How to Reduce LLM Token Costs (2026 Playbook)

Most teams overspend 60–80% on LLM tokens. Here's where the money leaks and how to plug it.

1. Slim every prompt

Verbose system prompts run on every call. Shortening filler ("in order to" → "to") and removing redundancy cuts tokens with zero quality loss. The token optimizer measures the exact savings.

2. Route to the cheapest capable model

The same prompt can cost 100×+ more on a frontier model than a small one. Don't use Opus for a classification task.

3. Cache repeated calls

Exact + semantic caching can eliminate 50–90% of repeated calls.

4. Measure before you send

Count tokens and price the call in your editor before it runs. Install the MCP server so Claude/Cursor can do this automatically.

Try the free calculator →

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