Traffic simulation is a tool used by urban planners to assess the impact of new urban designs and public policies on mobility. Over the years, numerous traffic models have been proposed, each model offering different levels of details and performances. Multi-level model coupling is an interesting approach to combine the advantages of complementary representations while limiting their drawbacks. In this paper, we design and evaluate the performances of hybrid traffic models combining a microscopic model (IDM) with a mesoscopic model (event-driven and queue-based). The results show that microscopic models have more diversity in terms of behaviors but reduce the vehicle average speed and mesoscopic models are more efficient in terms of computational time but display a higher vehicle speed. Their hybridization then enables to find a balance between scalability and the variety of the observed behaviors.