[D] Character-level vs. word-level tokenization

Hi all,

I'm relatively new to the field of NLP and while reading a blog post from 2015 [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/) by Andrej Karpathy, I was wondering about this part of the "Further Reading" section:

>Currently it seems that word-level models work better than character-level models, but this is surely a temporary thing.

Aren't most state-of-the art models these days using some kind of vocabulary, i.e. whole words or at least sub-words? Text in the wild …

machinelearning tokenization