Reasoning

Reasoning in AI refers to a model's ability to perform multi-step logical inference, draw conclusions from premises, and solve problems that require more than surface-level pattern recognition. While early language models primarily relied on statistical associations between words, modern frontier models demonstrate increasingly sophisticated reasoning capabilities across mathematics, science, coding, and common-sense tasks.

The push toward reasoning-capable models accelerated with the release of OpenAI's o1 and o3 series, which introduced "thinking" or "reasoning" tokens - internal chains of thought the model generates before producing its final answer. These models spend additional compute at inference time to work through problems step by step, trading speed for accuracy on complex tasks. Anthropic, Google, and other labs have followed with their own reasoning-enhanced models.

Reasoning capability is now one of the primary axes along which frontier models are evaluated. Benchmarks like GPQA Diamond test graduate-level scientific reasoning, AIME tests mathematical problem-solving, and coding benchmarks like SWE-Bench test the ability to reason about complex software systems. The distinction between models that can reason through novel problems versus those that recall memorized patterns is central to the debate about whether current AI systems exhibit genuine intelligence or sophisticated interpolation of training data.