The problem is that if benchmarks are run at a tight classifier that says "a request for high effort means check-under-every-stone regardless of simplicity" but a user request is run on a different classifier where "high means maybe high, maybe medium, maybe even low, even for meaningful tasks" then you're not getting the model that you saw in the benchmarks.
And, while you might be billed fewer tokens as a result (because the lower thinking would result in less investigatory work), you might not know this is happening, and know to dial up effort accordingly - you'd simply get a worse work product. And certainly, Anthropic's incentive for anyone on a subscription is to push this as aggressively as they can, so people use less of that subscription.
Sadly, I'd also expect that the OP's benchmark will be detected as a test of model capabilities, and thus be given a high classification so that this strategy remains undetected.