How to Stop Burning Tokens on Claude Code (The Complete Guide)

A vague 10-word prompt that requires 4 rounds of clarification costs more tokens than a precise 80-word prompt that works on the first try. The most expensive AI interaction isn't the long one — it's the one you have to repeat. Here are 8 techniques that cut token usage in half across Claude Code, Cursor, and every other AI coding tool.

Why Token Waste Happens

Every AI coding tool works the same way under the hood: your prompt plus the entire conversation history gets sent to the model with each message. Message 1 is cheap. Message 20 is expensive — because the model re-reads all 19 previous messages before generating a response.

This means the biggest token drain isn't complex prompts. It's long conversations. A 30-message conversation where each message re-reads the full history costs roughly 5x what six separate 5-message conversations would cost for the same total work.

The second drain is iteration. "Add auth" → "No, I meant OAuth" → "With Google provider" → "And add rate limiting" → "Also handle refresh tokens" costs five interactions when one detailed prompt would have gotten it right: "Add OAuth authentication with Google provider, including rate limiting on the auth endpoints and refresh token handling."

The 8 Techniques

1. Start fresh conversations every 15-20 messages. This is the single most impactful habit. Summarize your current progress in 3-4 sentences, start a new chat, paste the summary as context. Your token cost per message drops back to baseline.

2. Write prompts like handoff documents. Include what exists, what you want changed, what should NOT be touched, and the expected outcome. One precise prompt replaces 3-4 vague ones. Net token savings: 60-70%.

3. Use the right model for the task. Claude Sonnet for routine edits. Opus for complex reasoning. Don't use the most powerful (and most expensive) model for tasks that don't need it. In Cursor, manually select the model instead of using the default.

4. Trim your input. If you're asking Claude Code to review a file, extract the relevant section — don't feed it the entire 1,000-line file when only 50 lines matter.

5. Don't ask the AI to repeat or reformat. Copy the output and reformat it yourself. "Can you rewrite that as bullet points?" costs the same as the original response plus the new one. Select the text, reformat locally.

6. Use Projects for persistent context. In Claude, upload your project documentation, coding standards, and preferences to a Project once. Every conversation inherits this context without burning tokens re-explaining it.

7. Install Caveman for output compression. The open-source Caveman plugin strips verbose explanations from Claude Code responses, reducing output tokens by 40-60% while preserving code accuracy. See our 3 Claude Code repos guide for setup instructions.

8. Monitor with Code Burn. You can't optimize what you don't measure. Code Burn shows per-file, per-conversation token consumption. The visibility alone changes your behavior.

The Math That Changes Your Behavior

A typical Claude Pro subscription gives you roughly 45 Opus messages per 5-hour window. Without optimization, a complex coding session burns through this in 90 minutes. With these techniques, the same work takes 30-35 messages — leaving headroom for the rest of your day.

The difference between "I always hit rate limits" and "I rarely hit rate limits" isn't paying for a higher tier. It's workflow discipline.

The Counterintuitive Truth About Longer Prompts

A longer, more detailed prompt costs more tokens per message. But it costs fewer tokens per task because it reduces the number of back-and-forth messages. Our Prompt Optimizer makes prompts longer and more specific — and that's exactly why it saves you tokens overall. One 80-word prompt that works costs less than five 10-word prompts that don't.

The most expensive prompt you can write is a short, vague one that needs to be sent three times.

Disclosure: Some links in this article are affiliate links. We only recommend tools we've personally tested and use regularly. See our full disclosure policy.