Graph Neural Networks (GNNs) benchmarks often report single point estimates, even when performance differences are small relative to variation across random seeds, train/test splits, and datasets. Confidence intervals, paired comparisons, multiple-comparison correction, and rank-based aggregation are standard statistical tools, but they are rarely the default output of graph-learning benchmark suites. We introduce GraphNetz, a benchmarking framework whose default output is a structured statistical report rather than a raw accuracy table. GraphNetz currently includes 63 dataset loaders, four task types, and five canonical GNN architectures, while also supporting custom datasets and models. The framework standardizes multi-seed evaluation and automatically returns per-cell confidence intervals, Holm-corrected paired tests, and Friedman-Nemenyi critical-difference diagrams across tasks. In a cross-category benchmark over ten heterogeneous tasks, apparent rank differences among four canonical node-level encoders fall within a single Nemenyi clique, indicating that none is significantly better than the others at $α= 0.05$. GraphNetz therefore provides researchers with a reproducible computational and statistical pipeline to benchmark new graph-learning methods against standard architectures, over different tasks and a wide set of applications, while reporting principled statistical evidence for benchmarking which accounts for seed uncertainty. This framework is set to serve the graph-learning community with a reproducible and honest model comparison ready to be added to papers.