# What is prompt caching?

Prompt caching reuses the computed state of a prompt prefix that repeats across requests, such as a system prompt or tool definitions, so those tokens are not reprocessed and bill at a deep discount. On the current catalog, cached input on GPT-5.5 lists at $0.50 per million tokens against $5 uncached.

Agents benefit most: the system prompt and tool schema are identical on every turn, so an agent with a stable 3,000-token preamble caches it across thousands of requests. GLM 5.2 prices cached input at $0.26 against $1.40 uncached; the pattern is similar wherever a cached rate is published.

Caching changes model rankings on real workloads. A mid-priced model with a published cache rate can beat a nominally cheaper one once your cache hit rate is high; run your own numbers with the hit-rate slider in the price comparison.

## See also

- [LLM price comparison](https://allocate.network/tools/llm-price-comparison)

## Related terms

- [Tokens](https://allocate.network/glossary/tokens.md)
- [AI agents](https://allocate.network/glossary/ai-agents.md)
- [Context caching](https://allocate.network/glossary/context-caching.md)

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[HTML page](https://allocate.network/glossary/prompt-caching) · [Machine-readable catalog](https://allocate.network/catalog.json)