The increasing volume of ground and space based ionospheric measurements has led to the development of a new class of assimilation models. Though these assimilation approaches have undergone extensive testing, an operational assessment must be undertaken before the community may confidently consider integrating the new models into their existing frameworks. Standard evaluations test a model's performance under realistic operating conditions, where real-time data availability is limited. Our analysis presents a parallel assessment of a next-generation ionospheric assimilation model, based on a Kalman filter, and the current standard, which applies a direct fitting approach to data assimilation. These models are executed continuously at a fixed cadence, assimilating the available real-time total electron content to produce a nowcast ionospheric specification. The nowcasts and forecasts, which are generated by a physics-based model using the nowcasts as an initial state, are compared to measurements form the Global Positioning System receivers and the Digital Ionospheric Sounding System. These comparisons, performed under varied geophysical and operational conditions, yield global and regional confidence metrics that provide a general and output-product specific knowledge of the modeled environment for the real-time end user.