Metal additive manufacturing (also known as 3D printing) excels at creating large and intricately shaped metal parts in small batches that would be too expensive or even infeasible to make with traditional manufacturing methods. A deep understanding of the physics of the process, especially a precise prediction of how temperatures vary over time across a part when it is printed, is needed to assure the quality of the parts and to reliably plan how to build these parts. However, the current thermal models that provide these predictions are too slow to create or too inaccurate for practical usage. While recent research efforts have led to the development of a variety of fast-running models, a systematic comparison and understanding of the tradeoffs among speed, robustness and accuracy of these models does not exist. To address this gap, this Engineering Research Initiation (ERI) project looks to conduct extensive comparison of the available models with a range of complexities and benchmark them relative to each other. While offering significant hands-on training experience to students, this effort will enable future manufacturers and researchers to choose models that are well suited for their application and thereby create better quality parts more quickly and with less wasted material. Consequently, these efforts contribute to improving the advanced manufacturing capabilities of the United States which are critical to economic and national security interests. This work loo