A Spanish Language Proficiency Dataset for AI Evaluation

Benchmarking Spanish reading comprehension remains challenging due to the scarcity of proficiency-calibrated resources grounded in authentic human assessments. We introduce IC-UNED-RC-ES, a benchmark comprising more than 6000 items derived from Instituto Cervantes examinations, converted to a machine-readable format while preserving exam structure, proficiency levels, and scoring criteria. Unlike many existing resources, IC-UNED-RC-ES includes a diverse set of exercise formats, combining common multiple-choice questions with new formats such as matching and fill-in-the-gap, which support a broader assessment of reading skills. The benchmark supports evaluation at both the item and exam levels and includes an exercise taxonomy with category-specific metrics. Baseline results with current AI systems reveal a strong difficulty effect (a 15-point drop from lower to advanced levels) and substantial variation across exercise types, with inference- and discourse-heavy categories reaching only 41%. IC-UNED-RC-ES provides a human-aligned, interpretable testbed for diagnosing strengths and weaknesses in Spanish reading comprehension and for tracking progress across model generations.