Claude Code outperformed Antigravity and Codex in a comparative test to build an ESP32-based dashboard, delivering the most functional and efficient code for the hardware project.

Key Points

• The project required an ESP32 to display Claude usage limits, monitor DHT22 sensor data, and integrate with Home Assistant.
• Anthropic’s Claude Code provided a single, ready-to-flash INO file, demonstrating superior reasoning and integration capabilities.
• Google’s Antigravity (Gemini 3.1 Pro) prioritized complex planning and multi-file structures, which added unnecessary overhead for a local device.
• OpenAI’s Codex (GPT 5.5) relied heavily on ESPHome, forcing a YAML-based workflow that was suboptimal for the specific multi-task requirements.
• Claude Code successfully utilized a clever API workaround to fetch usage data, whereas other models struggled to find direct solutions.

Why it Matters

Choosing the right AI coding agent is critical for hardware development, as different models vary significantly in their ability to handle constrained environments and multi-subsystem integration. While some agents prioritize speed or established documentation, the most effective tool for embedded firmware is one that balances architectural simplicity with accurate, ready-to-deploy code.