CSIEval: A 6G Standard-Aligned Benchmark for AI-Based CSI Models

As 6G evolves toward an AI-native paradigm, channel state information (CSI) has become a key target for AI-enabled wireless communication, with growing research on CSI estimation, prediction, and compression feedback. However, existing evaluations lack systematic alignment with emerging 6G standards and use inconsistent protocols across studies, hindering fair comparison. Thus, we propose CSIEval, a standards-aligned benchmark for CSI processing that encompasses three representative tasks. CSIEval constructs and processes datasets in accordance with relevant communication protocols and evaluates models under a unified framework across four dimensions: performance, storage, efficiency, and generalization. The benchmark includes 8,000 CSI samples and more than 15 evaluation metrics, providing broad coverage of model capabilities. Experimental results demonstrate that CSIEval provides a comprehensive characterization of model behavior, enables fair comparisons across different models, and reveals the trade-offs among predictive accuracy, resource consumption, and generalization capability. Code and project resources are available at the anonymous project page: https://anonymous.4open.science/r/csieval-8018.