  Spatial planning in cluttered environments is crucial for mobile systems,
particularly agile quadrotors. Existing methods, both optimization-based and
learning-based, often focus only on success rates in specific environments and
lack a unified platform with tasks of varying difficulty. To address this, we
introduce FlightBench, the first comprehensive open-source benchmark for 3D
spatial planning on quadrotors, comparing classical optimization-based methods
with emerging learning-based approaches. We also develop a suite of task
difficulty metrics and evaluation metrics to quantify the characteristics of
tasks and the performance of planning algorithms. Extensive experiments
demonstrate the significant advantages of learning-based methods for high-speed
flight and real-time planning, while highlighting the need for improvements in
complex conditions, such as navigating large corners or dealing with view
occlusion. We also conduct analytical experiments to justify the effectiveness
of our proposed metrics. Additionally, we show that latency randomization
effectively enhances performance in real-world deployments. The source code is
available at \url{https://github.com/thu-uav/FlightBench}.
