I’ve spent a significant amount of time dorking around with different models and effort levels. Sharing what I’ve learned as I suspect the bulk of this community uses the effort setting incorrectly. Not every task demands the same effort level. The nature of the work shapes how deeply you want the model thinking about how to do it. In most cases less is better than more assuming you’re not just YOLOing your way through work blindly. For implementation start low and ONLY step it up if reviews keep failing. Measurable and easy to automate. A high cost model on low routinely beats a low cost model on high. For review start high and step it down if you’re consistently going several levels deep in review and correction cycles due to increasingly irrelevant findings. High effort levels will find issues endlessly if you let it. Stepping down effort naturally filters out the nit picky nonsense you don’t really care about. For planning and design model matters FAR more than effort level. Start high but don’t be afraid to step it down if the output isn’t measurably better. You’ll likely land in the medium realm eventually this way. If you care about token efficiency this matters a ton. Idiotic max everything strategies waste your money and produce worse results. TLDR; implementation low, review high, planning and design medium. Good results that are budget friendly. submitted by /u/berndalf
Originally posted by u/berndalf on r/ClaudeCode