Classifying programs based on their tasks is essential in fields such as plagiarism detection, malware analysis, and software auditing. Traditionally, two approaches are employed for this classification: static classifiers analyze program syntax, while dynamic classifiers observe their execution. Although dynamic analysis is commonly regarded as more precise, it is often considered impractical due to high overhead, leading the research community to largely dismiss it. In this paper, we revisit these assumptions by comparing static and dynamic analyses using the same classification representation: opcode histograms. We show that dynamic histograms—generated from instructions actually executed—are only marginally (4-5%) more accurate than static histograms in non-adversarial settings. However, if an adversary is allowed to obfuscate programs, the accuracy of the dynamic classifier is twice higher than the static one, due to its ability to avoid observing dead-code. Obtaining dynamic histograms with a state-of-the-art Valgrind-based tool incurs an 85x slowdown; however, once we account for the time to produce the representations for static analysis of executables, the overall slowdown reduces to 4x: a result significantly lower than previously reported in the literature.