Differences in cropland definitions, spatial resolution, and mapping strategies complicate product selection. We harmonized and evaluated eight 2020 cropland and land-cover products across mainland China using hierarchically labeled probability samples. Fixed map predictions enabled comparisons across common task-specific and product-definition-matched reference labels. Evaluation covered spatial agreement, design-weighted classification accuracy, design-based area bias, cropland-type responses, and cross-scale performance. Only 26.8% of pixels in the cropland union were classified as cropland by all products. Within the consensus-cropland evaluation domain, CropLayer achieved the highest overall accuracy (0.961) and F1 score (0.844). CCropLand30 led 30 m products in F1, whereas the Esri land-cover product (ESRI-LC) had national area bias closest to zero (−2.26%). Most 30 m products showed commission-dominated errors and positive area bias. Product responses varied across cropland types, and reference labels altered performance estimates. Switching from unified-agriculture to product-definition-matched labels reduced ESRI-LC’s F1 from 0.765 to 0.682 and shifted area bias from −8.08% to +14.88%. Spatial aggregation reduced F1 for some fine-resolution products without consistently changing local cropland-proportion error. These results show that classification accuracy and area bias yield different rankings, supporting product selection according to application, cropland definition, landscape context, evaluation metric, and spatial support.