Outcome Reward Models for code verification allow one to trade accuracy for speed in the generate-then-rank paradigm. This can be further improved through a generate-prune-then-rank approach where a weaker verifier prunes solutions prior to ranking, thus saving work on incorrect tokens. We show that this hybrid approach can be 11.65 times faster than running the whole test suite while only being 8.33% less accurate.