We analyze comparative questions, i.e., questions asking to compare different items, that were submitted to Yandex in 2012. Responses to such questions might be quite different from the simple “ten blue links” and could, for example, aggregate pros and cons of the different options as direct answers. However, changing the result presentation is an intricate decision such that the classification of comparative questions forms a highly precision-oriented task. From a year-long Yandex log, we annotate a random sample of 50,000 questions; 2.8% of which are comparative. For these annotated questions, we develop a precision-oriented classifier by combining carefully hand-crafted lexico-syntactic rules with feature-based and neural approaches—achieving a recall of 0.6 at a perfect precision of 1.0. After running the classifier on the full year log (on average, there is at least one comparative question per second), we analyze 6,250 comparative questions using more fine-grained subclasses (e.g., should the answer be a “simple” fact or rather a more verbose argument) for which individual classifiers are trained. An important insight is that more than 65% of the comparative questions demand argumentation and opinions, i.e., reliable direct answers to comparative questions require more than the facts from a search engine’s knowledge graph. In addition, we present a qualitative analysis of the underlying comparative information needs (separated into 14 categories like consumer electronics or health), their seasonal dynamics, and possible answers from community question answering platforms.