Prompt caching can lower the cost of repeated input, but only when a request reuses a sufficiently long matching prefix while its cached state is still available. Cache writes may cost more than ordinary input; savings come from subsequent reads, not from a blanket discount on every token.
What prompt caching reuses
A language model normally processes the prompt it receives. With prompt caching, a provider can save intermediate key-value (KV) states for an eligible prompt prefix and reuse them when a later request begins with the same content. OpenAI describes its cache this way: “The prompt cache stores key-value (KV) tensors, not the tokens themselves.” The model still processes new input and generates a new answer; caching is not a stored response.
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The distinction matters for cost: caching applies to repeated input processing, not to output tokens. A request can contain cached prefix tokens, uncached new input, and generated output, each with different billing implications.
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When a request gets a cache hit
A configured cache does not guarantee a hit. The provider and model determine how long the prefix must be, which boundaries are eligible, how matching works, and how long cached state remains available. A small change near the beginning can prevent reuse of the later content that otherwise would have matched.
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- Length: The reusable prefix must meet the model’s minimum cacheable length.
- Matching: Requests need a sufficiently identical prefix at an eligible boundary. Keep shared content in the same order and avoid putting changing material before it.
- Availability: A cached state may expire or be evicted. Retention depends on provider and cache mode.
- Reporting: Confirm a hit in usage data rather than assuming it from a cache setting.
How the providers differ
The thresholds and multipliers below are provider-published rules in documentation accessed in 2026, not independent benchmark results. Model lists, prices, and behavior can change, so check the current documentation and pricing for the exact API model you use.
| Provider and mode | Minimum or eligibility | Write and read pricing | Retention and reporting |
|---|---|---|---|
| OpenAI API: implicit and explicit caching for GPT-5.6 and later | At least 1,024 visible tokens for GPT-5.6 and later, according to the current guide. Explicit mode uses selected breakpoints. | Writes cost 1.25× standard uncached input price; reads cost 0.1× on most models or 0.05× for GPT-6.1 Sol. | The guide documents up to four cache writes per request. Track cached tokens and cache-write tokens in usage details; retention and minimums vary for earlier models and request types. |
| Anthropic Claude API: automatic and explicit caching | Supported on active Claude models; thresholds and model details should be checked in the current API documentation. | Five-minute writes cost 1.25× base input price; one-hour writes cost 2×; reads cost 0.1×. Dollar prices differ by Claude model. | The cited pricing page specifies five-minute and one-hour write options. Usage exposes cache-creation and cache-read token counts. |
| Google Gemini API: implicit caching and explicit cached-content API | Implicit caching is enabled by default for Gemini 2.5 and newer. The guide lists a 2,048-token minimum for Gemini 2.5 Flash and Pro, and 4,096 for the listed Gemini 3.5/3.6/3.7/3.8 Flash and 3.1 Pro Preview models. | Check current pricing for the specific model; the caching guide does not establish a universal savings multiplier. | The explicit API creates a cache resource with a TTL and model binding. Cached tokens appear in usage.total_cached_tokens. The guide recommends sending similar prefixes close together. |
Sources: OpenAI prompt caching guide; Anthropic prompt caching documentation; Google Gemini API caching guide, last updated September 2, 2026 UTC.
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What the price multipliers mean
For GPT-5.6-and-later OpenAI models, the provider’s documented 1.25× write and 0.1× read rates make the break-even logic visible. Assuming the same full prefix is written once and then fully read, the provider’s example is 1.35× ordinary input cost for one write plus one read, compared with 2× for two uncached passes. With one write and nine full reads, the example is 2.15× rather than 10× for ten uncached passes. These are provider illustrations at the 0.1× read rate, not guaranteed savings for a workload; the GPT-6.1 Sol read rate is different, and uncached suffixes and partial matches affect real totals.
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Structure prompts to reuse the stable part
Put content that stays the same at the beginning and content that changes toward the end. This is a design pattern, not a guarantee: exact cache behavior depends on the provider and model.
- Start with shared instructions. Keep repeated system and developer instructions consistent across requests.
- Place tool definitions and shared reference material next. Reuse the same ordering and wording for tool schemas and common documents where possible.
- Put changing content later. Add the current user request, turn-specific details, and changing tool results after the shared prefix.
- For explicit caching, use the provider’s breakpoint controls. OpenAI’s explicit mode allows content after the last selected breakpoint to remain uncached; use breakpoints only where the prefix is genuinely reusable.
- Check usage after representative requests. Confirm that cached or cache-read tokens appear and compare the resulting bill and latency with an uncached baseline.
Calculate realized savings for your workload
Use the exact model’s current input price and separate the reusable prefix from the changing suffix. For each request, compare the ordinary cost of all input tokens with the actual cache-write and cache-read charges plus any uncached input charges. Keep output-token charges separate, because caching the input does not discount the generated answer.
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A practical accounting view is:
- Cache creation: write-priced tokens × the model’s cache-write rate.
- Later hits: read-priced tokens × the model’s cache-read rate.
- Uncached input: suffix and other input tokens × ordinary input rate.
- Output: generated tokens × the model’s output rate.
Then compare the total against processing the same input uncached over the same requests. A workload with few repeats may not recover the higher write cost; frequent reuse of a long stable prefix can make the read discount more consequential. Latency is also worth tracking, but a cache hit should not be treated as proof of a particular latency improvement.
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Inspect the usage record for the model and API you actually call. OpenAI’s guide recommends tracking cached tokens, cache-write tokens, input tokens, latency, and realized cost; the relevant usage details include usage.input_tokens_details.cached_tokens and cache_write_tokens. Anthropic reports cache-creation and cache-read token counts. Gemini reports cached tokens in usage.total_cached_tokens. Field names and availability can evolve, so use the current provider documentation when instrumenting a client.
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If usage shows no cache reads, first check whether requests share an identical prefix, meet the model’s minimum, and occur within the cache’s availability window. If the request has a hit but savings are disappointing, compare actual read-priced tokens against write-priced tokens and uncached suffixes, then include output separately. A cache setting alone is not evidence of a lower bill.
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