Causes of climate predictions’ uncertainty include wide spread in modeled gross primary productivity (GPP) for evergreen broadleaf forests. Deterministic predictions inherently lack the portion of variability that a regression’s error term summarizes. Omitted predictors’ contribution to error represent simulations’ necessary underestimation of real variability. Earth system model outputs with high variability relative to reference data warrant skeptical examination. We compare three statistical and 15 process models to site-level means, seasonal amplitude and driver responsiveness of GPP as calculated at six Amazon eddy covariance (EC) towers. Current month’s weather determines only 12% of the variability in EC GPP, implying that models whose predicted GPP’s variability approaches that of EC GPP probably are substantially hypersensitive to weather drivers. Roughly half the models have stronger seasonal GPP variability than ECs show, and inaccurately identify the timing of annual minimum GPP. Responses to temperature and light for some highly seasonal models are of the opposite sign as EC GPP’s. Strongly seasonal models’ deepest dip in photosynthesis both occurs later in the dry season and is more severe than EC estimates. Excessive reactivity to drivers appears to cause the high simulated variability of the strongly seasonal models.