Better prompt caching for GPT-6
The applications behind these agents make a series of API requests that build on one another, often carrying forward the same instructions, tool definitions, and context from earlier turns. OpenAI caches that shared context to reuse computation across requests, reducing response times and giving developers discounts of up to 90% on cached input tokens.With the GPT‑6 family, we launched an improved prompt caching system that delivers higher cache hit rates by default. We now give cache discounts for eligible shared prefixes reused within a 30-minute window.
- ▪The applications behind these agents make a series of API requests that build on one another, often carrying forward the same instructions, tool definitions, and context from earlier turns.
- ▪OpenAI caches that shared context to reuse computation across requests, reducing response times and giving developers discounts of up to 90% on cached input tokens.With the GPT‑6 family, we launched an improved prompt caching system that de
- ▪We now give cache discounts for eligible shared prefixes reused within a 30-minute window.
OpenAI Blog files mainly under ai. We currently carry 21 of its stories.
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | OpenAI Blog |
| Canonical URL | https://openai.com/index/better-prompt-caching-for-gpt-6 |
| Publication time | Tue, 22 Sep 2026 21:00:00 GMT |
| Retrieval time | 2026-09-22T20:18:57.405Z |
| Last seen | 2026-09-22T20:18:57.405Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | eWNvuWgO2a-- · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
September 22, 2026ProductBetter prompt caching for GPT‑6Higher cache hit rates and new tools to help persistent agents run faster and cost less.Loading…ShareMonitor caching and diagnose cache missesMonitor caching and diagnose cache missesOptimize caching for your applicationGet startedMonitor caching and diagnose cache missesOptimize caching for your applicationGet startedGPT‑6 enables persistent agents to work for hours on complex tasks, from refactoring codebases to producing well-researched documents and presentations. The applications behind these agents make a series of API requests that build on one another, often carrying forward the same instructions, tool definitions, and context from earlier turns.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at OpenAI Blog.
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