Cursor improves token efficiency for long agent runs, cutting user costs by 7%

Cursor cut token costs for long agent runs by 7% through four engineering changes, with no quality regression. They trimmed the system prompt by ~66% as models now need less hand-holding; offloaded 60% of built-in tool definitions from static context to dynamic loading (similar to the 46.9% token reduction they previously achieved for MCP tools); compressed file reads; and used subagents strategically. The post doesn't disclose the absolute dollar or token amounts behind the 7% figure, nor the specifics of the compression and subagent implementations. The savings come from production A/B tests, so your mileage will vary by model and task length.