What reused prompt tokens mean
They indicate that OpenAI reported previously reusable prompt content on the provider side. This is not the same as a full local cache hit where the request never needs to go upstream again.
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OpenAI proof
How to see real reused prompt tokens in OpenAI.
Reused prompt tokens are only meaningful when they come from real provider-reported behavior. AI Optimizer keeps that signal visible without blurring it together with exact local cache hits.
Quick answer
The honest way to see reused prompt tokens in OpenAI is to surface them only when OpenAI reports real reuse. That signal should stay separate from exact local cache hits, because those are different kinds of wins.
Why people misread the metric
Many dashboards blur local caching and provider reuse together. That makes the numbers look better, but it also makes them less useful for decision-making.
Why the distinction matters
Exact cache hits and reused prompt tokens tell you different things about your workflow.
Exact Cache Hits
Fully served from the local cache. Strongest direct proof of repeat-workflow savings.
Partial Hits (OpenAI)
Only counted when OpenAI reports real reused prompt tokens.
Tokens Reused (OpenAI)
Provider-reported reuse that helps explain partial savings without pretending they are full cache hits.
A clean reporting split: exact local hits remain separate from provider-reported prompt reuse.
What honest reporting looks like
If OpenAI does not report reused prompt tokens, AI Optimizer does not invent them. That keeps the proof believable and makes it easier to understand what is actually happening in production.
Where this helps most
This is especially useful for teams comparing workflows, evaluating provider behavior, or trying to explain savings without inflated marketing claims.
Does every repeated OpenAI request show reused prompt tokens?
No. That depends on provider-side behavior and what OpenAI actually reports back.
Are reused prompt tokens the same as a cache hit?
No. A cache hit is local. Reused prompt tokens are provider-reported reuse.
Why keep them separate?
Because combining them hides what is truly local optimization versus what the provider reported upstream.
Show the real signal, not the inflated one.
Use AI Optimizer to keep exact local hits and provider-reported OpenAI reuse visible, separate, and easier to trust.