering in one pass. On benchmarks that need careful logic, they beat standard models. On easy chat, the extra wait is often wasted. OpenAI's o1 post (2024) describes a model that spends more compute on hidden chain-of-thought before answering.
That is the reasoning-model pattern.
Reasoning Model vs Standard LLM
Compare how different AI models approach problem-solving