Graph Neural Networks (GNNs) are widely used as regressors, for example to predict the accuracy of a neural architecture. Yet new message-passing (MP) layers are developed and benchmarked almost exclusively on classification tasks, and regression pipelines typically adopt a single MP layer without ablation. We ask how much the choice of MP layer matters for graph-level regression. Holding the architecture, loss and training recipe of four existing GNN regressors fixed, we substitute ten MP configurations spanning convolutional, isomorphism-based and attention-based designs. We evaluate them on eleven datasets of neural-network graphs that range from under ten to over a thousand nodes per graph and from a few hundred to over four hundred thousand samples, measuring rank correlation, prediction error, top-$k$ retrieval, latency and memory. MP choice changes results substantially, and we find that the best choice depends on graph size, training-set size and regression objective. Classical layers such as GEN, $k$-GNN and PNA match or exceed attention-based layers on small architecture graphs at lower cost, while GATv2 performs well on datasets with few large graphs.

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