> Claude Opus 5 is not more capable overall than our most capable general-access model, Claude Fable 5
Ok then so what's the point?
> Claude Opus 5 is not more capable overall than our most capable general-access model, Claude Fable 5
Ok then so what's the point?
Fable is twice the price.
If Fable gets correct answer quicker, then you might pay less than doing back and forth with Opus, plus you lose more of your own time.
I see no reason for using less able models in my workflows. There is this saying, penny wise and pound foolish
same as it ever was. It seems your argument implies a belief that you should always use the best model. Others think that not all tasks require the absolute most powerful, expensive, model.
The CursorBench plot, for example, shows that fable does have slightly better performance, but Opus is pretty close, and is less expensive per task
fable on longer coding tasks with fable subagents will easily chew through hundreds of dollars in a single run.
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less expensive per task might also mean less of your own time
Fable 5 is NOT included in Claude Pro subscription
Aren’t they planning to remove it even from max and keep it only credit based? OpenAI will be happy if that would happen
Really? It's better than Opus 4.8, that's the point.
When they release new versions of Sonnet, no-one expects them to be better than Opus.
it's nice to know how to work the thing that fable fails down to when it dislikes your prompt.
This is useful to me since I delegate most coding tasks to Opus and use Fable for planning.
The cost?
Same as 4.8
To have an answer to "Sol" GPT 5.6 which is far more cost effective and available than Fable.
Pricing, presumably
This is confusing to me because in their blogpost they show model benchmarks and it spanks Fable pretty soundly in most tests.
Cost.
Presumably, it’s cheaper.
You are being downvoted for a fair question and others are extremely wrong and confident.
The point is that Opus 5 is the best they can do without needing classifiers and absurdly broad safeguards.
The illusion of progress and advancement, to appease shareholders, and slightly postpone the looming bubble pop.
Why are we still talking like ai is majorly used for increasing shareholder value only? Its coding performance is top notch and quality is increasing at a rapid pace. It wasn't even half this good a year back. It even is useful for a subset of math problems.
People don't seem to be able to reconcile the fact that there is likely an overbuild and overspend on AI that may be inflating a bubble, and that AI is actually incredibly useful and getting really really good for certain tasks. Both camps are right, except for when they say the other is wrong.
By what measure is there an overbuild? Every metric I look at, shows inference unable to satisfy current demands.