Our experience & testing methodology

How we test AI tools

We do not review tools from their marketing pages. We use them — on the same kinds of tasks you would, run consistently across every tool in a category, so the comparison is fair. A demo tells you what a tool can do on a good day; repeatable tasks tell you what it does on an ordinary one. The second answer is the one that matters, and it is the one we publish.

Each tool in a review is scored against five criteria:

• Output quality — is the result actually good, and how much editing does it take to make it usable?
• Speed — how long does it take to get a usable result, including retries and rework?
• Pricing — what does real-world use cost, and is the value fair for the output?
• Privacy — how is your data handled, stored, and used for training, based on the tool's own terms?
• Reliability — how consistent is it across repeated runs, and how often does it fail or need supervision?

We weight quality and reliability most heavily. A tool that is fast and cheap but produces work you cannot trust does not earn a top spot, no matter how impressive its highlights look.

Our testing process, step by step

1. Define the tasks. For each category we build a fixed set of realistic tasks — the same brief, the same prompt, the same starting point — so every tool is measured on identical work.
2. Record the version. Before testing, we note the exact tool version, model, plan, and the date, because results only mean something when tied to a specific point in time.
3. Run the tasks repeatedly. We run each task more than once to see how consistent a tool is, not just how it performs on a single lucky attempt.
4. Score against the criteria. We rate output quality, speed, pricing, privacy, and reliability, capturing concrete examples of where a tool succeeds and where it struggles.
5. Compare side by side. Scores are set relative to the other tools in the same category, so a ranking reflects real competition rather than an isolated impression.
6. Write it up honestly. We publish the tasks, the criteria, and the version tested, alongside the strengths and the trade-offs — including the reasons a highly ranked tool might still be wrong for you.
7. Re-test as tools change. When a meaningful update ships, we revisit the review, update the scores, and stamp the article with a new last-updated date.

Categories we've worked across

Our testing spans the areas where AI is genuinely reshaping day-to-day work:

• Writing — drafting, editing, and long-form assistants.
• Coding — copilots and coding agents on real bug fixes and features.
• Research & agents — deep-research tools and autonomous agents judged on sourcing and accuracy.
• Productivity — note-takers, schedulers, and automation.
• Image & media — image, audio, and video generation.
• Foundation models — the large models that power the tools above.

Frequently asked questions