Why Advanced LLMs Fail at Firmware Development

Advanced LLMs like DeepSeek-R1 achieve only 55% accuracy on embedded systems tasks, revealing a critical capability gap despite excelling at general programming. The real issue: current benchmarks measure single-shot code generation without the iterative feedback loops—compilation errors, hardware testing, debugging—that characterize real embedded development. This distinction is essential for developers evaluating AI tools for firmware work, explaining why models that can build a Next.js app in one shot fail at basic components like push buttons.