How to cut Claude AI costs by routing tasks to cheaper models based on complexity

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A developer writing on DEV Community outlines a cost-saving workflow for Claude Code that assigns tasks to different AI models based on their complexity and value. The strategy treats the most expensive model, Claude Opus 5, as an orchestrator for planning and design decisions, while delegating implementation, boilerplate, and testing to cheaper models like Sonnet. The author notes that Opus 5 costs $10 per million input tokens and $50 per million output, roughly double the price of its predecessor, making it wasteful for routine coding chores. Beyond token savings, keeping the expensive model away from raw file contents and stack traces also preserves its context quality over long sessions. The practical setup involves configuring subagents in Claude Code with precise descriptions and system prompts, which the author warns requires more careful tuning than the initial three-step process suggests.

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