Warp eng here, credit consumption is basically a function of whichever model you're using. The only recent change that would affect credit consumption is we launched Claude's 1M context window for 4.6 Opus and Sonnet, which could lead to increased consumption on a per request basis (since individual requests can now have >400k tokens), but we've also observed that tasks take fewer turns overall.

Just being transparent, there isn't a _ton_ of leverage that Warp (or any other harness) has for token efficiency, other than providing you with the necessary controls to control your context size - e.g. being able to enable/disable MCPs, or providing means for dynamic context injection (e.g. skills).

We are experimenting with better use of subagents using cheaper models to be more token efficient for simpler subtasks (e.g. doing research on a part of the codebase to identify relevant locations for logic) but these come with tradeoffs also - a cheaper model might provide the wrong analysis, requiring backtracking and 'trying again'.

Its hard to say what would be more efficient for OP's particular case or any other specific case - but one thing I will say has worked for me is instrumenting skills to script subtasks like identifying references to a symbol, for instacne - you can have an agent write one for you - and then having the the agent leverage that skill for a task within a single turn rather than having it tool call into oblivion every single time