Reasoning Model

A reasoning model is an LLM trained — usually with reinforcement learning on verifiable problems — to spend extra computation "thinking" before it answers. Instead of emitting the first response, it generates a chain of intermediate steps, checks its own work, and backtracks when a path fails. OpenAI's o-series, DeepSeek-R1, and Claude's extended-thinking modes are examples. Compared with standard chat models, reasoning models are markedly better at math, code, multi-step logic, and planning, but they cost more and respond slower because they emit far more tokens. For SaaS builders, the practical rule is to route only genuinely hard tasks — complex code generation, data analysis, agentic planning — to a reasoning model, and keep cheap, fast models for classification, extraction, and simple chat. Many APIs let you dial a "thinking budget" up or down per request, trading latency and cost for accuracy. Don't reach for a reasoning model reflexively: on simple extraction or formatting jobs it is just slower and pricier with no quality gain.