Claude Code users frequently encounter high costs driven by extensive context windows (the amount of information an AI model processes at once), rather than the length of individual prompts alone. To mitigate this, the article advises aligning model selection with task complexity: reserve the high-performance Opus model for architectural decisions and complex debugging, use Sonnet for daily edits and tests, and rely on Haiku for simple formatting tasks.

Effective token management requires keeping persistent files like CLAUDE.md lean, as they consume resources in every interaction. Developers should point the tool to specific files or line ranges rather than entire repositories, and use subagents to keep verbose output isolated from the main conversation. Proactively using context-compacting commands and inspecting what consumes tokens before they accumulate can further prevent wasted resources.

Claude Code users frequently encounter high costs driven by extensive context windows (the amount of information an AI model processes at once), rather than the length of individual prompts alone. To mitigate this, the article advises aligning model selection with task complexity: reserve the high-performance Opus model for architectural decisions and complex debugging, use Sonnet for daily edits and tests, and rely on Haiku for simple formatting tasks.

Effective token management requires keeping persistent files like CLAUDE.md lean, as they consume resources in every interaction. Developers should point the tool to specific files or line ranges rather than entire repositories, and use subagents to keep verbose output isolated from the main conversation. Proactively using context-compacting commands and inspecting what consumes tokens before they accumulate can further prevent wasted resources.