Recently, content based 3D shape retrieval has been an active area of research. Benchmarking allows researchers to evaluate the quality of results of different 3D shape retrieval approaches. Here, we propose a new publicly available 3D shape benchmark to advance the state of art in 3D shape retrieval. We provide a review of previous and recent benchmarking efforts and then discuss some of the issues and problems involved in developing a benchmark. A detailed description of the new shape benchmark is provided including some of the salient features of this benchmark. In this benchmark, the 3D models are classified mainly according to visual shape similarity but in contrast to other benchmarks, the geometric structure of each model is modified and normalized, with each class in the benchmark sharing the equal number of models to reduce the possible bias in evaluation results. In the end we evaluate several representative algorithms for 3D shape searching on the new benchmark, and a comparison experiment between different shape benchmarks is also conducted to show the reliability of the new benchmark.