Nanoparticles have a long history of successful application to medical technologies. Many of these technologies also employ the magnetic properties of some particles. Thus, an increased understanding of the dynamic properties of magnetic particles in time-varying magnetic fields is essential for advancement in sensing, counting, imaging or therapeutic modalities. A stochastic Langevin equation approach to particle modeling has been documented previously, however this new study focuses on comparison of the model to other theoretical modeling approaches as well as current experimental techniques from magnetic nanoparticle spectroscopy. The results show that the model works for a larger bandwidth than many separate approximate methods, and that the harmonics of the magnetization found through simulation contain enough information to infer microenvironmental parameters, therefore justifying spectroscopic usage.