No studies have compared how well different prediction models discriminate older men who have a radiographic prevalent vertebral fracture (PVFx) from those who do not. We used area under receiver operating characteristic (AUROC) curves and a net reclassification index to compare how well regression-derived prediction models and non-regression prediction tools identify PVFx among men age ≥ 65 years with femoral neck T-score ≤ −1.0 enrolled in the Osteoporotic Fractures in Men (MrOS) Study. The AUROC for a model with age, bone density (BMD), and historical height loss (HHL) was 0.682 compared to 0.692 for a complex model with age, BMD, HHL, prior non-spine fracture, body mass index, back pain, grip strength, smoking, and glucocorticoid use (p-values for difference in five bootstrapped samples 0.14 to 0.92). This complex model, using a cutpoint prevalence of 5%, correctly re-classified only a net 5.7% (p-value 0.13) of men as having or not having a PVFx compared to a simple criteria list (age ≥80 years, HHL >4 cm, or glucocorticoid use). In conclusion, simple criteria identify older men with PVFx as well as regression-based models. Future research to identify additional risk factors that more accurately identify older men with PVFx is needed.