How we test AI tools.
Every guide and comparison here comes out of the same process: structured simulations run across a fixed set of metrics, scored on a public rubric, and re-run as the tools change.
We do not rank on impressions, and we do not run vendor demos. Each tool faces the same battery of simulations — repeatable tasks built to isolate one quality at a time — and we score the results against a rubric we keep public. We use the tools for weeks, not minutes, on the kind of work the reader actually does.
A single number can hide a lot, so we never publish one without showing the work behind it. Every guide lists its exact tests in a "How we tested" section, and every pick reports how it scored in each one. The metrics below are the spine of that process; the specific tasks vary by category, because testing a writing assistant is not the same as testing a coding agent.
What we measure
Each tool runs the same real tasks for the category — the same briefs, prompts, or codebases — and two reviewers score the results blind against a fixed rubric, so a name on the box never moves the number.
We repeat the hardest tasks many times and count how often a tool gets it right without hand-holding. A tool that nails a demo once but drifts on the tenth run is marked down for it.
On a fixed workload we measure time-to-first-output and time-to-finished-result, averaged over dozens of runs on the same machine and connection so a noisy network cannot flatter or punish a tool.
We price a month of real, observed usage for a typical user or team, then normalize to cost per useful result — so a cheap tool that needs five retries does not get to look like a bargain.
We time how long it takes to get from a clean start to a genuinely useful result, and note how well each tool fits the workflows people already have rather than demanding a new one.
Because these tools change weekly, we re-run the simulations on each meaningful update and date every verdict. A pick can lose its place when a rival ships, and we say so.
How we score
Results are scored 0 to 100 on a fixed rubric and shown small on each pick as NN / 100. Where the format allows it, scoring is blind: a reviewer rates the output without knowing which tool produced it. We weight the metrics toward what matters most for the category, then rank by the totals — and because every pick is scored in every metric, you can see exactly where a tool won and where it lost.
Nothing here is final. AI tools ship meaningful changes almost weekly, so every verdict is dated and re-run on each major release. A pick can lose its spot when a rival catches up, and when that happens we update the guide and say what changed.
Independence
We take no sponsorships and no payment for placement. A tool cannot buy its way onto a list, buy a higher rank, or buy a better score. Rankings reflect our testing and nothing else.
Priya leads testing on AI writing assistants and research tools. She designs the multi-week trials behind our guides, keeps the scoring rubric current, and re-tests our picks each time a major model ships.
Marcus tests coding assistants, agents, and the tooling around them. He runs every candidate against the same set of real repositories and tracks how often a tool helps versus how often it gets in the way.
Hannah covers the AI tools people reach for outside of work — image generators, note-takers, and assistants for everyday tasks. She tests for the people who do not read release notes and just want something that works.