So where are these mythical savings coming from? You're saying they have spent more per user therefore can charge each user less or something? I'm not following.

Definitely. They had insanely low rates on TTS up until a month or two ago ($4.20/1M) for example, which they only recently started increasing.

As their models get more competitive I'm sure prices will catch up.

I think we need to be explicit about the domains we're applying Composer 2.5 to in these discussions.

I mentioned here (https://news.ycombinator.com/item?id=48766275) how poorly it handles my specific use cases. My coworkers in DevOps and frontend UI swear by its cost-effectiveness, whereas I strongly prefer the reasoning capabilities of Opus 4.8 and Fable 5.

Composer 2.5 seems to be SOTA for Helm charts and React/Vue, but, for my usecases it absolutely struggles spectacularly when tasked with rigid body dynamics or kinematic logic.

Not my experience at all. I've been using Cursor hardcore for about two weeks now and Composer 2.5 and it's wonderful. Now with Grok 4.5 I'm quite excited about the possibilities.

In what way? I spend more of my time managing than hands on lately so I legitimately don’t know.

Not true. The only issue is cost of frontier models.

> I built a really feature rich application, insanely complicated, close to 200k LOC

If you listed it, how many features/LOC or vice-versa? Really hard to know if 200K LOC is good or bad, at the surface it sounds like too much, but I don't know what the application was either.

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.