Transient simulation of complex converter topologies is a challenging problem, especially in detailed analysis tools like SPICE. Much of the recent literature on SPICE transistor modeling ignores the requirements of application designers and instead emphasizes detail, physical accuracy, and complexity. While these advancements greatly improve model fidelity, they also serve to increase computational complexity, making the resulting models less attractive to application designers. This is in part because transistor models presented for SPICE are generally evaluated by accuracy alone, without consideration for the computational cost of model elements. Models designers tend to optimize toward the metrics by which their work is judged; with little precedent for disclosing computation time in addition to accuracy, the natural outcome is a plethora of highly accurate, detailed models which are less than ideal for complex application simulations. In order to optimize models for such simulations, this dissertation quantifies the relative computational performance of modeling approaches and contextualizes the results with regard to accuracy. This required the development of a new methodology for quantifying model computational performance. An extensive review of the relevant literature is undertaken to select candidate SiC MOSFET models likely to fare well in complex application simulations. By analyzing the accuracy and computational performance tradeoffs of these candidates, new insights into transistor model design and optimization are identified. These insights inform a new SiC MOSFET model synthesized and optimized from the best-of-breed model elements identified. By focusing on retaining high accuracy while making critical performance optimizations, the new model is ideally suited for complex converter simulations.