How to choose an AI code editor

AI code editors all promise speed. Very few publish the measurements behind the promise. This guide lays out a verification-first way to compare them — the same way our rankings do.

Start with the boring facts: which models are actually used, whether your code leaves your machine, what the pricing model is, and what happens to your data during training. A vendor that answers these plainly deserves more of your attention than one that leads with benchmark bars.

Second, separate editor quality from AI quality. Some of the best editors have the weakest assistants and vice versa. Our comparison tables keep these dimensions apart on purpose — collapsing them into one score hides exactly the trade-off you are trying to evaluate.

Third, distrust anything labeled simply "faster". Faster than what, measured how, on whose workload? Ask for the methodology. If there is none, treat the claim as marketing, not evidence.

Finally, accept unknowns. A tool with honest gaps in its profile is usually better documented than one whose profile is suspiciously complete. Unknown is a fact; hidden gaps are a decision.