AI-based methods such as AlphaFold have raised the possibility of using predicted models in place of experimentally-determined structures. Here we assess the accuracy of AlphaFold predictions by comparing them to density maps obtained from automated redeterminations of recent crystal structures and to the corresponding deposited models. Some AlphaFold predictions match experimental maps closely, but most differ on a global scale through distortion and domain orientation and on a local scale in backbone and side-chain conformation. Such differences occur even in parts of AlphaFold models that were predicted with high-confidence. Generally, the dissimilarities exceed those between high-resolution pairs of structures containing the same components but determined in different space groups. Therefore, while AlphaFold predictions are useful hypotheses about protein structures, experimental information remains essential for creating an accurate model.