In recent years, there has been a surge in the development of models for the generation of synthetic mobility data. These models aim to facilitate the sharing of data while safeguarding privacy, all while ensuring high utility and flexibility regarding potential applications. However, current utility evaluation methods fail to fully account for real-life requirements. We evaluate the utility of five state-of-the-art synthesis approaches, each with and without the incorporation of differential privacy (DP) guarantees, in terms of real-world applicability. Specifically, we focus on so-called trip data that encode fine granular urban movements such as GPS-tracked taxi rides. Such data prove particularly valuable for downstream tasks at the road network level. Thus, our initial step involves appropriately map matching the synthetic data and subsequently comparing the resulting trips with those generated by the routing algorithm implemented in OpenStreetMap, which serves as an efficient and privacy-friendly baseline. Out of the five evaluated models, one fails to produce data within reasonable computation time and another generates too many jumps to meet the requirements for map matching. The remaining three models succeed to a certain degree in maintaining spatial distribution, one even with DP guarantees. However, all models struggle to produce meaningful sequences of geo-locations with reasonable trip lengths and to model traffic flow at intersections accurately. It is important to note that trip data encompasses various relevant characteristics beyond spatial distribution, such as temporal information, all of which are discarded by these models. Consequently, our results imply that current synthesis models fall short in their promise of high utility and flexibility.