Once you truly understand what part of the process is increasing token consumption, you will be forced to use AI in a completely different fashion. According to the analysis presented by @ExponentialView "A coding agent that operates over 10 turns might need to re-read its full context every turn. That repetitive reading of context could use as many as 55x more tokens than a single-turn query for the same task. Actual active inference is probably only 15-20% of the total token consumption. The rest is invisible work that you, as a user, and possibly the company paying for it all, haven’t modelled." Proper documentation standard and knowing how to provide context effectively helps you reduce your token consumption. Think of token usage like fuel for your car. Don't waster it regardless of what model you are using.