While models and digital twins are heavily related, how we use these models, how we understand those models, and even what these models represent for digital twins might change over time. Existing research provides model classifications from different angles, e.g., kind of usage, model content, nature of abstraction; however, a common understanding of which kinds of models could be useful and are used in digital twin engineering and during its lifetime is missing. In this work, we present an initial faceted classification to label models based on different properties relevant for digital twins in manufacturing. We show the application of the classification on different models of a digital twin in manufacturing. The classification can make finding and reusing models in and for digital twins easier, help to identify information gaps, and give inspiration about how models might change their purpose and nature over time.