Reasoning Model

Reasoning models are language models specifically trained to generate long internal reasoning traces — a 'chain of thought' that may span thousands of tokens — before producing a final answer. OpenAI's o1 and o3 models exemplify this paradigm: they use reinforcement learning to develop reasoning strategies, 'thinking' through problems rather than immediately generating responses. Reasoning models dramatically outperform standard chat models on mathematical olympiad problems, competitive programming, and multi-step logic puzzles. The trade-off is latency and cost: reasoning models take longer and consume more tokens per query. They represent a shift from scale (more parameters) to compute at inference time.

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