What is an AI 'reasoning' model, and how do you actually notice the difference?

A couple of months back I asked a “regular” model to check the logic on a discount system where the rules kept stepping on each other (coupon, seasonal offer, and an already-reduced price, all three at once), and it handed back an answer that was confident, well-written, and wrong. I switched to the same provider’s reasoning mode, the exact same question, and it took fifteen seconds instead of two. This time it caught the odd edge case where two conditions cancelled each other out.

A reasoning model is an AI that, before writing the answer you actually see, works through an internal chain of intermediate steps, trying out paths and dropping the ones that don’t add up, instead of answering on the first try the way a regular chatbot does. That costs more time and more tokens, but it noticeably improves accuracy on tasks with several chained logical steps: code with crossed dependencies, contracts with clauses that contradict each other, that sort of thing.

And that’s exactly the catch: thinking harder isn’t free. With most providers you’re also paying for the internal reasoning tokens, the ones you never see on screen, so the same question can end up costing quite a bit more than with a regular model. For a customer-service question like “do you ship to Portugal?” that’s money down the drain: the standard model answers just as well in a fraction of the time. Which is exactly where understanding how tokens get counted actually helps you decide which model to run where, not just when to switch on reasoning.

We use it in a fairly surgical way.

For marketing copy, summarizing an email, or tagging a support ticket, a mid-tier standard model does the job, no question. For reviewing an architecture change before touching anything, chasing a bug that’s been fighting us for two days, or reading through a contract before a client sees it, we flip to reasoning mode without a second thought. The difference shows up most when the problem has a hidden trap: the regular model steps on it with total confidence, the reasoning one sometimes sees it coming and says so out loud before answering, something like “wait, this has a weird edge case.”

What I still haven’t figured out is when to hand that choice, fast model or model that thinks, over to the system itself instead of deciding it by hand every time. Some providers already offer an automatic router that picks for you based on the question, but the few times we’ve tried it in production it got it right less often than we’d have liked. So for now, we’re still the ones flipping the switch.