Time complexity. The Stanford parser is a phrase structure parser that creates dependencies as a post processing step. So, assuming that they use some variation of CKY, the time complexity is O(N^3 |G|) where |G| is the size of the grammar. This uses Nivre-style greedy parsing, which is O(N).

So, a slightly fairer comparison would be e.g. the Malt parser. Although this will probably be better than that in terms of accuracy, since last time I checked the Malt parser doesn't use dynamic oracles yet and by default doesn't integrate Brown clusters or word embeddings (though you could do that yourself). Though I wonder a bit about feature set construction, because in my experience perceptrons are far more sensitive to adding 'wrong' features than e.g. SVM classifiers. This becomes interesting especially when you train a model for another language or dependency scheme annotation, since the features that are relevant differ per set-up.