Reasoning models are LLMs trained to think step-by-step before answering. Models like OpenAI's o1/o3 and Claude's extended thinking spend more compute on complex problems, improving accuracy on math, coding, and logic tasks.
Practical example
A model spends additional computation working through a multi-step task before returning an answer.
What to check
Measure complete-task accuracy, cost, latency, and failure cases with the exact model and settings.
Limits and tradeoffs
More computation or a longer answer does not guarantee correct reasoning or source support.
Maintained by DriftRail. AI assisted the October 2026 content and template revision. Examples are illustrative; they are not measured product benchmarks. Suggest a correction.