Abstract Benchmarking is essential for evaluating the capabilities of large language models (LLMs). However, existing multimodal benchmarks lack dedicated resources for traditional Chinese opera, a domain rich in cultural and visual complexity. To address this gap, we introduce the TCO-Dataset, a bilingual multimodal dataset designed to assess LLMs’ ability to interpret and reason about Chinese opera images. The dataset contains 1,000 multiple-choice questions paired with high-resolution images across eight major opera genres. Each sample includes a carefully selected image, a corresponding question focused on cultural and visual understanding, and an annotated answer for evaluation. The dataset supports both Chinese and English, enabling cross-lingual model assessment. All items were reviewed through multiple rounds of expert validation to ensure consistency and accuracy. The TCO-Dataset supports diverse applications, including still-image-based visual-cultural reasoning, cultural heritage preservation, and domain-specific AI development. Initial evaluations show significant performance variation across models, underscoring the dataset’s challenge and value for advancing multimodal understanding.