Why does a chatbot sometimes answer in a second, and other times sit there for ten before a single word appears? Often the difference is the model itself. AI reasoning models are built to work through a problem internally before they reply, and that extra effort changes what they are good at, what they cost, and even how you should write your prompts.
What Makes a Reasoning Model Different
OpenAI draws the line plainly in its reasoning best practices guide. Its o-series models are “the planners,” trained to think longer and harder about complex tasks, while its standard GPT models are “the workhorses,” designed for straightforward execution. The same guide adds that neither family is better than the other; they are just different tools.
In practice, a reasoning model spends part of its effort on internal “thinking” before it produces the answer you see. Think of a chess player who studies the board for a minute before moving, compared with one who plays the first sensible move that comes to mind. The second player is faster, and for most casual games that is perfectly fine. The first earns their keep when the position is tricky.
If tokens and training are new to you, our explainer on how large language models work covers the groundwork these models build on.
Where Extra Thinking Helps
OpenAI’s guidance sorts the choice into a few trade-offs. For speed and cost, GPT models are faster and tend to cost less. For accuracy and reliability, and for working through ambiguity and complexity, the o-series models are the better fit. OpenAI also notes that most AI workflows will use a combination of both, with reasoning models handling agentic planning and decision-making while GPT models carry out the individual tasks.
That split maps neatly onto everyday use. A multi-step coding problem, a tangled spreadsheet question, or a plan with several dependencies is the kind of job where a planner pays off. Asking for a friendlier tone on an email or a quick summary of a paragraph rarely needs it. For a closer look at the planner-and-worker pattern, see our guide to AI agents.
What the Extra Effort Costs
Thinking is not free. Anthropic’s extended thinking documentation says higher thinking budgets allow more comprehensive reasoning, with diminishing returns, at the cost of increased latency. It also explains that the internal reasoning is counted among the billed output tokens, which is why the same question can cost more on a reasoning model than on a standard one.
The way these controls work is changing quickly. According to that same documentation, the older approach of setting a fixed thinking budget is deprecated on Claude 4.6 models, and Claude 4.7 and later models reject it with an error. Anthropic points developers to adaptive thinking instead, where the model decides whether and how much to think on each request, and at lower effort levels it may skip thinking entirely on easy inputs. If you build on these tools, check the current documentation rather than copying an older code sample.
How to Prompt a Reasoning Model
Habits from earlier chatbots do not always carry over. OpenAI’s guide recommends keeping prompts simple and direct, being very specific about the end goal, and using delimiters to separate instructions from data. It also advises against chain-of-thought prompts: because these models reason internally, telling them to “think step by step” or “explain your reasoning” is unnecessary and may not help.
Most of the advice in our prompt engineering tips still applies. State the goal, supply the context, and describe the format you want. The one habit to drop is coaxing the model through each step yourself.
Thinking Longer Is Not the Same as Being Right
A reasoning model can still reach a wrong answer, and it can still present that answer with confidence. Extra deliberation tends to help on problems with checkable steps, but it does not remove the need to verify facts, figures, and citations that matter. Our piece on AI hallucinations explains why fluent output and correct output are different things.
FAQs
What is a reasoning model in simple terms?
A reasoning model is an AI system trained to work through a problem internally before it writes its answer, much like drafting in your head before speaking. That extra step is what separates it from a standard chatbot model built mainly for quick, direct responses.
Are reasoning models always better than regular chatbots?
No. OpenAI says its reasoning and standard GPT families are simply different, with GPT models faster and usually cheaper and reasoning models stronger on complex, ambiguous problems. For simple writing or lookup tasks, a standard model is often the better choice.
Do I need to tell a reasoning model to think step by step?
Generally not. OpenAI’s guidance says that because these models reason internally, step-by-step instructions are unnecessary and may not improve results. A clear, specific description of what you want usually works better.
Why are reasoning models slower and sometimes more expensive?
The model spends additional effort on internal reasoning before it answers, and that effort adds delay. Anthropic’s documentation notes that this reasoning is counted among billed output tokens, so heavier thinking can raise the cost of a single request.
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