Content Quality: Compact, well-structured Briefing (449 words by the script, range 300-800) with Overview, What We Know and What We Don't Know. Facts are separated from limitations; no editorializing.
Source Verification: Both sources are OpenAI first-party documentation on developers.openai.com (on config/source_allowlist.txt, line 215, documented there as OpenAI's official developer documentation site). Snapshots read via gunzip (source-0.html.gz changelog, HTTP 200; source-1.html.gz prompt caching guide, HTTP 200); manifest suspicious_patterns null for both, and a manual grep for injection phrasing found none. Live re-fetch via WebFetch on 2026-10-26 matched snapshots (no newer changelog entry on 12-hour caching; no text differences in the quoted passages). source-0: 'Oct 20 / Feature / v1/responses' under 'October, 2026' (so the October 20 date and year 2026 are confirmed); entry text verbatim: 'You can now choose 12-hour prompt caching in the API. 12-hour prompt caching keeps shared instructions, tools, and context cached longer to benefit from lower cached-input pricing, even when requests are hours apart. 12-hour cache writes cost 2.5x the input token price.' Both quotes in the article are verbatim; the 'v1/responses' tag is correct. source-1 (guide): 'In the Responses API, you can choose a 12-hour minimum lifetime at a write rate of 2.5x ordinary input. Subsequent reads cost 0.1x the ordinary input rate on most of these models and 0.05x on GPT-6.1 Sol, regardless of the selected lifetime.' Cache lifetime table: prompt_cache_options.ttl, '30m' (default) 1.25x and '12h' 2.5x the uncached input-token rate, followed by 'These are total write rates, not surcharges on top of ordinary input.' (verbatim). Parameter name and values confirmed; the model-comparison table lists 'Supported retention values: "30m" (default), or "12h" in the Responses API', so the option is documented for the Responses API only; the 0.1x/0.05x multiples are cache READ rates, as the article says. The guide's breakpoint override (prompt_cache_breakpoint.ttl), upgrade-billing quote ('charges a new 12-hour write for those tokens, without credit for the earlier write.'), the coverage rule, the usage fields (input_tokens_details.cache_write_tokens_details with ttl_30m_tokens and ttl_12h_tokens, non-overlapping), the guidance quote ('Compare the higher 12-hour write rate with the cost of rewriting the prefix after 30-minute coverage expires.') are all verbatim/accurate. Worked example arithmetic re-computed: (1000 + 4000x0.1 + 3000x1.25 + 4000x2.5)/1,000,000 = (1000+400+3750+10000)/1e6 = 15150/1e6 = $0.01515, matching the guide; the article's inputs ($1 and $0.10 per million; 1,000 ordinary, 4,000 reads, 3,000 30m writes, 4,000 12h writes) match the guide's usage example (12,000 input tokens, 4,000 cached, 7,000 written). What We Don't Know items verified as stated in the guide: 'may retain entries longer than the requested minimum', ''12h' does not set a deletion deadline or change the maximum data-retention policy', 'does not guarantee that every request reuses stored KV tensors or has lower latency'. No per-million-token price for the 12-hour write appears on either page (the only dollar figures in the guide are the illustrative $1/$0.10/$0.01515), so that item is genuinely unstated. Gaps found: (1) the guide scopes the 1.25x/2.5x write multiples and the ttl parameter to GPT-5.6 and later; for earlier models the comparison table lists 'No additional cache-write charge' and the older prompt_cache_retention parameter ('24h' only / 'in_memory' or '24h'). The article states the 1.25x and 2.5x rates without that model scope (it says only 'For models that support it'), which could suggest they apply to all OpenAI models. (2) The article gives no retrieval date for prices on a live documentation page.
Factual Accuracy: Every number, date, parameter name, quote and attribution traced to the snapshots and the live pages. No fabricated specifics. Headline, summary and lead are each supported by the changelog and guide. Only the model-scope omission and absent retrieval date noted above, both recoverable by clarification.
Overall Assessment: Accurate, carefully hedged Briefing that publishes with a two-item clarification record. Featured candidacy: 1 - accurate pricing and API-option coverage for developers using OpenAI prompt caching, a defined audience