On MNIST KNN with clever feature engineering will get you 99.4%, and a crazy deep net ensemble will get you 99.8%. I'm not sure what your point is.

MNIST is the todo list of machine learning - it's a necessary but not sufficient condition for knowing which algos are good. In other words it's only useful for finding out which algos aren't good (e.g. if your code only gets 90% on MNIST you know there's a bug, but if your code gets 99% on MNIST it doesn't really mean much).