Simple Bench Dataset is a structured evaluation collection derived from the Simple Bench benchmark, designed to assess reasoning, comprehension, and multiple-choice question-answering capabilities of large language models through concise yet non-trivial problems that require logical inference rather than simple retrieval; each sample consists of a natural language input containing a question with multiple-choice options (A–F) and an output representing the correct answer, enabling straightforward and deterministic evaluation; the dataset is model-agnostic and optimized for benchmarking performance across reasoning tasks, fine-tuning QA systems, and comparing robustness on short-form logical problems, with evaluation typically performed via exact match accuracy or option-level classification, making it suitable for standardized and reproducible LLM assessment pipelines.