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OpenAI’s Decisions API is the better-documented option for new bounded-decision workflows, while Jev may remain the lower-change choice when an existing integration already matches your needs. OpenAI documents a general-availability POST /v1/decisions endpoint that accepts shared evidence and up to 400 typed questions. Its current model is gpt-6-luna, and its questions can return predicate probabilities, supplied choices, or rubric scores. Jev and OpenAI use materially different request and response contracts, so migration is not a drop-in rename. Preserve the decision meaning, re-map each question type, and retune thresholds on labeled examples before switching production traffic. There is no comparable first-party benchmark proving that either service is universally more accurate or faster.
What are Jev and OpenAI Decisions API designed to do?
Both services target bounded decisions: a software system supplies evidence, asks narrowly defined questions, and receives structured answers that can drive the next workflow step. This is different from an open-ended chat completion. The application defines the possible decision, the evidence to inspect, and—where appropriate—the allowed answers or scoring rubric.
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OpenAI describes Decisions as a generally available API for decisions over text, images, and structured inputs. The current guide documents POST /v1/decisions and lists gpt-6-luna as the supported model. A request contains shared input plus a questions array; each question declares the kind of answer required. See the OpenAI Decisions guide and the create-decision reference for the live schema.
Jev should be treated more cautiously in a current comparison. A Vercel comparison dated 7 October 2026 describes Jev requests as a state plus questions, with answers mapped by question ID. Jev’s exact current availability, pricing, image-input behavior, and privacy controls were not established in current first-party documentation, and search results conflict on image support. Confirm those items in TypeSafe’s documentation before making a procurement or migration decision. The Vercel comparison is useful for understanding the shape of an existing Jev integration, but it is a secondary, date-sensitive source: OpenAI Decisions API vs. Jev.
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How do the three OpenAI question types work?
Choose the question primitive that matches the decision’s semantics, not the one whose output number looks most convenient. OpenAI returns a typed answer for each question, and choice and score answers also include probability information.
Predicate: is a condition true?
A predicate question expresses a truth condition, such as whether an invoice appears fraudulent or whether a support request meets an escalation rule. The answer is a probability for the predicate. Your application can then apply a policy—automatic approval, rejection, or human review—rather than treating the probability as a universal confidence score.
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A choice question asks the model to select one option that you provide. Use it for a finite category or route, such as billing, technical, or account access. The response names one of the supplied choices and includes probability information, allowing your application to set a review policy.
Score: how well does the evidence satisfy a rubric?
A score question evaluates an ordered rubric. It is suitable when the answer is not a category but a level—such as a quality grade or severity band—with defined scoring criteria. The response contains the rubric score and probability information. Keep the rubric explicit and stable; changing its wording changes the meaning of historical scores.
These types are not interchangeable labels for a generic confidence value. A predicate probability answers “how likely is this condition?” A choice answer identifies one member of a finite set. A score answer places evidence on an ordered rubric. Mapping a Jev question to the wrong primitive can silently change business behavior even when the returned values look plausible.
What evidence and request limits does OpenAI document?
The Decisions API accepts several evidence forms in one request. The current guide lists text, images, JSON, conversation history, function calls, and tool outputs. Images may be supplied as hosted public URLs or inline base64 data URLs. The same documentation says that files, audio, and item references are not supported as inputs, so an application that receives those formats must transform or extract the relevant evidence before calling the endpoint.
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One request can contain up to 400 questions. All questions use the shared evidence supplied with that request, which is useful when several independent classifications or rubric checks concern the same document, conversation, or tool result. Splitting a large set into multiple calls changes token usage and may affect caching, latency, and consistency; measure those effects on your workload.
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| Capability | OpenAI Decisions API | Jev status for this comparison |
|---|---|---|
| Endpoint/request shape | POST /v1/decisions; shared input plus a questions array |
Vercel describes a state plus questions; verify the current Jev contract |
| Question count | Up to 400 questions in one request, according to the current OpenAI guide | Not stated in the available current source |
| Text input | Supported | Not independently confirmed here |
| Image input | Public image URLs and inline base64 data URLs supported | Search results conflict; confirm with current TypeSafe documentation |
| JSON, conversation, function calls, tool outputs | Supported as documented input forms | Not stated |
| Files and audio | Not supported directly | Not stated |
| Current model availability | gpt-6-luna is the currently supported model in the guide |
Not stated |
| Response identity | Named answers associated with the submitted questions | Vercel describes ID-mapped answers |
“Not stated” does not mean that Jev lacks a capability; it means you should not design a purchase or migration around an unverified assumption. Check the vendor’s current contract, limits, and data terms immediately before implementation.
What is different about the Jev and OpenAI request and response contracts?
The main integration difference is the data model around the questions. Vercel’s description of Jev uses a state object and question IDs, with the client looking up answers by those IDs. OpenAI’s documented model uses shared input, an ordered questions array, and named answers. Field names, answer extraction, and probability representations therefore need deliberate translation.
| Integration concern | Jev as described by Vercel (7 October 2026) | OpenAI Decisions API | Migration implication |
|---|---|---|---|
| Evidence container | state |
Shared input |
Move the same evidence, but rewrite request construction and validation |
| Questions | Questions identified for answer mapping | questions array with a declared type |
Assign each question a predicate, choice, or score primitive |
| Answer lookup | ID-mapped answers | Named answers tied to submitted questions | Replace Jev-specific extraction code and preserve your internal stable IDs |
| Probability field | Do not assume its representation matches OpenAI | Predicate returns a probability; choice and score include probability information | Normalize into an internal representation before applying policy |
| Option and rubric semantics | Preserve the existing question meaning when possible | Supply choices or a scoring rubric explicitly | Do not convert a category into a score merely to obtain a number |
Keep a vendor-neutral internal object in your application—for example, a decision ID, question ID, answer type, answer value, probability, model version, and policy outcome. The adapter for each provider can then translate its external schema without forcing business logic to depend on provider-specific field names.
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A safe migration preserves the decision’s meaning first and changes the transport second. Run the new adapter in shadow mode before allowing it to make irreversible decisions.
- Inventory every decision. Record the evidence supplied, each question’s wording, answer options, ordering, downstream action, and any Jev threshold or fallback. Include questions that are rarely triggered; edge cases often contain the most consequential assumptions.
- Classify each question. Map a finite category to
choice, an ordered rubric toscore, and a truth condition topredicate. If a Jev question mixes a category and a severity grade, split it into separate OpenAI questions rather than forcing both meanings into one type. - Preserve semantics and ordering. Carry over evidence wording, option text, and option order when order has meaning. Document any rewrite, because even a seemingly harmless synonym can alter borderline classifications.
- Build the OpenAI request adapter. Place the shared evidence in the documented input structure and construct the questions array. Validate that every choice has the intended finite options and every score has a complete rubric. Enforce the 400-question limit before sending a request.
- Build a response adapter. Extract the named answer, probability information, and any score into your internal object. Do not let business code read provider-specific response fields directly.
- Normalize policy decisions. Convert each provider’s output into your existing outcomes—such as approve, reject, route, or review—while keeping the raw answer and provider metadata for auditability.
- Run shadow evaluations. Send the same production-like cases to both systems without changing the live outcome. Include cases that previously went to human review, not only easy examples.
- Retune thresholds. Re-estimate acceptance and review thresholds using labeled examples. A value between zero and one from Jev is not automatically calibrated the same way as an OpenAI probability.
- Canary the cutover. Start with a low-risk segment, monitor disagreement and review volume, and retain a rapid rollback to the Jev adapter until the new path is stable.
The Vercel comparison specifically calls out differences in field names and probability representations and recommends testing thresholds on labeled examples. Treat that guidance as migration advice, not as evidence that one provider has better calibration.
How should I compare accuracy, calibration, and latency?
There is no universal winner in the available evidence. OpenAI and Jev must be compared on the decisions your product actually makes, with labels and costs that reflect the consequences of errors.
Build a labeled evaluation set
Collect representative examples across normal, ambiguous, adversarial, and low-quality inputs. Have qualified reviewers assign the intended category, rubric score, or truth label, and record when a human would reasonably defer rather than force an answer. Keep a held-out set that neither adapter uses for threshold tuning.
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Measure the errors that matter
- Accuracy or agreement: compare each answer with the adjudicated label for the relevant question type.
- False approvals and false rejections: weight these according to financial, safety, or customer-support impact.
- Review rate: count how often the system correctly routes uncertainty to a human and how often it creates avoidable review work.
- Calibration: group probability outputs into bands and check whether observed correctness matches the stated probability. Do this separately for each question type.
- Coverage: measure malformed, unsupported, or unanswerable inputs rather than silently dropping them.
- Latency: record end-to-end time, including network, preprocessing, retries, and queueing, at the concurrency your application expects.
- Cost per completed decision: include retries, human review, cache behavior, and any preprocessing—not just the nominal token price.
Do not publish a performance verdict from a small anecdotal sample. The available sources contain no matched first-party Jev-versus-OpenAI benchmark for accuracy, latency, or calibration.
What does OpenAI Decisions API cost?
The current OpenAI guide lists uncached input at $0.10 per million tokens and cache hits at $0.01 per million tokens. Output tokens are not charged under that published pricing. These are token rates, not a guaranteed per-request price: your total depends on evidence size, question count, cache reuse, and request frequency.
The same guide warns that cache writes, regional-processing premiums, and long-context multipliers can affect cost. Confirm the live pricing page and the processing mode selected for your account before calculating a budget. For a realistic estimate, log input tokens per decision, the proportion of requests that hit the cache, the number of questions per call, and any regional or long-context surcharge.
| Cost item | Published OpenAI information | What to verify in your estimate |
|---|---|---|
| Uncached input | $0.10 per million tokens | Average evidence and question tokens per request |
| Cache hit | $0.01 per million tokens | Whether your repeated evidence actually qualifies for a cache hit |
| Output tokens | Not charged in the current guide | Any future pricing change and non-token platform costs |
| Cache writes | May affect cost | Write behavior for your chosen request pattern |
| Regional processing | Premiums may apply | Required region and eligibility |
| Long context | Multipliers may apply | Evidence length and the applicable context tier |
No current Jev price is established here. Obtain a written, current quote or pricing page from Jev before declaring one service cheaper.
What privacy, residency, and compliance controls are available?
OpenAI says zero data retention (ZDR) and HIPAA use are available for eligible customers, subject to eligibility and agreement conditions. The guide also documents data residency in supported API regions, with regional processing in the United States and in Europe—defined there as the European Economic Area plus Switzerland. Confirm that your organization, endpoint configuration, and contract qualify; the existence of a control in the product documentation is not a blanket compliance approval.
Current Jev data-retention, residency, and compliance terms were not verified in the available first-party material. Ask Jev for the applicable retention period, training-use policy, subprocessors, deletion process, regional-processing options, and contractual terms for your data category. Compare equivalent commitments rather than treating a marketing label as a technical guarantee.
Regardless of provider, minimize evidence to what the decision needs, redact unnecessary personal data, log access to decision results, and maintain a retention schedule for raw inputs and outputs. Those application controls remain your responsibility even when a vendor offers a favorable data-processing agreement.
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Is the OpenAI API faster than Jev?
No general speed conclusion is established. OpenAI’s 22 October 2026 announcement reports that its Decisions integration in Speak reduced time to next action from 1.4 seconds to 0.13 seconds in testing. That is OpenAI’s account of a specific Speak integration and should not be read as a general Jev-versus-OpenAI latency benchmark.
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Measure the path your users experience: request creation, image encoding or evidence extraction, network transfer, provider processing, response parsing, retries, and any human-review queue. Test cold and warm caches, realistic payload sizes, concurrency, and regional placement. Report median and tail latency, because a low average can conceal unacceptable p95 or p99 delays.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which API should I choose for a new project?
Choose OpenAI when you need a documented, typed contract, multimodal and structured inputs, up to 400 questions per request, and published pricing and eligibility information. Choose Jev only after confirming its current contract, modality, price, privacy terms, and operational fit directly with the vendor; an existing Jev integration can be a rational reason to avoid migration if it already meets your requirements.
| Your situation | Practical choice | Why |
|---|---|---|
| Starting from zero and need documented image, JSON, conversation, or tool-output inputs | Evaluate OpenAI first | Those input forms and the request limit are documented in the current guide |
| Already operating Jev with stable, acceptable outcomes | Run a measured migration trial before switching | Migration changes schemas and may change calibration; avoid change for its own sake |
| Need a finite label, an ordered grade, and a truth test in one workflow | OpenAI’s typed questions are a clear fit | Choice, score, and predicate express those meanings directly |
| Strict regional or regulated-data requirements | Compare contractual eligibility, not feature lists | OpenAI documents ZDR, HIPAA, and regional processing only for eligible customers; Jev terms require confirmation |
| Decision quality is business-critical | Run a matched labeled evaluation of both | No published universal performance winner exists |
Vendor selection should be an evidence-based decision: define the error that costs your business most, test both adapters on the same cases, and include operational and compliance requirements alongside model outputs.
What pitfalls can break a migration?
- Using the wrong primitive: a score is not a category, and a predicate is not a free-form explanation. Re-state the business rule before mapping it.
- Reusing Jev thresholds: identical-looking probabilities can have different calibration. Tune on labeled data and include a human-review band.
- Dropping option order: if your existing logic or users rely on order, preserve it and test boundary cases.
- Assuming unsupported inputs work: OpenAI’s guide excludes files, audio, and item references. Detect these before the request and apply a documented preprocessing path.
- Ignoring the 400-question ceiling: validate batches in code and define deterministic splitting for larger workloads.
- Hard-coding provider fields: isolate request and response parsing in an adapter so a schema revision does not alter policy code.
- Comparing nominal prices only: include cache writes, regional premiums, long-context multipliers, retries, and human review.
- Treating an announcement as a benchmark: the Speak latency figure is a specific integration result, not a cross-provider test.
- Skipping rollback: retain the old provider path until disagreement, error, cost, and latency monitoring meet your acceptance criteria.
How should I monitor a production decision service?
Store a versioned decision definition with every result: provider, model, question text, option or rubric version, input schema version, answer, probability, latency, token usage, and final policy action. Keep raw evidence only as long as your retention policy permits, and redact sensitive fields in operational logs.
Alert on changes in review rate, answer distribution, malformed responses, unsupported-input errors, token cost, and p95 latency. Sample disagreements for human adjudication and feed confirmed labels back into threshold reviews. When you change a rubric, option list, prompt wording, or provider model, create a new definition version rather than comparing unlike results under one label.
What is the current availability outlook?
OpenAI announced general availability on 22 October 2026, and its current guide identifies gpt-6-luna as the supported model. Model availability, regional eligibility, pricing, and schema details can change, so pin the API behavior you depend on in integration tests and check the official guide before a release.
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Jev’s later status is less certain from the current material. The comparison published by Vercel is dated 7 October 2026, while current first-party TypeSafe documentation was not verified for this article. Treat Jev-specific claims as provisional until you confirm them at the source.
Bottom line: is OpenAI Decisions API a replacement for Jev?
It can replace Jev for many bounded-decision workflows, but it is not a drop-in replacement. OpenAI offers a current, typed contract with predicate, choice, and score questions, documented multimodal and structured inputs, a 400-question request limit, published token pricing, and stated eligibility paths for ZDR, HIPAA, and regional processing. Jev may still be the sensible choice for an unchanged deployment, but its current capabilities and commercial terms must be verified directly.
Make the decision with a semantic mapping, a provider adapter, and a labeled side-by-side evaluation. Preserve the business meaning first, then choose based on measured error costs, calibration, latency, total cost, and compliance—not on an unverified claim that one API wins everywhere.
Frequently Asked Questions
Can the Decisions API accept a PDF or audio recording directly?
The current OpenAI documentation lists files and audio as unsupported input forms. Extract the needed text or image evidence in your own pipeline, then send a supported representation such as text, a hosted image URL, or inline base64 image data.
How many separate decisions should I put in one request?
OpenAI documents a maximum of 400 questions per request, all evaluated against shared evidence. Batch together questions that genuinely use the same evidence, but benchmark larger batches because token volume, latency, cache behavior, and failure impact can differ from smaller calls.
Should I expose the returned probability to end users?
Usually treat it as an internal policy signal rather than a promise of certainty. Validate calibration on your labeled workload first, then decide whether to show a human-readable explanation or route low-probability cases to review instead of displaying a raw number.
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Keep stable internal decision and question IDs, the original evidence reference, the provider and model, the version of each option list or rubric, the raw typed answer, and the final policy action. This lets you audit disagreements and compare providers without coupling business records to either external schema.
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