Try doing real work with them, it's night and day difference especially for systems programming. The non-frontier models to a lot of benchmaxxing to look good.

> Benchmarks suggests they are comparable

The problem here is people think AI benchmarks are analogous to say, CPU performance benchmarks. They're not:

* You can't control all the variables, only one (the prompt).

* The outputs, BY DESIGN, can fluctuate wildly for no apparent reason (i.e., first run, utter failure, second run, success).

* The biggest point, once a benchmark is known, future iterations of the model will be trained on it.

Trying to objectively measure model performance is a fool's errand.