OpenAI is making GPT-6 prompt caching more reliable and controllable, adding higher hit rates, diagnostics, breakpoints, prewarming, and cache-preserving reasoning changes.

Long-running agents often resend instructions, tool definitions, and earlier context, so caching avoids recomputing those prefixes across successive API calls.

That reuse can cut cached-input token costs by up to 90%, while a new dashboard exposes cache-hit rates and cached versus uncached token volume.

ofcourse, a high cache hit rate is still remains partly an application-design problem. because GPT-6’s improved caching alone doesn’t guarantee good economics; developers still need to structure long-running agents so stable instructions and tools remain reusable.