What Is a Reasoning Model? AI Built to Think Before It Answers
A newer class of models specifically trained to work through a problem step by step before producing a final answer, rather than answering immediately.
Thinking before answering
A reasoning model is specifically trained to work through a problem step by step internally before producing a final answer, rather than generating a response immediately — spending extra computation during generation on working through the problem, not just on producing the final answer text.
Why this helps on genuinely hard problems
Complex math, multi-step logic, and intricate coding problems often can't be solved correctly in a single, immediate pass — working through intermediate steps, checking them, and building on them tends to produce meaningfully more accurate results than jumping straight to a final answer.
The real cost: more time and compute per answer
This extra step-by-step reasoning process uses meaningfully more inference computation and takes longer per response than a standard model producing an immediate answer — captured by the idea of test-time compute, a genuine tradeoff between accuracy and both speed and cost.
Frequently Asked Questions
What is a reasoning model?
A model specifically trained to work through a problem step by step internally before producing a final answer, rather than responding immediately — aimed at improving accuracy on complex, multi-step tasks.
Why don't all models use this reasoning approach by default?
Extended step-by-step reasoning takes meaningfully more computation and time per response, which isn't worth the cost for simple queries — it's a deliberate tradeoff reserved for tasks that actually benefit from it.