As text-to-motion (T2M) models move beyond short action captions toward real-world applications, they are increasingly expected to follow diverse instructions. However, existing evaluation protocols remain insufficient for systematically assessing instruction following, physical plausibility, and fine-grained controllability under such diverse language instructions. To address this gap, we introduce T2MBench, a benchmark with 1,025 prompts across four categories—Dynamics, Complexity, Interaction, and Accuracy—covering 14 representative T2M models and three evaluation dimensions: LLM-Based Evaluation, Multi-Factor Motion Evaluation, and Fine-grained Accuracy Evaluation. Our results reveal substantial gaps in current T2M models’ ability to consistently follow diverse language instructions while maintaining motion plausibility and fine-grained control.T2MBench establishes a reusable diagnostic benchmark for advancing research toward more reliable T2M generation under diverse language instructions. Data and materials are available at https://github.com/Courteous121/T2MBench-release.