An articulated vehicle is a two-body design capable of precise maneuvering around obstacles, while carrying heavy loads over rough terrain. In the context of path planning for automated articulated vehicles, it is desirable to fully utilize the maneuverability of the vehicle to enable autonomous operation in confined areas. In this paper we study the impact of model accuracy in an optimization based path planner for an articulated vehicle. For this purpose, we compare the traditional kinematic bicycle model with a two-body articulated model. We evaluate performance in terms of path length, path quality, success rate and computation time through performing test queries in artificial environments and through experiments on a full scale articulated hauler. Results show that for simple, unidirectional maneuvers, performance differences are small, but for more difficult bidirectional maneuvers, the articulated model produces shorter and higher quality paths at a higher success rate. However, the articulated model has 2.75 times longer computation time on average.