That's not being debated here. The initial reported numbers were false and this was simply pointed out. You're changing the subject.
That's not being debated here. The initial reported numbers were false and this was simply pointed out. You're changing the subject.
Opus 5 medium has the same score as 3.8 flash on artificial analysis intelligence index.
Are you implying Google or Artificial Analysis are reporting false numbers? What's your source?
BTW you're comparing 3.8 flash high to opus 5 medium. 3.8 flash medium scores lower.
Flash models are on the order of 1/10th the size of Opus models, so some flex in the thinking level is fair.
When comparing closed models, the only thing that actually matters to anyone using them is some mix of cost and speed. Considering how much memory a server is using, when evaluating models that you'll never have access to in order to host yourself, doesn't really make sense.
Flash is just a name with no defined or consistent meaning even within labs, let alone between them. Considering both are closed weight, there is no way to truly assess how big the size delta between the two is. Then again, who cares about size, performance and end-to-end speed+cost are what matters along with task adherence, task assessment and so on.
Model size also can not be inferred by tokens/sec for a multitude of reasons, but to showcase two examples, Opus 5 and Sonnet 5, as well as Gemini 3.1 Pro Preview and 3.1 Flash have each very comparable output speeds when using the same deployment as a basis for comparison, despite it being very likely that within their generation, the former are larger than the latter. Feel the need to mention this, as I unfortunately stumble upon so many poorly reasoned, speculative hype post trying to infer model size via utterly unreliable metrics, not based in actual data.
It’s like comments below arguing about the reasoning levels not normalized to some metric (like cost, output token amount or duration) but just the labels or high, max, medium, etc. Those mean almost nothing even when comparing models based on the same pretrain (just compare GPT-5.4 to GPT-5.2), they mean less than nothing comparing different labs releases.
It's not totally a mystery
https://arxiv.org/html/2604.24827v1
The short of it is by using hard facts knowledge that is difficult to compress, and then quizzing models on these facts and calibrating against a bunch of open models, you can kind of feel out the size of closed models.
I really like that one, but it kinda highlights what I could have far better explained. Their 90% PI is three times in both directions. Between 3T and 24T for GPT-5.5.
That’s a massively wide, inaccurate and at best barely informative range, demonstrating that even the most well thought out method will yield little usable information.
Additionally, I got some private evaluation taking a similar approach towards gauging models in topics I’ve found either over or underfitted by labs. If we just used that to rank models (not get a potential size range but just a rough order) Thinking Machines Inkling would need to be lager than Fable 5.
> [...] shows an intelligence score of 59, the same as Opus 5 medium!
Nothing here is false, you are simply confused. You either didn't read what they wrote in its entirety or decided to reinterpret what they did write.
"Beating opus" is the false part, no?