A changing timestamp inside Claude’s cached prompt prefix can make every request look new to the cache. Anthropic’s documentation says cache matching uses prompt content up to a cache breakpoint; if that content changes, Claude writes a fresh cache entry instead of reading the previous one. Move the breakpoint so it follows stable, reusable context and comes before timestamps or other request-specific content.
Why a timestamp can leave Claude’s cache hit rate at 0%
Claude’s prompt cache matches content through a cache breakpoint. Anthropic describes the cache key as a cryptographic hash of the prompt up to that point. If a timestamp is inside that prefix, it changes the hashed content from request to request, so the new request no longer matches the earlier entry.
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Anthropic explicitly warns that placing a breakpoint on a block that changes every request—such as a timestamp or arbitrary user input—writes a fresh entry each time and never hits. The mechanism is documented, but the documentation does not independently verify any particular user’s reported 0% hit rate or identify a specific incident. Anthropic’s prompt-caching documentation
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How to place the breakpoint so stable context can be reused
Keep the repeated portion of the request together before the cache boundary. Put values that vary between calls after it.
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- Before the breakpoint: reusable instructions, tool definitions, and other shared context that remains unchanged across requests.
- After the breakpoint: timestamps, user-specific input, and other changing content.
Anthropic supports automatic caching, which adds a top-level cache_control and moves the breakpoint to the last cacheable block as a conversation grows. Explicit breakpoints are available when you need finer control. The minimum cacheable prompt length depends on the platform and model, so check the documentation for the one you are using before diagnosing short prompts as a cache failure. Prompt caching options and requirements
How to confirm the cache is being read
Compare the API response usage fields across repeated requests with the same stable prefix. A nonzero cache_read_input_tokens value on a later request is evidence that Claude reused cached input. Do not treat lower latency by itself as proof of a cache hit.
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cache_read_input_tokens: input tokens read from cache.cache_creation_input_tokens: input tokens written to cache.input_tokens: input tokens after the last cache breakpoint.
For total input processed, add cache_read_input_tokens, cache_creation_input_tokens, and input_tokens. Anthropic documents these usage fields and accounting in its Claude API prompt-caching guide.
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Anthropic’s pricing documentation, accessed in 2026, lists these cache pricing multipliers relative to base input-token pricing. Dollar costs vary by model; check the pricing page for the model you use because prices can change. Anthropic pricing
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| Cache operation | Documented multiplier |
|---|---|
| 5-minute cache write | 1.25× base input-token price |
| 1-hour cache write | 2× base input-token price |
| Cache read | 0.1× base input-token price |
These are multipliers, not fixed dollar amounts. Whether a write or read is economical depends on how often the stable prefix is reused and on the applicable model pricing.
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