Claude Code: 6 Tips to Avoid Token Shortages

Token management has become a major challenge for users of Claude Code, an AI favored by developers. To avoid exceeding hourly or daily quotas, several techniques have been highlighted.

One of the most straightforward methods is to use the "opusplan" mode. This mode allows users to utilize Claude Opus 4.6 solely for planning, while Claude Sonnet 4.6 is used for the rest of the tasks. Although Claude Sonnet 4.6 is theoretically less powerful than Claude Opus 4.6, it remains one of the best models on the market for coding. To activate it, simply use the command /model opusplan.

Another essential technique is to guide Claude in managing context. The command /compact allows for compacting the agent's context, thereby reducing token consumption. Instructions can be integrated into the CLAUDE.md file to automate this process. Additionally, using natural language prompts that encourage the AI to be concise significantly reduces token usage.

It is also recommended to manually clean the context by checking the tools activated by default. These tools can account for between 5 and 15% of the total context size. Disabling unnecessary MCP servers, skills, and plugins can save valuable tokens.

For more advanced solutions, using a compression proxy like RTK proves to be very effective. This proxy filters and compresses unnecessary outputs, reducing contextual noise by 60 to 90% across a hundred common commands such as git, cargo, pytest, npm, and docker. RTK eliminates noise (comments, whitespace, boilerplate), groups similar items, and deduplicates repeated lines.

Integrating a knowledge graph can also reduce token consumption. Tools like code-review-graph, which already has over 10,000 stars on GitHub, provide Claude with a pre-digested map of the codebase, thus avoiding repeated analyses. Code-review-graph reports average gains of 8.2x in the number of tokens used.

Finally, an original approach involves using the Claude Code caveman-compression skill. By simplifying the language, this method can save up to 58% of the tokens used for grammar, knowing that 30 to 40% of the tokens in a natural language text are used solely for grammar.