This paper compares two influential theories of processing difficulty: Gibson (2000)'s Dependency Locality Theory (DLT) and Hale (2001)'s Surprisal Theory. While prior work has aimed to compare DLT and Surprisal Theory (see I compare estimated surprisal values from two models, an RNN and a Transformer neural network, as well as DLT integration cost from a hand-parsed treebank, to reading times from the Dundee Corpus. The results for integration cost corroborate those of Ultimately, I conclude that a broad-coverage model must integrate both theories in order to most accurately predict processing difficulty.