Ask any frontier model to implement binary search, and the answer will look impressive. Ask it to change a shared utility used by fourteen services without breaking callers, and the differences become much less polite.
That is the problem with most “GPT-6 vs. Claude 5.1 vs. Gemini” comparisons. They test the wrong surface area. A model can look brilliant on a self-contained algorithm prompt while still being expensive in a real repository: it may invent imports, ignore constraints, swallow errors, produce sprawling diffs, or write tests that only cover the happy path.
The useful question is not:
Which model writes the prettiest function?
The useful question is:
