Reasoning vs Standard Models

A standard model answers immediately: your prompt goes in, the response streams out. A reasoning model (also called a thinking model) first spends tokens working through the problem — planning, checking intermediate steps, backtracking — before committing to an answer. This “thinking” is essentially chain-of-thought reasoning trained into the model and scaled up at inference time. OpenAI’s o-series and GPT-5’s thinking modes, Claude’s extended thinking, Gemini’s thinking variants, and DeepSeek-R1 all follow this pattern; by 2026 most frontier models are hybrids where you dial the reasoning effort up or down per request.

The tradeoff is the point: reasoning dramatically improves results on math, complex code, multi-step planning, and agentic tasks, but you pay for it in latency and thinking-token costs. Using a reasoning model to reformat JSON burns money and time for nothing; using a standard fast model to architect a migration plan gets you shallow answers. Model routing — matching each task to the cheapest model that does it well — is one of the highest-leverage cost decisions you’ll make.

In practice this is an API parameter as much as a model choice: you’ll set a reasoning-effort or thinking-budget option (for example, Anthropic’s thinking parameter with a token budget), decide whether to surface or hide the thinking trace in your UI, and account for thinking tokens in billing. A useful default: fast model first, escalate to reasoning when evals show the task demands it.