Benchmarks / AI coding
Beyond the score: reading AI coding benchmarks
What makes a coding benchmark useful? A closer look at models, test environments, and the numbers behind a result.
MiloPublished on Start with the setup
A benchmark score is easier to understand when you know how it was produced. Which model did the agent use? What instructions did it receive? How much time and context did it have? These details belong alongside the headline number.
Compare the same work
For an agent comparison, using the same model and task set helps isolate the effect of the agent itself. Different models, retry budgets, or test environments can change the result. Keep those differences visible rather than treating every score as interchangeable.
Look past a single number
Solved tasks matter, but so do cost, execution time, and how results vary across runs. A benchmark offers evidence about a particular setup. Try the agent on your own repository as well: the useful question is how it performs on the work you actually need done.
| Detail | What to check |
|---|---|
| Model | Same model and version across agents |
| Tasks | Same task set and pass criteria |
| Budget | Time, tokens, and retries allowed |
| Cost | Total spend per completed task |