AI coding agents like Claude Code, Codex, and Cursor are revolutionizing development workflows, but their token consumption can be surprisingly high. This analysis breaks down the real costs behind a single task, showing how agents read projects, find files, analyze errors, modify code, and run commands—each step consuming tokens. For developers and teams using these tools, understanding these patterns is crucial for managing budgets and optimizing usage. The post offers practical advice on reducing waste, such as limiting context windows and batching tasks. As AI coding becomes mainstream, this cost transparency is invaluable for indie hackers and engineering leaders alike.
A detailed analysis of token consumption in AI coding agents, providing actionable cost optimization tips.