Reasoning models are language models post-trained to deliberate internally before answering: generating extended chains of thought, exploring alternatives, and
Language models post-trained to deliberate internally before answering, generating extended chains of thought, exploring alternatives, and self-checking, with the amount of 'thinking' often controllable per request. They dominate hard benchmarks in math, coding, and analysis.
For genuinely hard problems, since they turn inference compute into a quality dial. Builders route easy queries to fast models and hard problems to reasoning models with larger thinking budgets, pricing accuracy explicitly.