I'm not sure if you are aware, but you have to approach prompting Fable slightly differently from a model like Opus.

It's important to include the reason aka the why of your task [1] in your prompt. You'll get more mileage if you verbalize your thought process when prompting Fable. Anthropic say you should think of Fable as a "thought partner".

1: https://platform.claude.com/docs/en/build-with-claude/prompt...

2: You might find some of the example prompts listed here useful https://x.com/trq212/status/2073100352921215386

Fable needs more... ambitious tasks than Opus to tell the difference and let me tell you the difference is there.

Simple tasks are simply saturated just like simple benchmarks. There's a level of intelligence where you simply don't need more for some things.

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.

Did you explicitly tell it to use Sonnet or Opus subagents and stick at or below high effort? Asking because such practices make a huge difference in the quality of output and the amount of tokens burned. I used one of my accounts to explore ultramax and it was just a token hog that might be worse than Opus.