Password guessers are instrumental for assessing the strength of passwords. Despite their diversity and abundance, comparisons between password guessers are limited to simple success rates. Thus, little is known on how password guessers can best be combined with or complement each other. To extend analyses beyond success rates, we devise an analytical framework to compare the types of passwords that guessers generate. Using our framework, we show that different guessers often produce dissimilar passwords, even when trained on the same data. We leverage this result to show that combinations of computationally-cheap guessers are as effective in guessing passwords as computationally-intensive guessers, but more efficient. Our framework can be used to identify combinations of guessers that will best complement each other. To improve the success rate of any guesser, we also show how an effective training dataset can be identified for a given target password dataset, even when the target dataset is hashed. Our insights allow us to provide a concrete set of practical recommendations for password checking to effectively and efficiently measure password strength.