There is a remarkable degree of polarisation in current debate about the capacities of Large Language Models (LLMs). One example of this is the debate about reasoning. Some researchers see ample evidence of reasoning in these systems, while others maintain that these systems do not reason at all. This paper seeks to shed light on this debate by examining the divergent uses of the term reasoning across different disciplines. It provides a simple clarificatory framework for talking about behaviour that highlights key dimensions of variation in how ‘reasoning’ is used across psychology, philosophy and AI. This highlights not just the extent to which researchers are talking past each other, but also that common inferences about model capability that accompany classification decisions are, in fact, far less compelling than they might seem.