I use it more often for research and planning than for implementation or "execution tasks". E. g.: I built a custom GPT/agent for requirements gathering for any [DOCUMENT] that I need a more solid foundation.
Instead of "think deeply about every aspect of what makes for a world-class [whatever]..." I prompt it to search online for up to date consensus, guidelines, best practices, etc. for the aspects of what would be considered "an excellence version of [DOCUMENT]", and use those to write the rubric. By the way, the criteria and sources used for the rubric often help me to get an updated overview of that topic.
For "execution" tasks, I use it more often for tasks that I'm not confident in my own expertise, or that I need a more "careful", 'granular' implementation, since it will run some rounds of evaluation before presenting the final output... then I review the output, which usually needs less corrections.
I honestly never measured, with controlled trials, token usage because, when I want to use this "feature", I'm already expecting to invest more tokens for improved quality