That sounds very plausible. But it implies they could offer even higher performance models at much higher costs if they chose to; and presumably they would if there were customers willing to pay. Is that the case? Surely there are a decent number of customers who’d be willing to pay more, much more, to get the very best LLMs possible.

Like, Apple computers are already quite pricey -- $1000 or $2000 or so for a decent one. But you can spec up one that’s a bit better (not really that much better) and they’ll charge you $10K, $20K, $30K. Some customers want that and many are willing to pay for it.

Is there an equivalent ultra-high-end LLM you can have if you’re willing to pay? Or does it not exist because it would cost too much to train?

▲

criemen 3 hours ago | parent [-]

> Is there an equivalent ultra-high-end LLM you can have if you’re willing to pay? Or does it not exist because it would cost too much to train?

I guess at the time that was GPT-4.5. I don't think people used it a lot because it was crazy expensive, and not that much better than the rest of the crop.

	▲	foobar10000 35 minutes ago \| parent [-]
		The issue is not better - it’s better _AND_ fast enough. An agentic loop is essentially [think,verify] in a loop - i.e. [t1,v1,t2,v2,t3,v3,…] A model that does [t1,t2,t3,t4] in 40 minutes, if verify takes 10 min, will most likely do MUCH worse that a model that does t1 (decently worse) in 10 mins, v1 in 10 mins, t2 now based on t1 and v1 in 10 mins, v2 in 10 mins, etc.. So, for agentic workflows - ones where the model gets feedback from tools, etc…, fast enough is important.