Definition modeling is used as a probe of what a language model has internalized about a domain, and it has been studied on general lexicography and on jargon-heavy specialist domains. We extend this task to mathematics, where a term can be defined very differently for different communities. In this context we study the baseline capacity of models to retrieve generate definitions, and their audience-bias when explaining mathematical terms without specific guidance. We further asses to what extent can they follow a certain style when directed, and to which degree being exposed to definitions aid or hurt model performance when solving mathematical questions.