Benchmark datasets for evaluating profile-based ecosystem flux methods across environmental gradients are currently lacking. This limits method evaluation, cross-site intercomparison, and the expansion of tower-based monitoring for gases not routinely measured by the eddy covariance (EC) method. Here, we present a benchmark dataset derived from 47 terrestrial towers in the National Ecological Observatory Network (NEON), integrating co-located EC, concentration profiles, tower geometry, and canopy structural metrics. We developed a dataset to evaluate the performance of three widely used gradient flux approaches – the modified Bowen ratio (MBR), aerodynamic (AE), and wind-profile (WP) methods – against co-located EC measurements of CO₂ and H₂O. We evaluate how canopy structure, sensor height configuration, and data filtering influence agreement with EC across ecosystems, with canopy heights ranging from 0.15 to 53 m. Performance varied strongly among approaches and measurement configurations. Across all ecosystems, 11% of height-pair combinations for CO₂ and 19% for H₂O achieved moderate-to-strong concordance with EC, with the MBR approach providing the most consistent performance. Reliable estimates most often occurred when both sampling heights were above the canopy (AA) or when one level was above the canopy and the other within sparse canopies (AW). Concordance declined as canopy complexity increased, highlighting persistent challenges in tall, complex forests. Since filtering substantially reduced data availability, we developed an ensemble framework that combined reliable estimates across the three GF approaches and height pairs. Ensemble GF fluxes reproduced seasonal diel dynamics measured by EC across ecosystems (R<sup>2</sup> = 0.58–0.99), capturing both the seasonal magnitude and direction of net ecosystem exchange. Benchmark analyses show that the GF method can provide robust ecosystem-scale flux information when deployed under favorable structural and micrometeorological conditions. The released benchmark dataset provides a resource for testing new flux algorithms, optimizing sensor placement, benchmarking profile methods, and extending multi-gas tower observations to be more inclusive of gases that are difficult to measure with fast-response EC instrumentation, including CH₄, N₂O, volatile organic compounds, and reactive pollutants.