For any task, a Pareto frontier exists of costs v. success-rate. Anthropic has 20 model/effort combinations currently, and maybe 10 (?) of those are never the best choice for any task (where a cheaper combinations provides same or better results). You know your workloads, and can estimate which combination to use, but models can too (and will get better).
If Sonnet can increase its reasoning effort, it should know that at some point switching to Opus makes more sense than cranking itself up to xhigh/max effort. A model can, and should be calibrated to know if a cheaper config has the same or higher success probability. It should also know which config has the next better performance (whether it's itself at a higher effort, or a more costly model).
At any price point, there exists the best model/effort combination. Why be against letting a person set the effort/time (e.g. on a slider with 5 options) and letting the models self-allocate tasks based on their strengths?
Just because it would be nice to have that, doesn’t mean that it’s a good idea in practice, or that there currently exists a viable way to implement it.
> At any price point, there exists the best model/effort combination.
People are already offended by any barely perceptible tweaks to models; can’t wait to hear what they think about having their options reduced to whatever Anthropic decides is the pareto-optimal “do the thinky thing” setting this week.
Yet, current non-users simply don't care about reading 4 system cards during roll-in and then keeping up with new ones every few weeks.
And, a solid adaptive thinking classifier is trivial compared to Anthropic's remaining tech stack. Task complexity estimation and model/effort selection are simple predictions with clear proxies in current data and millions of training samples generated daily by current users.