Social navigation has become increasingly important for robots operating in human environments, yet many newly proposed navigation methods remain narrowly tailored or exist only as proof-of-concept prototypes. Building on our previous work with Arena, a social navigation development platform, we now propose, Arena-Bench 2.0 a comprehensive social navigation benchmark of state-of-the-art planners, fully integrated into the Arena framework. To achieve this, we developed a novel plugin structure—implemented on ROS2—to streamline the integration process and ensure straightforward, efficient workflows. As a demonstration, we integrated various learning-based and model-based navigation approaches and constructed a diverse set of social navigation scenarios to rigorously evaluate each planner. Specifically, we introduce a scenario generation node that allows users to construct complex, realistic social contexts through a web-based interface. We subsequently perform an extensive benchmark of all integrated planners, assessing both navigational and social metrics. Our evaluation also considers factors such as sensor input, reaction time, and latency, enabling insights into which planner may be most appropriate under different circumstances. The findings offer valuable guidance for selecting suitable planners for specific scenarios.