No company at the moment has enough money operate with 10x the reasoning tokens of their competitors because they're bottlenecked by GPU capacity (or other physical constraints). Maybe in lab experiments but not for generally available products.
And I sense you would have to throw orders of magnitude more tokens to get meaningfully better results (If anyone has access to experiments with GPT 5 class models geared up to use marginally more tokens with good results please call me out though).
Well, how many more dogs would you need to help you write your university thesis? It's a logical fallacy to assume that more tokens would somehow help - especially that even with cursory use you would see that LLMs, once they go off the road, they are pretty much lost, and the best thing you can do with them is to give them a clear context.
And I sense you would have to throw orders of magnitude more tokens to get meaningfully better results (If anyone has access to experiments with GPT 5 class models geared up to use marginally more tokens with good results please call me out though).