Compare GPT-5.5 and Claude Opus 4.7 on three coding jobs

Separate benchmark claims from the practical tests

Luke’s April comparison tests GPT-5.5 and Claude Opus 4.7 on a real codebase. The description discusses benchmark claims and omissions, then moves to a landing-page redesign, bug hunting and a broken competitor-search algorithm. These are three different outputs to inspect, not one universal measure of model quality.

1. Save the same starting code and brief for both models. Define the required page behaviour, evidence for a valid bug and expected competitor-search result before running the tests.
2. Keep work on a reviewable branch or test copy and make the deployment boundary explicit. The video describes unwanted production pushes as part of Luke’s experience with Claude.

Review the design and reproduce the bugs

The redesign starts at 6:09 and bug hunting at 10:58, with a bug-hunt verdict at 14:21. The description flags false-positive findings, so a detailed explanation alone is insufficient evidence that a defect exists. The changed page also needs functional checks beyond its appearance.

1. Compare the pages at the same screen sizes and complete their main user action. Record which requirements each version meets and which changes would still be needed before release.
2. For each bug, inspect the relevant code and reproduce the claimed failure with test data. Separate confirmed defects from concerns that depend on an assumption or cannot be repeated.

Check the algorithm repair and the delivery boundary

The final task fixes competitor search from 16:25. The chapter at 20:30 returns to production-push behaviour before the final comparison at 23:02. Keep correctness and control of external actions separate when deciding which workflow suits your own work.

1. Run a known failing search case before and after the repair, plus another case that already worked. Inspect the returned matches and the code responsible for them.
2. Record revisions, remaining defects and any actions beyond the agreed scope. Base the verdict on those observed results rather than transferring a benchmark headline to every future task.

The takeaway

A useful coding comparison tests visual work, reproducible defects and algorithm behaviour separately, while also checking whether the agent respected the delivery boundary.