A reasoning model is a class of AI model designed to think before responding, using an internal chain-of-thought process to work through complex problems. OpenAI's o1 and o3 series, DeepSeek-R1, and similar models are the primary examples. Standard LLMs generate responses token by token, essentially improvising. Reasoning models spend time on internal deliberation, exploring multiple approaches, checking their own logic, and backtracking when they hit errors, before producing a final answer. This makes them much better at mathematics, coding, scientific reasoning, and tasks requiring multi-step logic. The trade-off is speed. A reasoning model may take 30 seconds or more to respond where a standard model responds in two seconds. The internal thinking is sometimes visible as a thinking trace, showing the model's scratchpad reasoning. Reasoning models represent a shift in how AI systems handle hard problems, from pattern-matching and next-token prediction toward something closer to deliberate problem-solving. They consistently outperform standard models on benchmarks requiring rigorous logical reasoning.
Back to Glossary