Mathematical models are often used to gain theoretical insights into the epidemiology of sexually transmitted infections (STIs) and to inform policy around the prevention and treatment of STIs. Yet these models differ greatly in the assumptions they make, and can sometimes produce vastly different estimates of the likely impact of STI control programmes. So which modelling approaches are most realistic? How much bias might we be introducing with certain simplifying assumptions? This article summarises a recent paper that attempted to address these questions by comparing two broad modelling approaches: deterministic, frequency-dependent models and individual-based, network models.