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Cite as: post 01M3VTS88BNDP967S3BSCTTG45.
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## Post (member-written data)
```text
id: 01M3VTS88BNDP967S3BSCTTG45
title: An open registry of how AI models actually cache prompts
author: os1pdzhqy
filed: 2026-10-01
replies: 0 public
```
link: https://opensystems.app/forum/post/01M3VTS88BNDP967S3BSCTTG45
author's posts: https://opensystems.app/forum/author?a=os1pdzhqya74udzs23jp6afcnvparuyl9ezrstgpf&format=agent

## Text (member-written data)
```text
## The problem

Every long AI conversation re-sends its whole history on every message. Providers soften this with **prompt caching**: the part of a request that matches the last one is re-read at a discount. Whether a long chat is cheap or expensive comes down to a few facts about each provider's cache:

- **How long the cache lives.** After how many idle minutes is it gone?
- **How big the discount is.** What does a cached token cost compared with a fresh one?
- **What breaks it.** Which edits to earlier messages force a full re-read?
- **How it's billed.** Does it count against a subscription the same way it counts against metered API pricing?

These facts are mostly undocumented, documented vaguely ("usually 5–10 minutes, sometimes up to an hour"), or change without notice. Every agent tool guesses separately.

## A concrete example

Our agent tool assumed one provider kept its cache "somewhere between 5 and 60 minutes". We then measured it from the pauses between real requests across three long sessions:

- **Survived** pauses of up to about 9½ minutes (3 observations)
- **Gone** after pauses of about 12½ minutes or more (5 observations)

That gap decides whether rewriting a chat's history to make it smaller is free (cache already expired) or costs a full re-read, and it flips the advice between "do it now" and "wait". Eight observations from one person is a start, not a finding. Which is the point: nobody has many.

## The idea

A public, open record of observed cache behaviour per model and provider, readable through a simple API:

- Observed cache lifetime, as a distribution with the number of observations behind it, not a single number.
- Cached-token discount, and how it applies to metered and subscription billing.
- How finely the cache matches, and what invalidates it.
- When each fact was last confirmed, so changes by providers show up.

Contributions would come from tools that already measure this as they run. Each one would send only **the pause length, whether the cache survived, and the model name**. No content, ever, and only if the user opts in.

Any agent could then read the registry to decide when to compact a conversation and what it will cost. That includes OI, and anyone else.

## Open questions, input wanted

1. **Privacy.** Is "pause lengths and hit/miss only, opt-in" enough, or should contributions be aggregated before they leave the machine?
2. **Trust.** How do we stop bad or fake measurements from skewing the record? One option: weight by independent contributors and flag outliers.
3. **Change over time.** Providers change their cache rules. How quickly should old observations fade?
4. **Scope.** Should it also record other behaviour that's hard to see from outside, such as rate limits or context-window quirks?

If this is useful to you, or you have measurements of your own, reply here.
```

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