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Designing an agent to reuse stable prompt prefixes can reduce repeated input-processing costs and latency—but no single line of code guarantees a 10× reduction in total agent costs. Prompt caching lets supported APIs reuse key-value (KV) state for an eligible, unchanged prefix; new input and generated output still have to be processed.
What KV-cache-friendly design means
When a model processes a prompt, it computes key-value tensors (KV state) for the tokens in context. For a later request with a matching prefix, a supported prompt cache can reuse the saved state rather than compute it again. OpenAI explains that its prompt cache stores KV tensors, not the tokens themselves, in its prompt caching documentation.
The reusable prefix can include rendered instructions, developer messages, tool definitions and earlier conversation content. Matching is prefix-based: if the beginning of the prompt changes, later identical material may no longer qualify as part of the same cached prefix. Keeping an agent session alive by itself does not ensure a cache hit.
A cache hit affects eligible repeated input, not the whole request. New user content and tool results still need processing, and the model still generates an output. Cache eligibility, lifetime, minimum prompt length, breakpoints and read/write pricing vary by provider, model and platform.
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How to structure an agent for reusable prefixes
- Identify what stays the same. Put stable global instructions, tool schemas and reference material in the candidate reusable prefix.
- Keep its beginning stable. Avoid placing per-call timestamps, request IDs or changing results near the start if those changes would break prefix matching. Preserve the same rendered content and ordering to the degree the provider requires.
- Put changing content later. Place request-specific text and changing tool results after the stable material, subject to the provider’s cache boundaries and API rules.
- Configure boundaries only when useful. Where explicit cache breakpoints are available, check the current model and platform requirements and test whether a breakpoint improves reuse. Anthropic documents cacheable tools, system instructions and messages, along with model- and platform-specific eligibility, minimum lengths, durations and pricing, in its prompt caching guide.
- Verify actual usage. Inspect the API’s usage fields or cache diagnostics to distinguish cache reads and writes from uncached input and output. A stable-looking prompt is not proof that the provider served a cache hit.
What savings have been reported—and what they do not prove
Provider-published figures show that caching can make a substantial difference in particular settings, but they measure different things and should not be combined into a general promise of 10× lower agent costs.
| Reported figure | What it describes | How to interpret it |
|---|---|---|
| Up to 95% | OpenAI’s documented discount ceiling for cached input on supported models. | This is a discount on eligible cached input, not a reduction of up to 95% in total agent cost. Check the current documentation for model availability and pricing. |
| 2.7 to 5.3 times | Anthropic-reported reduction in agent-loop cost across benchmarks in its cost guide. | This is a provider-measured benchmark result, not a guarantee for a different agent, model or workload. |
| 83%; 88% with input trimming | Anthropic’s example of a small triage agent, reporting the reduction from caching alone and then with input trimming added. | The result is specific to that example; it does not establish the savings for other agents. |
Anthropic’s benchmark context is in Optimizing for cost and intelligence. The figures above are not interchangeable: a cached-input discount, an agent-loop benchmark and a small-agent example use different scopes. The “10” in the proposed headline is not established by these sources as a universal total-cost result.
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How to measure whether caching helps your agent
Test with representative runs from the actual workload, not just a short prompt or a single successful request. Compare runs with caching enabled and disabled where the API allows a meaningful comparison, and track the inputs and outputs that drive your bill.
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- Uncached input tokens and generated output tokens.
- Total API cost across the full agent loop, including repeated model calls.
- Latency, including time to first token, across comparable runs.
- Cache hits and misses as prompts, tool schemas or conversation context change.
Account for provider-specific write and read prices, minimum eligible prompt length and cache lifetime when interpreting the results. A cache may help repeated long prefixes more than short or frequently changing prompts; measure the cost and latency together to determine the effect on your own workload.
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OpenAI and Anthropic caching are not interchangeable
Both providers document prompt caching, but their eligibility rules, cache controls, pricing and diagnostics differ. Anthropic describes cache-control breakpoints across tools, system instructions and messages; OpenAI documents its own prefix-matching behavior. Consult the documentation for the exact model and hosting platform you use rather than assuming that a prompt layout or pricing rule transfers between APIs.
A 2026 arXiv preprint evaluated caching strategies across more than 500 agent sessions and reports that cache-block placement can affect cost and time-to-first-token. Those findings are research results, not a guarantee for every production agent harness: the study.
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