I find that GPT 5.4 is okay at it. It does think harder for harder problems and still answers quickly for simpler ones, IME.

Is knowing how hard a problem is, before doing it, solved in humans?

It makes me think of this parallel: often in combinatorial optimization ,estimating if it is hard to find a solution to a problem costs you as much as solving it.

With a small bounded compute budget, you're going to sometimes make mistakes with your router/thinking switch. Same with speculative decoding, branch predictors etc.

Maybe it is an unsolved problem, but either way I am confused why Anthropic is pushing adaptive thinking so hard, making it the only option on their latest models. To combat how unreliable it is, they set thinking effort to "high" by default in the API. In Claude Code, they now set it to "xhigh" by default. The fact that you cannot even inspect the thinking blocks to try and understand its behavior doesn't help. I know they throw around instructions how to enable thinking blocks, or blocks with thinking summaries, or whatever (I am too confused by now, what it is that they allow us to see), but nothing worked for me so far.

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