Comment by Corence
4 hours ago
It is a fair question. I'd expect the numbers are all real. Competitors are going to rerun the benchmark with these models to see how the model is responding and succeeding on the tasks and use that information to figure out how to improve their own models. If the benchmark numbers aren't real their competitors will call out that it's not reproducible.
However it's possible that consumers without a sufficiently tiered plan aren't getting optimal performance, or that the benchmark is overfit and the results won't generalize well to the real tasks you're trying to do.
> I'd expect the numbers are all real.
I think a lot of people are concerned due to 1) significant variance in performance being reported by a large number of users, and 2) We have specific examples of OpenAI and other labs benchmaxxing in the recent past (https://grok.com/share/c2hhcmQtMw_66c34055-740f-43a3-a63c-4b...).
It's tricky because there are so many subtle ways in which "the numbers are all real" could be technically true in some sense, yet still not reflect what a customer will experience (eg harnesses, etc). And any of those ways can benefit the cost structures of companies currently subsidizing models well below their actual costs with limited investor capital. All with billions of dollars in potential personal wealth at stake for company employees and dozens of hidden cost/performance levers at their disposal.
And it doesn't even require overt deception on anyone's part. For example, the teams doing benchmark testing of unreleased new models aren't the same people as the ops teams managing global deployment/load balancing at scale day-to-day. If there aren't significant ongoing resources devoted to specifically validating those two things remain in sync - they'll almost certainly drift apart. And it won't be anyone's job to even know it's happening until a meaningful number of important customers complain or sales start to fall. Of course, if an unplanned deviation causes costs to rise over budget, it's a high-priority bug to be addressed. But if the deviation goes the other way and costs are little lower than expected, no one's getting a late night incident alert. This isn't even a dig at OpenAI in particular, it's just the default state of how large orgs work.