# OpenAI prompt caching not saving money: the 1024-token minimum and prefix rules
## The symptom
`usage.prompt_tokens_details.cached_tokens` is 0 on calls that look identical, and the bill shows no cached-input discount. Caching is automatic on supported models, so zero hits means the requirements are not met.
## Confirm the cause
Check the documented requirements against your requests:
1. **Minimum cacheable length.** 1,024 tokens for GPT-5.6 and later; varies by model and request settings for earlier models. Shorter prompts never cache. Hidden system tokens do not count toward the minimum.
2. **Exact prefix match.** The cache matches the longest previously computed prefix. Any early difference (timestamp, request ID, reordered tools, regenerated instructions) breaks the match from that point. Static content first and byte-identical; volatile data last.
3. **What counts.** Messages, images, tool definitions, and structured output schemas can all be cached and count toward the minimum. Changing the tool list between calls resets the match.
4. **Reporting granularity.** Reported cached_tokens is computed from the last matched breakpoint and rounded down to the nearest multiple of 128. Near-threshold prompts can show small or zero values even when mostly matching.
## The fix
- Stable prefix first: instructions, examples, tool definitions, images byte-stable; user data, timestamps, and retrieval results last.
- Get the shared prefix over the minimum. Six hundred identical tokens never cache no matter how stable they are.
- Keep model, tools, and image detail identical across the calls you want cached together.
## Verify the fix
Log cached_tokens per request and compute the hit ratio over a day. Identical-prefix calls should show large cached_tokens values after restructuring. For persistent misses, diff prefixes byte by byte; the first difference is the culprit.