The evaluation of ranking tasks remains a significant challenge in natural language processing (NLP), particularly due to the lack of direct labels for results in real-world scenarios. Benchmark datasets play a crucial role in providing standardized testbeds that ensure fair comparisons, enhance reproducibility, and enable progress tracking, facilitating rigorous assessment and continuous improvement of ranking models. Existing NLP ranking benchmarks typically use binary relevance labels or continuous relevance scores, neglecting ordinal relevance scores. However, binary labels oversimplify relevance distinctions, while continuous scores lack a clear ordinal structure, making it challenging to capture nuanced ranking differences effectively. To address these challenges, we introduce OrdRankBen, a novel benchmark designed to capture multi-granularity relevance distinctions. Unlike conventional benchmarks, OrdRankBen incorporates structured ordinal labels, enabling more precise ranking evaluations. Given the absence of suitable datasets for ordinal relevance ranking in NLP, we constructed two datasets with distinct ordinal label distributions. We further evaluate various models for three model types, ranking-based language models, general large language models, and ranking-focused large language models on these datasets. Experimental results show that ordinal relevance modeling provides a more precise evaluation of ranking models, improving their ability to distinguish multi-granularity differences among ranked items-crucial for tasks that demand fine-grained relevance differentiation.