Determining the reading level of children’s literature is an important task for providing educators and parents with an appropriate reading trajectory through a curriculum. Automating this process has been a challenge addressed before in the computational linguistics literature, with most studies attempting to predict the particular grade level of a text. However, guided reading levels developed by educators operate at a more fine-grained level, with multiple levels corresponding to each grade. We find that ranking performs much better than classification at the fine-grained leveling task, and that features derived from the visual layout of a book are just as predictive as standard text features of level; including both sets of features, we find that we can predict the reading level up to 83% of the time on a small corpus of children’s books.