Foundation models (FMs) have achieved significant success across various tasks, leading to research on benchmarks for reasoning abilities. However, there is a lack of studies on FMs performance in exceptional scenarios, which we define as out-of-distribution (OOD) reasoning tasks. This paper is the first to address these cases, developing a novel dataset for evaluation of FMs across multiple modalities, including graphic novels, calligraphy, news articles, and lyrics. It includes tasks for instance classification, character recognition, token prediction, and text generation. The paper also introduces prompt engineering techniques, Out-of-distribution Reasoning Chain-of-Thought (ORCoT) and ORCoT+Few-Shot, to improve performance. Validation of FMs using various methods revealed improvements.