Reasoning Models Explained: o-series, R1, and When to Use Them
What reasoning models actually do, how they differ from standard models, and when the extra "thinking" is worth it.
By AiClubb Team
Reasoning models — like OpenAI's o-series and DeepSeek's R1 — spend extra compute thinking before they answer. The result is dramatically better performance on math, logic, and planning.
Under the hood, they use chain-of-thought internally: the model generates intermediate reasoning steps, evaluates them, and refines before producing the final answer.
The trade-off is speed and cost. Reasoning models are slower and pricier per request. For simple tasks — a short email, a quick summary — a standard model is the right tool.
A useful heuristic: if the task has a single obvious path, use a standard model. If it requires multi-step deduction, constraint satisfaction, or careful planning, switch to a reasoning model.
You can also get reasoning-style benefits from standard models by prompting them with explicit chain-of-thought. The effect is smaller but free.
The trend is clear: reasoning will become the default. For now, knowing when to spend the extra tokens is a real skill.