When is it worth paying a model to think longer?

Some models now think before they answer. They write intermediate steps, sometimes thousands of tokens, before the reply you see. You pay for those tokens and wait for them. Whether that is a good trade depends on the task, and the only reliable way to find out is to measure.

Extra reasoning helps most when a task has dependent steps: calculations, constraint checks, planning, debugging. It helps little when the answer is a lookup. If an invoice number is in the email, thinking harder does not find it faster.

There is a second way to spend compute: ask several times and vote. Under independence, voting turns a 60 percent per-sample success rate into about 68 percent with five samples. The same arithmetic turns 40 percent into about 32 percent. Voting amplifies whatever the majority already does. Real samples from one model also share mistakes. If most of them misread the question the same way, the vote is confidently wrong.

Do not take the visible reasoning as an explanation either. Research has shown models giving step-by-step justifications for answers that were actually driven by a bias in the prompt, without ever mentioning the bias. A trace is evidence to check, not a record of what happened.

The practical approach is unglamorous. Collect a few dozen labelled cases from your real task. Run a fast model, the same model with voting, and a reasoning model on the same cases. Record accuracy, cost, and latency together, with dates and settings. Add a cheap, specific check where you can, such as a unit test, a schema, or a recomputed total, because exact checks catch errors that voting cannot.

Reasoning Models & Test-Time Compute works through this with an offline lab, so you can see voting fail on a correlated mistake before you spend money on a real model.