I felt the same tbh; I notice more the regressions in the weeks before a new release than any potential improvement the new model might have actually brought.

It may also depend on the workload. At work everything is very domain specific with barely (if any) public training data; both need thorough review and careful hand holding, meanwhile at home Fable is scared of libtorch and falls back to Opus even if it's not touching the ML parts.