Capacity expansion models are computational tools used by utility planners looking to find the least cost option for planning a system and by researchers or policymakers attempting to understand the effects of various policy implementations. In this report, we analyze the impacts of model configuration and detail on resource selection decisions of capacity expansion models. Our analysis focuses influential in avoiding investments in excess thermal capacity. We also compare computation time between configurations to evaluate tradeoffs between computational burden and model accuracy. From this analysis, we find that certain advanced dispatch representations (e.g., DC optimal power flow) can have dramatic adverse effects on computation time but can be largely inconsequential to investment outcomes, at least at the renewable penetration levels modeled. Finally, we find that certain underappreciated aspects of investment decisions and model representations thereof, such as spur lines for new renewable capacity, can influence model outcomes particularly in the renewable technology and location chosen by the model. Though this analysis is not comprehensive and results are specific to the model region, input assumptions, and optimization-modeling framework employed, the findings are intended to provide a guide for model improvement opportunities.