---
title: What is prompt caching?
url: https://stowly.dev/learn/what-is-prompt-caching
description: Cache reads up to 90% cheaper, write premiums, TTLs, and when caching pays off.
docs_index: https://stowly.dev/llms.txt
version: v0.1
lastUpdated: 2026-09-19
---

> Fetch the complete documentation index at https://stowly.dev/llms.txt before exploring further.
The first time you send a long prompt, the model reads every token at full price. The second time, it shouldn't have to. **Prompt caching** lets providers remember chunks of your prompt and bill them at a steep discount on the next request.

## What gets cached

Prefixes, not random spots. A cache stores the early part of your request — the big system prompt, pasted documents, tool definitions — anything that stays identical between calls. Change a character too early and the cached path breaks: everything after that point is re-read at full price.

## How the pricing works

Cached **reads** are cheap; the first request pays a **write** premium:

- **Cache read:** up to 90% cheaper than base input on Claude models. OpenAI discounts cached input by 50–90% depending on the model.
- **Cache write:** costs a little more than plain input (1.25× on Claude) — the provider stores the tokens for you.
- **TTL:** caches are short-lived, minutes not days. An active agent session keeps hitting them; a request hours later starts cold.

## When it pays off

Caching rewards **repetition**: chat with a long fixed system prompt, agents calling the same tools all day, RAG pipelines reusing the same document library. One-off one-line prompts get nothing — there is nothing to reuse.

Stowly's calculator prices cache tiers in — [open it](/ai-token-cost-calculator), pick a model, and use the cache controls to see the gap between a cold and a warm prompt. Every rate is cited on [Sources](/sources), and the exact math is in [Methodology](/methodology).