Similarly, tasks that are too easy also aren’t ideal either. If a small model makes mistakes and backtracks but eventually cracks it, it will be using a lot more tokens than a bigger model that does it all with minimal mistakes.

I think what you're really getting at is that it's only useful if the benchmarks are predictive of your workloads. If it predicts well (for example, your tasks are equally easy), then the fact that a larger model can complete it more quickly means that you may be able to complete the task more cheaply, depending on the token cost.

If the benchmarks are non-predictive, well, you can't use them for much of anything, which is of course a recurring problem with every benchmark ever.