AI Reasoning Model
A class of language models trained to 'think' before answering — generating extended internal reasoning steps to solve complex problems more reliably.
What Is AI Reasoning Model?
An AI reasoning model is a language model optimized to perform deliberate, multi-step reasoning before producing a final answer. Rather than responding immediately, these models generate an extended internal chain of thought — working through a problem step by step, exploring approaches, checking intermediate results — and only then output a conclusion. This 'thinking' phase, sometimes called inference-time or test-time compute, substantially improves performance on hard tasks in math, coding, logic, and planning.
Reasoning models such as OpenAI's o1 and o3 series, DeepSeek-R1, and the reasoning modes of Claude and Gemini are typically trained with reinforcement learning that rewards correct final answers, encouraging the model to discover effective reasoning strategies. The key insight is that spending more compute at inference time — letting the model think longer — can rival or exceed the gains from simply making the model larger, opening a new axis for scaling AI capability.
Why It Matters
Reasoning models represent a major shift in how AI capability is achieved. For years, progress came mainly from scaling model size and training data; reasoning models add a second lever — scaling the compute spent thinking at inference time. This makes them dramatically better at complex, verifiable problems, but also slower and more expensive per query. Knowing when a task warrants a reasoning model versus a fast standard model is now a key practical decision.