Medical licensing exams paint an incomplete picture of how AI performs in actual healthcare settings, where complexity extends far beyond multiple-choice questions into nuanced patient interactions and clinical decision-making. This gap between test performance and real-world capability has significant implications as healthcare systems increasingly consider AI integration. A comprehensive evaluation framework now reveals how nine leading AI models perform across 121 specific medical tasks that mirror daily clinical practice. The assessment covered five core areas: clinical decision support including diagnostic reasoning and treatment planning, clinical documentation from visit notes to procedure reports, patient communication spanning education materials and care instructions, medical research involving literature analysis and clinical data interpretation, and administrative coordination including scheduling and workflow management. Advanced reasoning models DeepSeek R1 and o3-mini achieved 66% success rates across these diverse scenarios, demonstrating superior performance in complex clinical reasoning compared to standard models. Claude 3.5 Sonnet matched this performance while requiring 15% less computational power, suggesting efficiency advantages for practical deployment. The evaluation methodology employed multiple AI evaluators acting as a jury to assess outputs against expert-defined clinical criteria. These findings represent a crucial shift from theoretical medical knowledge testing toward practical clinical competency assessment. The results suggest current AI capabilities remain insufficient for autonomous medical practice, with roughly one-third of tasks handled inadequately. However, the performance levels indicate potential for meaningful clinical support roles, particularly in documentation, patient education, and research assistance where human oversight can compensate for limitations.