A comparative build-off tested Claude, GPT-5.5, and Grok 4.5 on identical application development tasks, with results now circulating across developer communities. For healthcare AI implementations, these head-to-head evaluations matter because they expose which models handle clinical coding workflows, EHR integration logic, and compliance-sensitive tasks most reliably. The emerging developer friction around Claude's language patterns and the proliferation of alternative interfaces (terminal replacements, cost optimization tools) signals that model selection for healthcare systems is becoming more competitive and use-case specific. Healthcare organizations evaluating foundational models for clinical decision support or documentation automation should factor in these real-world performance comparisons rather than relying on vendor claims alone.