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The light-use efficiency-based gross primary productivity (LUE-GPP) model is widely utilized for simulating terrestrial ecosystem carbon exchanges owing to its perceived simplicity and reliability. Variations in cloud cover and aerosol concentrations can affect ecosystem LUE, thereby influencing the performance of the LUE-GPP model, particularly in humid regions. In this study, the performance of six big-leaf LUE-GPP models and one two-leaf LUE-GPP model were evaluated in a humid agroforestry ecosystem from 2018–2020. All big-leaf LUE-GPP models yielded GPP values consistent with that derived from the eddy covariance system (<i>GPP</i>_EC), with <i>R</i>² ranging from 0.66–0.73 and <i>RMSE</i> ranging from 1.81–3.04 g C m⁻² d⁻¹. Differences in model performance were attributed to the differences in the quantification of temperature (<i>T</i>_s) and moisture constraints (<i>W</i>_s) and their combination forms in the models. The <i>T</i>_s and <i>W</i>_s algorithms in the eddy covariance-light-use efficiency (EF-LUE) model well characterized the environmental constraints on <i>LUE</i>. Simulation accuracy under the common limitation of <i>T</i>_s and <i>W</i>_s (<i>T</i>_s × <i>W</i>_s) was higher than the maximum limitation of <i>T</i>_s or <i>W</i>_s (Min (<i>T</i>_s, <i>W</i>_s)), and the combination of the <i>T</i>_s algorithm in the Carnegie–Ames–Stanford Approach (CASA) and the <i>W</i>_s algorithm in the EF-LUE model was optimized in combination forms, thereby constraining LUE for <i>GPP</i> estimates (<i>GPP</i>_BLO, <i>R</i>² = 0.76). Various big-leaf LUE-GPP models overestimated or underestimated <i>GPP</i> on sunny or cloudy days, respectively, while the two-leaf LUE-GPP model, which considered the transmission of diffuse radiation and the difference in photosynthetic capacity of canopy leaves, performed well (<i>R</i>² = 0.72, <i>p</i> < 0.01). Nevertheless, the underestimation/overestimation for shaded/sunlit leaves remained under different weather conditions. Then, the clearness index (<i>K</i>_t) was introduced to calculate the dynamic <i>LUE</i> in the big-leaf and two-leaf LUE-GPP models in the form of exponential or power functions, resulting in consistent performance even in different weather conditions and an overall higher simulation accuracy. This study confirmed the potential applicability of different LUE-GPP models and emphasized the importance of dynamic <i>LUE</i> on model performance.