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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesA cache write does not guarantee a later cache hit. Check the response’s usage fields: cache_creation_input_tokens shows tokens written, while cache_read_input_tokens shows tokens read from cache. If reads stay at zero, check eligibility, whether the prompt prefix through the breakpoint is unchanged, the time between requests, and whether your API platform supports the caching mode you chose.
What a cache write—and a missing read—means
Prompt caching works on an eligible prompt prefix up to a cache breakpoint. A nonzero cache_creation_input_tokens means tokens were written to a cache entry; it does not show that a later request reused that entry. For reuse, look for a nonzero cache_read_input_tokens in a subsequent response.
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Anthropic advises checking both fields: if both are zero, the prompt was not cached, possibly because it did not meet the minimum-length requirement. A cache_control marker does not override that requirement. See Anthropic’s prompt-caching documentation.
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Check these causes in order
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Confirm caching is supported for your model and platform
Minimum prompt lengths and caching modes vary by model and platform. Verify support for the exact model and API route you are using, and confirm that the cacheable prefix meets that model’s minimum. An ineligible prompt may be processed without caching or an error.
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On Amazon Bedrock, Anthropic says automatic caching through the top-level
cache_controlfield is unsupported. Use explicit breakpoints instead, as described in the Claude on Amazon Bedrock documentation. -
Compare the prefix through the breakpoint
A later request must preserve the relevant prefix through the cache breakpoint. Changes to earlier content or its ordering can prevent the expected prefix from being reused. Keep stable, reusable content before the breakpoint and, where your request structure allows, put changing per-request content after it.
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Check the elapsed time against the cache lifetime
The default ephemeral cache lifetime is five minutes. If the next request arrives after expiration, a new cache creation rather than a cache read may be expected. Anthropic also documents a one-hour option; verify that the exact model and platform support it before relying on it.
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Read the response usage for each request
Record the model, platform, timestamp, cache mode, TTL, and response
usagefor the initial call and each follow-up. Comparecache_creation_input_tokenswithcache_read_input_tokensacross calls to distinguish a write from reuse.Rank #3
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Understand the cost of repeated writes
Anthropic’s pricing page lists five-minute cache writes at 1.25× the base input-token price, one-hour cache writes at 2×, and cache reads at 0.1×. These are pricing multipliers, not fixed prices; actual spend depends on the model, token counts, and current pricing. Compare the current per-model rates with your measured usage before attributing a bill increase to caching. See Anthropic’s pricing page.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use the usage fields to narrow the diagnosis
| Observed usage | What it indicates | Next check |
|---|---|---|
cache_creation_input_tokens is nonzero; later cache_read_input_tokens is zero |
Tokens were written, but the later request did not report a cache read. | Compare prefix and ordering through the breakpoint, elapsed time, and platform/mode support. |
| Both fields are zero | The request was not cached; Anthropic notes that a prompt below the minimum length is one likely reason. | Check the exact model’s minimum and whether the chosen mode is supported on the platform. |
| Cache creation recurs after a delay | The previous entry may have expired; the default ephemeral lifetime is five minutes. | Compare the interval with the configured TTL and verify any one-hour option’s support. |
These checks describe documented causes, not a diagnosis of a particular request. Establishing the root cause for your case requires the request payloads, model, platform, timing, and usage records.
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