Q: Chat models vs reasoning models — when do I use which?

Some models answer immediately. Some think first, then answer. The interesting part is that this is no longer only a choice between models: increasingly, it's a setting on the same model.

Here's the current landscape. OpenAI's GPT-5 family (GPT-5.5, GPT-5.4, and their cheaper mini and nano versions) are general-purpose models with a reasoning effort setting: at low effort they respond fast and cheap, at high effort they work through the problem internally before answering. OpenAI also still ships dedicated reasoning models (the o-series: o3, o3-pro, o4-mini) built specifically for hard problems. With Claude, the model tiers (Opus, Sonnet, Haiku) are about size and capability, and thinking is a per-request toggle: by default Claude answers immediately, and you explicitly enable thinking when the task needs it.

So the real question isn't "which model type" but "how much thinking does this task need." The answer for most everyday work (conversation, summarizing, drafting, straightforward code) is: none. Fast mode is cheaper, quicker, and plenty. For genuinely hard problems (multi-step math, tricky debugging, planning, analysis with many moving parts), turning on reasoning buys a real quality jump, at the cost of latency and more tokens.

The rule of thumb: default to fast, escalate to thinking only when the task defeats fast mode. Running everything at high reasoning is one of the most common cost mistakes in early AI apps. And one budget note: reasoning tokens are billed and count toward your limits, even though the user never sees them.