With the wealth of models available (open source vs closed, api vs local), I find optimizing the cost-efficiency of your token consumption an important part of business-oriented AI engineering. You don't need "the best" for every task.
Same for me, I certainly don't have the same definition of success and failure either.
A more expensive model has *less* rooms for wandering around than a cheaper model.
If Claude wanders around during 10min until finding the most obvious solution, then I count it as a failure.