AI coding tools: how to compare them instead of chasing rankings
The market for AI programming tools changes so fast that any list of names goes stale within weeks. How to evaluate them lasts longer.
The main categories
- Editor assistants. They suggest code as you type and let you talk to the model about the open file.
- Editors built around AI. The model can see the whole project and make changes across many files at once.
- Terminal agents. They are given a task and read files, run commands and run tests on their own.
- Browser app builders. From a description they create a working prototype, usually with hosting included.
- Chat assistants. General models into which you paste code and questions. Flexible, but you have to carry the results over by hand.
Questions worth asking
Where does my code go? Check whether your data is used to train models and whether you can turn that off.
How much control do I have over changes? A good tool shows the differences before saving and lets you reject them.
What do the costs look like? A subscription, request limits and pay-per-use are three different worlds. With agent work, usage grows quickly.
Does it cope with my technology stack? Popular languages are supported better than niche ones. Test it on your own project, not on a demo.
What happens if the service disappears or changes its pricing? Avoid depending on a single provider wherever possible.
How to test
Pick one task you know inside out, for example a small function in an existing project, and carry it out in two or three tools. Compare not only the result but also the number of corrections you had to make. That tells you more than any ranking.
