Claude Code コスト最適化

Every interaction with Claude Code consumes tokens for both input and output. Input tokens include your prompt, system instructions, CLAUDE.md contents, and any files Claude reads. Output tokens cover the generated response. Understanding this breakdown is the first step to controlling costs. The most impactful optimization is reducing unnecessary context. Avoid sending entire files when only a specific function is relevant. Use targeted prompts like 'fix the validateEmail function in src/utils/validators.ts' instead of 'fix the validation bug in my project.' The latter forces Claude to read multiple files to locate the issue, consuming far more input tokens. Another key technique is structuring your CLAUDE.md to be concise. Every session loads this file, so trimming it from 500 lines to 100 focused lines saves thousands of tokens daily across sessions.

Claude Code supports multiple model tiers, and choosing the right one per task can cut costs by 50% or more. For simple tasks like formatting, renaming variables, or generating boilerplate, a smaller model works perfectly. Reserve the most capable models for complex architectural decisions, multi-file refactoring, or nuanced code review. You can configure model preferences in your CLAUDE.md or pass them per-session. A practical pattern is to default to the standard model for everyday work and switch to the advanced model only when tackling problems that require deep reasoning across multiple files. Consider creating task-specific aliases or scripts that automatically select the appropriate model. For instance, a 'quick-fix' alias can use a lighter model, while a 'deep-review' alias invokes the most capable tier.

Prompt caching is one of the most effective cost-reduction techniques available. When you send the same system prompt or CLAUDE.md content repeatedly, Claude can cache these tokens and charge significantly less for subsequent requests. Cache hits can reduce input token costs by up to 90%. To maximize cache hits, keep your system prompts and CLAUDE.md stable across sessions. Avoid adding timestamps or session-specific data to these files. The more consistent your prompts are, the higher your cache hit rate will be. You can also structure your workflows to batch similar tasks together. Processing ten files with the same type of transformation in sequence lets Claude cache the instruction context, making each subsequent file much cheaper to process.

Without visibility into spending, optimization is guesswork. Set up monitoring to track token consumption per session, per day, and per project. Claude Code provides session summaries that include token counts, which you can pipe into your own tracking system. Establish budget thresholds and alerts. A simple approach is to use notification hooks that warn you when daily spending exceeds a preset limit. This prevents runaway costs from long-running sessions or accidentally processing large codebases. Review your spending patterns weekly. You may discover that certain types of tasks consume disproportionate tokens. Optimizing just the top three token-consuming workflows often yields the biggest cost savings with minimal effort.