If your app sends the same long system prompt, tool definitions or document with every request, you are paying to process those tokens again and again. Prompt caching fixes that: the provider stores the processed prefix of your prompt and charges a steep discount when you reuse it, while also cutting latency. In 2026 all three major APIs support it, but they behave differently. This guide compares OpenAI, Claude and Gemini so you can pick the right setup and avoid the common mistakes.
What Is Prompt Caching and Why It Matters
LLMs must process every input token before generating output. When the beginning of a prompt is identical across calls, the provider can reuse the internal state computed for that prefix instead of recomputing it. You get two benefits: lower cost on cached input tokens and faster time to first token. The savings are largest for long prompts, which is why prompt caching is a staple of RAG apps, coding agents and chatbots with large instructions.
Note that prompt caching is different from response caching. The model still generates a fresh answer every time; only the input processing is reused. If you want to skip the model call entirely for repeated questions, look at a semantic cache in your AI gateway instead.
Prompt Caching Compared: OpenAI vs Claude vs Gemini
OpenAI: automatic caching
OpenAI caches automatically on supported models once the prompt passes a minimum length (1,024 tokens at the time of writing). There is nothing to enable and no extra fee for cache writes. Cached input tokens are billed at a discount that varies by model. You can improve hit rates by keeping static content first and optionally passing a prompt_cache_key so related requests land on the same cache.
Claude: explicit breakpoints
Anthropic gives you control through cache_control markers on content blocks. You decide exactly what is cached. Cache reads cost a small fraction of the base input price, while writes cost somewhat more than normal input. The default lifetime is about five minutes and refreshes on each hit, with a longer one-hour option available at a higher write price. Explicit control makes costs predictable but requires a code change.
Gemini: implicit and explicit
Google offers both modes. Implicit caching is automatic on recent Gemini models and passes savings through when a prefix repeats. Explicit caching lets you create a cache object with a chosen time to live, which is billed for storage while it exists. This suits very large, long-lived contexts such as a big document or codebase queried many times.
| Provider | How it works | Write fee | Best for |
|---|---|---|---|
| OpenAI | Automatic prefix matching | None | Zero-effort savings |
| Claude | Explicit cache_control breakpoints | Yes, above base price | Predictable agent and RAG workloads |
| Gemini | Implicit plus explicit with TTL | Storage fee on explicit | Huge reusable contexts |
Pricing and minimum lengths change often, so verify current numbers on each provider’s pricing page before modelling costs.
How to Structure Prompts for Better Cache Hits
Caches match on an exact prefix, so ordering is everything. A single changed character early in the prompt invalidates everything after it. Follow these rules:
- Static first, dynamic last: put system instructions, tool schemas and reference documents at the top, and user input at the end.
- Never inject timestamps or request IDs into the prefix.
- Keep tool definitions stable: reordering or editing tools breaks the cache.
- Append, do not rewrite, conversation history so earlier turns remain a valid prefix.
- Warm the cache with one request before launching parallel calls.
Measuring Savings
Every provider reports cached token counts in the response usage object. Log cached versus uncached input tokens per request, then track your cache hit rate over time. A healthy RAG or agent workload often reaches high hit rates; if yours is low, check for prefix drift. Combine prompt caching with batch processing for compounding discounts, as covered in our LLM batch API comparison.
Common Prompt Caching Mistakes
The most frequent problem is silent cache misses. Teams enable caching, see no savings and assume the feature is broken, when the real cause is a prefix that changes on every call. Dynamic values such as the current date, a user name or a random session ID placed near the top of the system prompt will invalidate the cache each time. Move those values to the end of the prompt, or into the user message.
A second mistake is caching content that is too small or too short-lived. If your traffic is sparse and the cache expires between requests, you pay write costs without enjoying read discounts. In that case, either batch requests closer together, use a longer lifetime where the provider offers one, or skip caching for that endpoint.
Finally, remember that cached prompts are still subject to your data policies. Review how each provider handles retention of cached content, especially if your prompts include customer data, and consider redacting sensitive fields before they enter the prefix.
Which Provider Should You Choose?
If you want savings with zero code changes, OpenAI’s automatic approach is hard to beat. If you run agents with large, carefully managed prompts and want predictable billing, Claude’s explicit breakpoints give you the most control. If you routinely query one very large document or codebase for hours, Gemini’s explicit caches with a custom lifetime can be the cheapest option. Many teams run more than one provider behind a gateway, so it is worth designing prompts with a stable prefix regardless of vendor.
Prompt Caching FAQ
Does prompt caching change the model output?
No. It only reuses computation for the input prefix, so answers are equivalent to an uncached call.
How long does a cache last?
It depends on the provider. Short-lived caches last minutes and refresh on use, Claude offers a longer one-hour option, and Gemini explicit caches last for the TTL you set.
Is prompt caching worth it for short prompts?
Usually not. Providers require a minimum prefix length, and small prompts save little. It pays off when the repeated prefix is thousands of tokens.
Can I combine prompt caching with the Batch API?
In many cases yes, and the discounts can stack, but check each provider’s documentation for current rules.
Conclusion
Prompt caching is the easiest cost and latency win available to LLM developers. Choose OpenAI for automatic savings, Claude when you want explicit control, and Gemini for very large reusable contexts. Whichever you pick, put stable content first and measure your hit rate.
Next step: audit your most expensive endpoint today, move static content to the front of the prompt and enable caching. Then read the official docs for OpenAI and Claude to fine-tune the details.
