In experiments to validate wind turbine design codes, the full inflow field moving into the rotor is never measured. Instead, it is reconstructed from spatially limited measurements by using an atmospheric model. As such, the inflow represents a source of uncertainty when validating turbine models. Here, we characterize the behavior and accuracy of modern inflow reconstruction techniques. We compare eight inflow models for nine ~10 minute reference inflows, three from a real-world experiment with a 2.8 MW turbine and six from a synthetic field campaign. We document the models' differences in time series behavior and statistical characteristics like mean profiles, turbulence intensity, and power spectra. Across all case studies, the Superstatistical Mann model had the smallest root mean square error (average of 0.93 m s^-1), and TurbSim had the largest (average of 1.19 m s^-1). PyConTurb performed similarly to the inflows based on the Mann model. Notably, error time series showed synchronized spikes across models, often corresponding to physically coherent features that were not observed in the hub-height measurements. This study points toward areas for future inflow reconstruction model development, and it provides the foundation for future work that will examine turbine load validation errors in conjunction with inflow errors.