Large language models (LLMs) have shown great promise in generating structured diagrams from natural language descriptions, particularly Mermaid sequence diagrams for software engineering. However, the lack of existing benchmarks to assess the LLM's correctness on this task hinders the reliable deployment of these models in production environments. To address this shortcoming, we introduce MermaidSeqBench, a human-verified and LLM-synthetically-extended benchmark for assessing LLM capabilities in generating Mermaid sequence diagrams from natural language prompts. The benchmark consists of 132 samples developed via a hybrid methodology of human-verified flows, LLM-based augmentation, and rule-based expansion. The evaluation uses an LLM-as-a-judge model to assess generation across various fine-grained metrics such as syntax correctness, activation handling, error handling, and practical usability. To demonstrate the effectiveness and flexibility of our benchmark, we perform initial evaluations on numerous state-of-the-art LLMs with multiple LLM judges which reveal significant capability gaps across models and evaluation modes. MermaidSeqBench provides a foundation for evaluating structured diagram generation and establishes the correctness standards needed for real-world software engineering deployment.