Reasoning Models

Reasoning models represent a significant evolution in AI capabilities, specifically designed to handle tasks that require careful, step-by-step logical thinking. Unlike standard chat models that respond quickly with a best guess, reasoning models allocate additional computation time to "think through" a problem before responding. OpenAI's o1 and o3 models pioneered this approach, and the concept has spread across the industry.

The key innovation behind reasoning models is test-time compute scaling — the idea that spending more computation during inference (not just training) can dramatically improve the quality of responses on hard problems. Reasoning models use extended internal chains of thought, exploring multiple solution paths, checking their own work, and backtracking when they detect errors. This process is often hidden from the user, who only sees the final, polished answer, but the intermediate reasoning can be many times longer than the visible output.

Reasoning models particularly excel at mathematics, formal logic, scientific analysis, complex coding problems, and any task that benefits from systematic, step-by-step thinking. On benchmarks like MATH, GPQA (graduate-level science), and competitive programming tests, reasoning models dramatically outperform standard models of similar size. OpenAI's o3 achieved scores on some benchmarks that were previously considered out of reach for AI systems.

The tradeoff with reasoning models is cost and latency. Because they generate extensive internal reasoning chains, they consume significantly more tokens (and therefore cost more) and take longer to respond. A simple question that a standard model answers in one second might take a reasoning model ten seconds as it works through its thinking process. For quick, straightforward tasks, reasoning models are overkill. They shine when accuracy on complex problems matters more than speed — in technical analysis, research, code debugging, and any scenario where getting the right answer is worth waiting for.