Not quite. These cost-per-task benchmarks report the cost of the task after the model gives its initial answer. The total cost is irrelevant, and isn't factored into the model's decisions - a run of the full benchmark for something like Fable might cost $10k.

What I'm looking for is the inverse. I want to give the model a budget of $100, and see how much it can accomplish with that $100. For smaller models, this means they can do more than just choose thinking amount, they can do something like a /loop to keep iterating on a problem until they get it right.

Can something like Deepseek V4 Flash get more answers correct than Fable, when given equal budgets?

Think of it as answering this question: How much intelligence can you get out of a model given a budget of $100? A cost-per-task dash correlates, but it doesn't give you an answer to that question.

It's still spooky to see exponential scales on the money axis.

I do not have exponential funds in my allowance...