IMHO it's roughly task depth (fable) vs breadth (opus). Fable is great at tracing and debugging sometimes, but otherwise shorthand for confabulation. It's persistent but ungovernable, struggles to switch contexts, and goes insane with too much freedom to explore. Don't point it at anything that looks like a "system" for actual work (but mapping or planning might be ok). Opus is maybe not as creative, but it's more stable and more trustworthy. Opus driving Fable could be awesome, but Fable unleashed/unsupervised on longer horizon tasks or things that require more methodical changes on lots of components seems like a disaster every time I try it.

Since this kind of thing is always down to harness, project-type, and other structural constraints, of course your mileage may vary. Fable is probably great for pen-testing, or as a decision-making kernel of other kinds of applications, and way better than Opus at those things. Probably fine for code-review or changing a codebase of a few thousand lines in any language. Actually building that codebase or changing an even bigger one? Woof.

How this fits in with science? IDK but I bet other existing causal reasoning benchmarks might tell the whole story and this is back to stability again. Sometimes having a smart idea is really important! But more often it's important to just not forget what you were doing. What was I talking about? Oh look a squirrel

This is measuring on scientific tasks though. I haven't used Fable lately but when I was playing with it when it was new its "safety" features made it practically impossible to use for biomedical science -- it once refused to work on a pipeline of mine that was analyzing pathogenicity islands in bacteria (presumably because it had guardrails to that effect to stop potential bioterrorists and the like)

undefined

[dead]

How is an llm parser_spec to parser better than something like lex?