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.