We analyze the two recent Medicare alternative payment models, the comprehensive primary care plus (CPC+) and the primary care first (PCF). Both models comprise fee-for-service, traditional capitation, and pay-for-performance (P4P) components. The main objective of these reimbursement models is to advance toward value-based care. However, the models confer some hesitations since the P4P component is based on factors not entirely controlled by the practice, increasing the potential admission of healthier patients and affecting the profit of small primary care practices. We have modified the P4P component in both models to include a non-controllable agent (the hierarchical condition category score) and a controllable factor (the Bice–Boxerman continuity of care index) through a probabilistic classification model to predict hospital admissions. This study aims to determine the impact of adjusting the P4P component, in the CPC+ and PCF reimbursement models, on the profit per team, revenue for performance per team, and severity of admitted patients. We develop a mixed-integer programming formulation and analyze, using a 2k factorial design, the reimbursement models and the main elements of their adjusted P4P components (i.e., the probabilistic classification model coefficients and hospital admission threshold). The results indicate that the coefficients of the probabilistic classification model and the hospital admission threshold have a significant effect on the profit and revenue for performance per team. There is also a tendency of the PCF to admit less severe patients than the CPC+. Yet, the effects are more notable in the PCF payment model because the proportion of P4P in the total revenue under the CPC+ is minimal (16.5% versus <1%). Similarly, the PCF's downside is its sensitivity to P4P changes, displaying high variability in the output variables under analysis.