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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsIf OpenAI requests that appear to share a long prompt are not reusing cached work, compare the requests’ fully rendered prefixes and cache-relevant settings before assuming there is an application bug. Use Prompt Cache Diagnostics to inspect individual requests, then verify the result with cached-token usage and actual costs.
Why is my OpenAI prompt cache not hitting?
Prompt caching reuses an unchanged prefix of input; two prompts that are merely similar are not enough. The content at the start of the token-bearing request must match, and requests also need compatible settings, including model, service tier, and tools. OpenAI’s Prompt Caching guide and diagnostics guide describe these conditions.
Compare two actual requests expected to reuse context. Start at the beginning and compare all rendered content that contributes tokens: system and developer instructions, tool definitions, conversation history, and any other content before the portion you expect to reuse. A small request-specific value inserted early can change the prefix and leave later otherwise-identical content outside the matching portion. That is a practical inference from the prefix-matching rule, not a diagnosis of any particular application.
- Confirm the model and service tier are compatible.
- Compare the tool definitions and their ordering as rendered in each request.
- Diff the token-bearing content from the first position, not just the user message or a prompt template.
- Check whether a dynamic value appears before the stable context you want reused.
How do I find prompt prefix drift?
Inspect individual requests with diagnostics
Run representative requests through OpenAI’s Prompt Cache Diagnostics. Its request-level detail helps establish whether the prefixes and relevant settings match and whether a request hit a cached prefix. Use a pair that your application expects to reuse, including one request that reports unexpectedly low or zero cached tokens.
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Use the dashboard for trends, not root-cause analysis
The Prompt Caching Dashboard is useful for application-level cache-read hit-rate patterns over time. It does not explain every individual miss; use request diagnostics to investigate a specific pair of requests.
Reorder stable and changing content carefully
If a changing value breaks a useful shared prefix, consider placing stable instructions and tool schemas earlier and volatile, user-specific content later. This is an implementation strategy inferred from the prefix rule, not a guarantee of cache reuse: compatibility, eligibility, cache availability, and request behavior still affect the result. Preserve the meaning and safety of the application’s instructions when changing their order.
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How can I see cached tokens in the OpenAI API?
For Responses API requests, inspect usage.input_tokens_details.cached_tokens. Also record total input tokens, cache-write tokens where exposed, latency, and realized cost for the same requests. The Usage API reference separately defines input_cached_tokens for aggregated text-input usage.
Calculate an aggregate hit rate from cached tokens and input tokens gathered over the same request set and time period. Do not treat a hit indicator as proof that the entire input was cached: a request can reuse only its matching prefix and process the rest as new input.
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Why did my cached token count drop?
Check for changes that shorten or invalidate the reusable beginning of the request, then compare model and request settings. A change to early instructions, tool schemas, conversation history, model, or service tier may mean less of the expected prefix is shared. A count can also look lower if you compare different request populations or periods, so calculate rates using aligned totals rather than comparing cached-token counts alone.
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Eligibility rules also differ by model generation. OpenAI documents a minimum of 1,024 visible input tokens for GPT-5.6 and later; hidden OpenAI-provided system tokens do not count toward that minimum. For earlier models, the documented minimum varies with request settings. The guide also describes generation-specific breakpoint behavior and cached-token reporting, so check the rule for the exact model rather than assuming one threshold applies universally.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I tell whether caching is actually saving money?
Use the current OpenAI API pricing page for the exact model’s uncached-input, cached-input, and cache-write rates. Rates and cache-write treatment differ by model and can change; there is no single savings percentage that applies to every request or model.
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Estimate realized cost from your own usage records: include cached input, uncached input, and cache writes where applicable, using the rates in force for the model and period being evaluated. Compare requests with similar workloads and include latency if it matters to your application. A cache hit alone does not establish a particular cost reduction, and the documentation does not establish a general real-world savings figure.
What should I check before enabling extended prompt caching?
Review the endpoint-specific retention details in OpenAI’s data-controls documentation. OpenAI says extended prompt caching stores key/value tensors as application state and that the endpoint uses described there are not eligible for Zero Data Retention. Confirm the relevant endpoint’s retention terms and your organization and project controls before enabling it.
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