The change is one line in a request body, which is exactly why it got switched on in three of our services without anyone doing the arithmetic. A longer cache sounds like a free win. It is not free, it costs double, and whether it pays depends on a property of your workload that most teams have never measured: how many times a day the same stable prefix gets reused, and how far apart those uses sit. We had it enabled on a prefix used twice a day. That is the one case where it cannot possibly pay.
What was published, and when
On 20 October 2026, OpenAI documented a new minimum cache lifetime in the prompt caching guide for the Responses API. For GPT-5.6 and later, a request sets prompt_cache_options.ttl to pick between two values:
"30m", the default, a 30 minute minimum lifetime at a cache-write rate of 1.25x the uncached input-token rate. The documentation suggests it for “conversations and agent steps that reuse context frequently”."12h", a 12 hour minimum lifetime at a cache-write rate of 2.5x the uncached input-token rate, suggested for “stable instructions or reference material reused across longer gaps”.
Three details in that section matter more than the headline. First, these are total write rates, not surcharges added to ordinary input, so the 12-hour write is double the short one rather than 2.5 times more expensive than it. Second, cache-read rates are identical under either lifetime, which means the entire decision is a write-against-write comparison. Third, the request-level value applies to the implicit breakpoint and to explicit breakpoints without their own setting, while prompt_cache_breakpoint.ttl overrides it for one breakpoint, so a single request can mix lifetimes. Unsupported values return a 400 invalid_request_error.
The documentation also spells out the pattern it expects: keep material reused throughout the day at the start of the prompt with a 12-hour breakpoint, use the 30-minute default for session context that changes more often, and in explicit-only mode leave the current question after the last breakpoint so that content never incurs a cache-write charge at all.
Our arithmetic from the documented rates. Two widely spaced uses a day is the trap: the writes tie, and the extra read makes the long lifetime strictly worse.
Our expert take 1: this is an engineering decision wearing the costume of a parameter
Here is the whole calculation, because it takes one paragraph and almost nobody does it. Reads cost the same under either lifetime, so compare writes. On the default, a prefix reused with gaps longer than thirty minutes is rewritten each time, costing 1.25 per use. On the long lifetime it is written once at 2.5 and read thereafter. One use: 1.25 against 2.5, the default wins. Two uses: 2.5 against 2.5 in writes, and the long path still owes you a read, so it loses. Three uses: 3.75 against 2.5 plus two reads, and the long path wins for any read rate below roughly 0.6 of the uncached rate, which is every read rate on offer.
So the threshold is three widely spaced uses of the same prefix per working day. Above it, switch. At or below it, switching doubles your write bill for nothing. That number is not in the documentation because it depends on your traffic shape, and that is precisely the point: the vendor shipped a knob and left the measurement to you.
A parameter with two values and a 2x price difference is not a configuration choice. It is a question about your own workload that somebody has to be accountable for answering.
In our case the answer was embarrassing. A retrieval service with a large stable instruction block ran a batch twice a day, morning and late afternoon. Someone set the lifetime to twelve hours on the reasonable-sounding grounds that the gap was more than thirty minutes. The gap being long is a necessary condition, not a sufficient one, and at two uses the move was a pure loss. We found it by asking which prefixes had a long lifetime and how often each was actually hit, a question no dashboard answered because nobody owned it. The sequencing we use for this kind of gap is the same one in our note on hiring agent infrastructure engineers: define the metric the role is accountable for before writing the job description.
Our expert take 2: the skill this rewards is prompt layout, which is not what the market is selling
Read the documented pattern again as a job description rather than a tip. Stable material first, with the long lifetime on its breakpoint. Session context after it, on the default. The live question after the last breakpoint, so it is never written at all. What that describes is an ordering constraint on how a prompt is assembled, enforced across every call path in a codebase, including the ones where a developer has helpfully concatenated the user question into the middle of the system block.
That is a structural property of your prompt-building code, and in most teams it is nobody’s property. Prompts are assembled in three places by four people, one of them a template in a feature branch. The moment cache lifetime carries a 2x price, the assembly order stops being a style question and becomes a cost control, and it needs an owner with the authority to say that the question goes last.
This is also where the market mismatch bites. The candidates most visibly available for “AI” roles in Dubai right now are strong at eliciting behaviour from a model: phrasing, evaluation sets, guardrails. Valuable, and not this. The person who fixes a cache-write bill reads a token usage breakdown, finds that cache_write_tokens is large on requests that should have been reads, and traces it to a prefix that changes by one timestamp on every call. That is systems debugging with a billing dimension, closer to performance engineering than to prompt craft. We screen for it with the same approach as our note on AI infrastructure hiring, which is to hand over a usage report rather than ask about experience.
Let us discuss it: bring us your token bill
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Get 3 Free Developer ProposalsOur expert take 3: the retention sentence is the one your security review will quote back at you
One sentence in that section deserves to be read slowly, because it is the opposite of what the parameter name suggests. OpenAI states that it may retain entries longer than the requested minimum, and that the 12-hour value “does not set a deletion deadline or change the maximum data-retention policy”.
So ttl here is a floor, not a ceiling. It is a guarantee about availability and billing, not a promise about erasure. If you operate in a regulated environment, and in this market a sizeable share of engineering teams sit downstream of a bank, an insurer or a government entity, that distinction is the difference between a correct answer and a finding. A team that tells its risk function “we set a twelve hour TTL so data is gone in twelve hours” has inverted the meaning of the only sentence that mattered.
We treat this as a screening question rather than a policy item, because it separates two groups cleanly. Ask a candidate what a cache lifetime of twelve hours implies about data retention. People who have run this in production say it implies nothing about retention and reach for the data-processing terms instead. People who have read a changelog say the data is deleted after twelve hours. The second answer is not a knowledge gap you can train away in a quarter, because it reflects a habit of reading documentation for permission rather than for constraints.
Bands 1 and 2 are a code structure, not a prompt. Nobody owns a code structure that spans four services by accident.
The three roles this creates, with the question that tests each
None of these are new job titles, and that is deliberate. Inventing a title for a parameter is how teams end up with a requisition nobody can fill. These are existing roles with a scope that this change makes concrete, and each has a question that a candidate either answers from experience or does not.
| Role | Scope this change gives it | The question that tests it |
|---|---|---|
| Agent infrastructure engineer | Owns prompt assembly order across services, and the per-request cost number that comes out of it. | “Here is a usage breakdown where cache_write_tokens is high on repeated calls. Find the cause.” |
| Platform engineer with a billing mandate | Turns the three-use threshold into a measurement rather than a guess, per prefix, continuously. | “How would you know next week which cached prefixes are below break-even?” |
| Backend engineer, integrations | Enforces that the live question stays after the last breakpoint in every call path, including the awkward ones. | “Where in this codebase would a variable sneak into a cached prefix?” |
| Not this: a prompt specialist | No cost ownership, no call-path authority. The wording is not what the 2x is charging for. | Not applicable, and that is the finding. |
The fourth row is worth defending in a planning meeting. A prompt specialist requisition is the one that gets approved easily, because the title matches the news cycle. It also leaves the bill exactly where it was. If you want to compare the loaded cost of these shapes before committing to one, our team cost calculator covers each.
Three caveats worth stating
First, the break-even above assumes gaps longer than the 30-minute default. If your prefix is hit every few minutes the short lifetime already holds and the long one is irrelevant. Measure the distribution of gaps, not the average, because a workload with a burst in the morning and one call at night has an average that describes neither.
Second, this applies to GPT-5.6 and later in the Responses API, and the documentation notes these settings do not change the older prompt_cache_retention parameter. A mixed fleet will have two mental models running at once, which is its own source of mistakes.
Third, our three-use threshold is arithmetic on published rates, not a benchmark. It tells you which way the decision leans; your own usage report tells you the answer. Treat any number in this article that we did not attribute to the documentation as a hypothesis to test against your bill.
FAQ: the 12-hour cache lifetime and what to hire for
What exactly did OpenAI change on 20 October 2026?
For GPT-5.6 and later models in the Responses API, a request can now set prompt_cache_options.ttl to choose the minimum cache lifetime. The documented values are "30m", which remains the default and carries a cache-write rate of 1.25 times the uncached input-token rate, and "12h", which carries a cache-write rate of 2.5 times the uncached input-token rate. OpenAI states these are total write rates rather than surcharges on top of ordinary input, and that cache-read rates are the same under either lifetime. The request-level setting applies to the implicit breakpoint and to explicit breakpoints that do not carry their own ttl, and prompt_cache_breakpoint.ttl overrides it for an individual breakpoint. Unsupported values return a 400 invalid_request_error.
Is the 12-hour lifetime always cheaper for an agent that runs all day?
No, and the break-even is easy to work out. Reads cost the same under either lifetime, so the comparison is write against write. If a stable prefix is used twice in a day with a gap longer than thirty minutes, two writes at 1.25 cost 2.5, which is exactly what one 12-hour write costs before you add the second read, so the 12-hour option never wins at two uses. At three uses the 30-minute path costs 3.75 in writes while the 12-hour path costs 2.5 plus two reads, and it wins for any read rate below roughly 0.6 times the uncached rate. Three widely spaced uses of the same prefix in a working day is the practical threshold. Below that, paying double for the longer lifetime is a loss.
Does a 12-hour cache lifetime mean our data is stored for 12 hours?
The documentation is careful on this point and worth quoting in a security review rather than paraphrasing. It states that OpenAI may retain entries longer than the requested minimum, and that the 12-hour value does not set a deletion deadline or change the maximum data-retention policy. The ttl is a minimum lifetime, which is a performance and billing guarantee, not a retention ceiling. Any team that answers a data-residency questionnaire by pointing at this parameter has read it as the opposite of what it says, which is one of the clearer signals to look for when you are assessing a candidate who claims production agent experience.
Which role should a Dubai team hire first to act on this?
An agent or AI infrastructure engineer who treats the prompt as a build artefact rather than a string. The work this parameter creates is prompt layout: putting stable instructions and reference material at the front of the prefix, giving that breakpoint the long lifetime, leaving session context on the default, and keeping the live question after the last breakpoint so it never incurs a write charge. That is a structural concern owned by whoever controls how prompts are assembled, which in most teams is nobody in particular. Hiring a prompt engineer will not cover it, because the skill required is cost-aware systems design, not wording.
Discuss it with us before your next invoice
Tell us how many agent call paths you run and who owns the cost per request today. We will come back with the role that closes the gap, the screen that tests for it, and the shape of the pool available in this market. Platform engineers | Node.js developers
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Source: OpenAI API documentation, “Prompt caching”, cache lifetime section for GPT-5.6 and later, consulted 22 October 2026. The two ttl values, the 1.25x and 2.5x cache-write rates, the statement that these are total write rates and that cache-read rates are identical under either lifetime, the breakpoint override behaviour, the 400 invalid_request_error on unsupported values, and the retention wording are all from that page. The break-even of three widely spaced uses per day, the three-role shortlist, the screening questions and the prompt-layout-as-ownership argument are our own analysis and are not part of OpenAI’s documentation.