Language models do not "speak" any languages in a literal sense. They are machine learning models that are trained on data in a particular language (or languages), and learn to predict tokens (words) based on patterns in that data.
So you could say a given language model "understands" the language(s) it was trained on, insofar as it has learned probabilities of sequences of words, grammar, etc. - but it does not have any real "understanding" or generation of language. It is just predicting probabilities.
So the short answer is: a language model "speaks" the language(s) it was trained on, but not in a literal sense - it is just generating predictions based on patterns in the data. The more data in more languages, the more multilingual a language model could be - but it still wouldn't really "speak" multiple languages with comprehension.
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