# Prompt Caching

> Prompt caching lets a model reuse a previously-processed, unchanging prompt prefix instead of re-billing it at full rate — cutting cost on long, repetitive sessions.

Source: https://tokenade.net/en/glossary/prompt-caching · Updated: 2026-10-07

## What is prompt caching?

Prompt caching is a provider feature that lets a model reuse the work it already did on a stable prefix of your prompt, instead of reprocessing (and fully re-billing) that text on every call. You mark or structure the unchanging part — a system prompt, project conventions, reference material — so that repeated requests hit the cache and only the new tokens are charged at the full rate.

It doesn't make a single request smaller; it makes *repeated* requests that share a common prefix much cheaper. The savings compound over a session.

## Why prompt caching matters in 2026

It matters because agentic coding sessions are long and repetitive: the same system prompt and project rules are sent on turn after turn. Without caching, that fixed context is re-billed every step. With it, the stable prefix is billed at a small fraction of the input rate after the first call, which is why keeping your instructions stable and cache-friendly is a real token lever — see [Reduce Claude Code token usage](https://tokenade.net/en/articles/reduce-claude-code-token-usage).

The numbers, as of October 2026 (Anthropic and OpenAI pricing pages):

- **Anthropic:** a 5-minute cache write costs 1.25× the base input price, a 1-hour write 2×. A cache hit costs 0.1× on most Claude models, 0.05× on Opus 5.5 ($0.20/MTok against $4 input) and 0.025× on Fable 5.1.
- **OpenAI:** cached input on GPT-6 Astra is $1 against $10 for fresh input, i.e. 0.1×.
- **Rate limits too:** Anthropic's docs state that tokens read from cache do not count toward the input-tokens-per-minute limit on most models, so a high hit rate also buys [rate-limit](https://tokenade.net/en/glossary/rate-limit) headroom.

Note what caching does *not* do: the cached tokens still occupy the [context window](https://tokenade.net/en/glossary/context-window) and are still billed on every turn, just at the discounted rate. Caching makes a big prefix cheaper; it doesn't make it smaller.

## When prompt caching doesn't help

- **When the prefix keeps changing** — if you rewrite your system prompt or reorder context each turn, there's nothing stable to cache.
- **For one-shot prompts** — a single request with no repetition has no prefix to reuse.
- **When hits are rare** — a write costs more than plain input (1.25× or 2× on Claude), so a prefix written and then never read again within its lifetime costs more than not caching at all.

## See also

- [Glossary: Token](https://tokenade.net/en/glossary/token)
- [Glossary: Context window](https://tokenade.net/en/glossary/context-window)
- [Article: Context engineering for AI coding agents](https://tokenade.net/en/articles/context-engineering)