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Prompt caching can sharply reduce the cost of repeated input to an LLM, but a 70–90% cut in your total bill is not automatic. Providers discount eligible cached input tokens; new input, cache writes, storage, and generated output can still cost money. The result depends on how much context repeats, how often it is reused before expiry, and which model and cache rules apply.
What prompt caching does
Prompt caching reuses computation for eligible context that appears again in later requests, usually a matching prompt prefix or context explicitly stored with a provider. OpenAI describes the mechanism this way: “Prompt caching reuses work when requests share the same prompt prefix.” The new, changing portion of each request still needs processing.
A cache hit is not the same as reusing an entire conversation for free. The provider may charge a discounted rate for cached input, a different rate to create the cache, and—in some configurations—a storage fee. Uncached input and generated output remain separately billable. Each provider and model sets its own eligibility, matching, expiry, and usage-reporting rules.
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How much can prompt caching save?
The oft-quoted 70–90% range should be treated as a possible result for a suitable workload, not a general promise about an API bill. The sources cited here do not establish an independent, provider-neutral 70–90% reduction in total LLM spend. A high discount on cached input can produce a much smaller reduction once you include new input, output, cache writes, storage, misses, and expired entries.
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Calculate the baseline and cached cost over the same set of requests. For the baseline, assume the reusable context is sent and billed as ordinary input every time. For the cached case, count each token at the rate that actually applies to it:
- Baseline input cost: total input tokens × ordinary input-token rate.
- Cached-workload input cost: cache-write tokens × write rate + cache-read tokens × read rate + uncached tokens × ordinary rate + applicable storage charges.
- Total request cost: input cost + output-token charges + any other applicable charges.
- Savings percentage: (baseline total request cost − cached total request cost) ÷ baseline total request cost × 100.
Use the same model and output workload on both sides of the comparison. If output charges are unchanged, caching reduces the input portion but does not remove output charges.
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Worked example: a 71.4% reduction in input cost
This calculation uses Anthropic’s published multipliers for a 5-minute cache, as shown in its pricing documentation accessed October 7, 2026: a cache write costs 1.25× the base input rate and a cache read costs 0.1×. It is a model-independent calculation in units of the base input rate, not a quoted dollar price or a measured result. Check the live price for the model and context tier you use.
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Assume ten calls, each containing a 10,000-token stable prefix and 1,000 tokens of new input. The first call writes the prefix to the cache; the next nine calls read it. Assume those reads occur within the 5-minute cache duration, all are eligible hits, there are no storage charges, and all ten calls produce the same output workload in both scenarios.
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- Without caching: 10 × (10,000 + 1,000) = 110,000 input tokens at the base input rate.
- With caching: first write = 10,000 × 1.25 = 12,500 rate-equivalent tokens; nine reads = 9 × 10,000 × 0.1 = 9,000; new input = 10 × 1,000 = 10,000. Total = 31,500 rate-equivalent tokens.
- Input-cost reduction: (110,000 − 31,500) ÷ 110,000 = 71.4%.
The 71.4% is an input-cost result under those assumptions. Output charges are unchanged, so the reduction in total request spend would be smaller. For example, if output charges happened to equal the uncached input cost in this workload, total savings would be 39.3%: the 78,500 units saved on input divided by 200,000 units of baseline input-plus-output cost. That output-cost assumption is only an illustration, not a provider price.
Why real workloads save less—or sometimes nothing
- Few repeats: an expensive write followed by only one or two reads may not repay its premium.
- Short-lived reuse: requests arriving after expiry may need to write the context again.
- Changing prefixes: edits to the reusable portion can prevent a match under the provider’s rules.
- Short prompts: a request may fall below a model’s minimum eligible length.
- Low input share: output-heavy workloads have less total spend available for input caching to reduce.
- Misses or storage charges: cache misses and any applicable storage costs reduce net savings.
How provider caching rules differ
Do not assume that a cache duration, discount, minimum prompt length, or usage field applies across providers—or even across every model from one provider. The following comparison reflects the cited documentation; “not stated” means the linked source material cited here does not establish a value for that cell.
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| Provider | How caching works | Eligibility and pricing details established here | Retention, storage, and verification |
|---|---|---|---|
| OpenAI | Automatic caching for supported models reuses matching prompt prefixes; the guide also describes cache breakpoints and retention options. | The current guide states a 1,024-token minimum for GPT-5.6 and later. Other details depend on the model and request settings. Current cache-write and cache-read dollar rates are model-dependent; consult the OpenAI prompt caching guide. | The guide describes retention options and usage diagnostics; exact options depend on the model and settings. See the guide for current controls and usage fields. The Prompt Caching 101 cookbook example explains that a miss processes the full prompt and may cache an eligible prefix for a later request. |
| Anthropic | Explicit cache control with 5-minute and 1-hour durations, according to its pricing documentation. | The documentation lists 5-minute writes at 1.25× base input pricing, 1-hour writes at 2×, and reads at 0.1×. Dollar prices depend on model and applicable context tier. These multipliers are from the Anthropic pricing page, accessed October 7, 2026. | The cited pricing details establish the two durations; storage charges and verification fields are not stated here. Check the live pricing page and model documentation before estimating costs. |
| Google Gemini | Supports implicit caching for eligible models and explicit context caching. | Eligibility, token minimums, reductions, and storage costs depend on model and configuration; specific values are not stated here. See Gemini context caching and API optimization. | Explicit caching may incur storage charges for the configured duration. Do not assume implicit caching guarantees a hit or a saving; check the model documentation for the applicable controls and usage details. |
OpenAI’s October 2024 announcement includes historical pricing examples, not current prices. Use the live model documentation rather than carrying those old dollar figures into a present-day estimate: OpenAI’s historical prompt-caching announcement.
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- Identify what stays the same. Separate durable instructions, schemas, tool definitions, and other stable context from the details that change with each request.
- Put reusable material early. Where the provider’s rules allow, keep the stable content together at the beginning and put request-specific material later.
- Keep the reusable portion consistent. Avoid unnecessary edits to its content or ordering. Small differences can matter under a provider’s cache-matching rules.
- Choose the right cache mechanism. Use automatic caching where it fits; consider explicit breakpoints or stored context only if the provider supports them and the expected reuse justifies the added controls or storage cost.
- Check model-specific requirements. Confirm supported inputs, minimum eligible length, cache pricing, expiry, and applicable request settings in the current documentation for the exact model.
Repeatedly sending a request or keeping a conversation open does not by itself prove that a provider registered a cache hit.
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How to verify savings before changing your budget
- Capture a baseline. Record input and output token usage, total charges, and latency for representative requests before enabling or restructuring caching.
- Run comparable requests. Use the same model and similar inputs and outputs, while allowing enough repeated calls to include normal cache writes, reads, misses, and expiry.
- Inspect the provider’s usage data. Track cache-write tokens, cache-read tokens, uncached input tokens, and output tokens where the provider exposes them. Use the provider’s current usage fields or dashboard, not request count alone.
- Recalculate total cost. Apply the rates for each billing class and include storage where applicable. Compare total spend for the same workload, not just the discounted cached-token rate.
- Check latency separately. Record it if responsiveness matters, but do not infer a latency benefit from a cost discount; measure it on the workload you intend to run.
Price pages and cache behavior can change. Recheck the provider’s current documentation when changing models, request settings, or cost forecasts.
Quick Recap
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