Mastering Claude Code Token Efficiency

Claude Code efficiency is driven by managing the context window and maximizing prompt caching to lower token costs. Users should initialize session settings early, use @-mentions for files, and minimize command output to keep the conversation history focused. Regularly clearing or compacting sessions prevents historical data from inflating the cost of future turns.

Key Points

• Prompt caching is the most effective way to reduce costs, but it is broken by mid-session changes to models, effort levels, or long periods of inactivity.
• Output tokens are significantly more expensive than input tokens because they require sequential GPU processing during the decode phase.
• Every file read and command output stays in the session context, meaning users pay to re-send that data on every subsequent turn.
• Strategic use of commands like /clear, /compact, and /rewind helps maintain a relevant context window and prevents the model from becoming distracted by irrelevant data.
• Subagents can be used to isolate high-output tasks, returning only the necessary information to the main session to save on context space.

Sentiment

Skeptical and frustrated, with a focus on the perceived high manual overhead and cost of the tool.

In Agreement

• Learning the idiosyncrasies of agentic tools is a necessary step for professional efficiency.
• Using specific commands like /statusline or custom hooks can help users monitor token usage and cache status effectively.
• Manually prompting the model for 'fast answers' can be a useful workaround to save costs when high-effort reasoning isn't required.
• The /clear and /compact commands are essential for maintaining a lean context in long-running sessions.

Opposed

• The 'you're holding it wrong' approach is a product failure; the software should manage context and caching automatically.
• A one-hour cache TTL is too short and unfairly penalizes developers for taking breaks or working at a human pace.
• The shift from flat-fee editors to pay-per-token agents is a regression that forces developers to focus on 'gasoline usage' instead of productivity.
• @-mentions can be less efficient than targeted search/read tools because they may force the model to ingest entire large files unnecessarily.
• The opaque nature of the system makes it impossible for users to truly evaluate or trust the cost-saving tips provided.