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Short answer: OpenAI’s Decisions API is a generally available, bounded-decision endpoint for applications that need a predicate probability, a choice from supplied options, or a rubric score. It accepts shared text, images, JSON, conversation history, function calls, and tool outputs, and currently supports gpt-6-luna. Jev uses a different request and answer contract. If you are moving an integration, preserve the meaning and ordering of every decision, remap each question to OpenAI’s predicate, choice, or score type, then retune thresholds on labeled examples. There is no published, matched Jev-versus-OpenAI benchmark that proves a universal winner for accuracy, calibration, or latency.
What is the difference between Jev and OpenAI’s Decisions API?
Both services are designed for bounded decisions inside a software workflow rather than open-ended chat. The important difference is the contract your application sends and receives. A Vercel comparison dated October 7, 2026 describes Jev requests as a state plus questions, with answers mapped by question ID. OpenAI’s API instead uses shared input, a questions array, and named answers. Treat the Jev description as secondary and date-sensitive: current TypeSafe documentation was not verified, and search results conflict about some Jev capabilities, including image input. Vercel’s comparison
| Axis | OpenAI Decisions API | Jev |
|---|---|---|
| Decision model | Questions attached to shared evidence; each question is a predicate, choice, or score. | A state plus questions, according to the October 2026 Vercel comparison. |
| Answer addressing | Named answers associated with the submitted questions; choice and score include probability information. | ID-mapped answers, according to Vercel. |
| Documented model | gpt-6-luna is currently the only supported model. |
Current model availability was not established. |
| Inputs | Text, hosted public image URLs, inline base64 images, JSON, conversation history, function calls, and tool outputs. | Current modality and image-input status require confirmation from TypeSafe’s documentation. |
| Questions per request | Up to 400. | Not stated in the available current source. |
| Published price | $0.10 per million uncached input tokens; $0.01 per million cached input tokens; output tokens are not charged. | Current price was not established. |
| Privacy and residency | ZDR and HIPAA are available for eligible customers; supported regional processing includes the United States and Europe (EEA plus Switzerland), subject to agreements. | Current controls were not established. |
Do not turn this table into a quality verdict. The services may make different decisions on your data, and neither source supplies a controlled comparison using the same prompts, labels, traffic, and latency measurement.
How does OpenAI’s Decisions API work?
OpenAI’s endpoint separates evidence from the decisions you want to ask about. One request can provide a shared body of evidence and then ask many typed questions against it. The current endpoint is POST /v1/decisions, and OpenAI documents gpt-6-luna as the only supported model at present. OpenAI’s Decisions guide Create-a-decision API reference
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What does a predicate question return?
A predicate asks whether a condition is true. The answer is a probability for that condition, so it fits gates such as “Does this submission meet the stated policy?” or “Is the extracted address complete?” Your application still chooses the operating threshold; the API does not know whether a false positive or a false negative costs more in your product.
What does a choice question return?
A choice question supplies a finite set of allowed options and returns one of those options. OpenAI also returns probability information for choice answers. Use this type for mutually exclusive categories such as routing a ticket to a team or selecting one workflow branch. Keep option wording stable when you compare versions, because changing labels changes the decision task.
What does a score question return?
A score question uses an ordered rubric rather than a free-form number. Define what each score means, then interpret the returned score and its probability in your application. This is appropriate for graded checks such as a review rubric, where “4” must have a clear meaning distinct from “3.”
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Which evidence formats can I send?
The guide lists text, images, JSON, conversation history, function calls, and tool outputs. Images can be supplied as hosted public URLs or inline base64 data URLs. Files, audio, and item references are not supported by the current guide, so an application that relies on those forms must convert or preprocess them before calling the endpoint.
How many questions can one request contain?
OpenAI documents a maximum of 400 questions in a single request. That limit is a request-level ceiling, not a promise that one very large call will be cheaper or easier to operate. Group questions that share evidence, and split independent work when payload size, timeout budgets, or failure isolation matter.
What request and response changes should I expect when moving from Jev?
Plan the migration as a semantic translation, not a find-and-replace of field names. The Vercel comparison specifically notes that field names and probability representations differ. Preserve the decision’s intent first; then adapt serialization and parsing to the OpenAI schema.
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Map each Jev question to one OpenAI primitive
- Truth condition: use
predicateand document what “true” means. - Finite category: use
choicewith the complete allowed option set. - Ordered rubric: use
scoreand define the rubric boundaries and examples.
Do not replace a rubric with a choice list merely because both can be represented as labels. A score preserves ordering; a choice does not. Conversely, do not use a score for categories that have no meaningful order.
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Preserve evidence, wording, and meaningful order
Carry over the evidence that made the Jev decision valid, including the same state fields, user text, and relevant tool results. Preserve option wording and order when order conveys priority or when downstream code relies on stable presentation. Record any deliberate wording change as a versioned migration decision rather than silently changing the task.
Adapt answer extraction
Jev’s ID-mapped answer pattern and OpenAI’s named-answer pattern require different lookup code. Build a mapping from your internal decision key to the OpenAI question name, then parse the returned type explicitly. For predicates, read the probability; for choices, validate that the returned value is one of your submitted options and store its probability; for scores, validate the rubric range and store the score with its probability.
Keep an audit record during the cutover
For each shadow or production request, retain a versioned decision definition, the evidence identifier, question names, returned values, probabilities, threshold used, and final business action. Redact or tokenize sensitive evidence according to your data policy. This record lets you explain a changed outcome without assuming that two numeric outputs have identical meaning.
How do I migrate a Jev integration step by step?
A safe cutover has a semantic inventory, a dual-run period, and a measured threshold review. Do not switch vendors before you can explain every changed decision to the owning product team.
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- Classify every question. Mark each as predicate, finite choice, or ordered score. Resolve ambiguous questions with the person who owns the business rule, not by guessing from its field type.
- Construct shared evidence. Combine the state and supporting material into the OpenAI input structure. Convert unsupported files or audio to text or another supported representation, and use a public image URL or inline base64 for images.
- Name questions deterministically. Create stable names that your application can use for parsing and logging. Keep a mapping back to the old Jev question ID.
- Translate options and rubrics. Submit the complete choice set and an explicit score rubric. Preserve the original order where it has meaning, and reject unexpected choice values rather than silently routing them.
- Implement typed parsing. Branch on the question type, validate the returned value, and store probability separately from the business threshold. Treat missing or malformed answers as an operational error with a safe fallback.
- Shadow both systems. Send identical evidence to Jev and OpenAI without changing the user-visible action. Capture disagreements, probabilities, latency, token use, and fallback events.
- Build a labeled evaluation set. Include ordinary cases, edge cases, historical errors, and examples that should be sent to human review. Have domain reviewers label the correct outcome independently of either model.
- Retune thresholds. Choose predicate, choice, and score acceptance thresholds from the labeled results. Do not reuse a Jev number just because OpenAI also returns a value between zero and one.
- Canary and monitor. Route a small percentage of traffic to OpenAI, compare business outcomes and review rates, then increase exposure only when error and latency budgets remain acceptable. Keep a rollback path to the frozen Jev contract until the new version is stable.
How should I compare accuracy, calibration, and latency?
Use a matched, application-specific evaluation. No first-party source establishes a universal Jev-versus-OpenAI winner, so a decision based on a generic benchmark or an isolated anecdote can be misleading.
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Measure the errors your product actually pays for
- Predicates: calculate false-positive and false-negative rates at the thresholds you could deploy, not only a single overall accuracy number.
- Choices: report per-option precision and recall, confusion between costly categories, and the rate of invalid or low-confidence outputs.
- Scores: compare agreement with the human rubric, weighted error by score distance, and whether high scores really identify cases safe to automate.
- Human review: measure how many cases each threshold sends to review and the error rate among cases you automate.
Check calibration, not just ranking
A probability is useful only if it corresponds reasonably to observed frequency. Bin predictions by probability, compare predicted and observed rates, and inspect calibration separately for important segments. If the numbers are poorly calibrated, select thresholds on held-out labeled data and consider a review band rather than treating the raw value as a guarantee.
Measure end-to-end latency
Record request queue time, network time, model time, parsing time, and the time until your application can take its next action. Use the same payloads, region, concurrency, timeout, and retry policy for both services. OpenAI reports that its Decisions integration in GPT-Live reduced time to next action from 1.4 seconds to 0.13 seconds in testing with Speak; that is OpenAI’s account of a specific integration, not a general API latency benchmark. OpenAI announcement, October 22, 2026
Use confidence intervals and failure tests
Keep a held-out test set and report uncertainty around rates, especially for rare but severe errors. Add malformed JSON, missing fields, long conversations, image-fetch failures, timeout retries, duplicate submissions, and partial downstream outages to operational tests. A provider that is slightly better on clean examples may be worse for your product if its failure mode is harder to recover from.
What does the OpenAI Decisions API cost?
OpenAI’s current guide lists $0.10 per million uncached input tokens and $0.01 per million cached input tokens. Output tokens are not charged. Cache writes, regional-processing premiums, and long-context multipliers can change the effective bill, so calculate using the processing mode and context size you will actually run. OpenAI Decisions pricing and controls
| Cost item | Published treatment | What to verify in your design |
|---|---|---|
| Uncached input | $0.10 per million tokens | Average evidence size, question count, and monthly volume. |
| Cached input | $0.01 per million tokens | Whether repeated evidence and request structure actually produce cache hits. |
| Output tokens | Not charged | Other API or application costs around parsing, storage, and review. |
| Cache writes | May affect cost | Live pricing for your cache behavior. |
| Regional processing | Premiums may apply | Required region and eligibility before committing to a residency design. |
| Long context | Multipliers may apply | Whether conversation history or evidence exceeds the standard context band. |
Jev’s current price was not established in the available current sources. Obtain a first-party quote or pricing page before using cost as a deciding factor. For either provider, estimate the full workflow cost: preprocessing, retries, storage, human review, and the financial impact of wrong decisions.
What privacy and data-residency options are documented?
OpenAI says zero data retention (ZDR) and HIPAA use are available for eligible customers. Data residency is available in supported API regions, with regional processing in the United States and Europe (EEA plus Switzerland); eligibility, configuration, and contractual conditions apply. Confirm that your organization qualifies and that the exact endpoint and processing mode meet your policy before sending regulated data. OpenAI Decisions guide
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Do not infer equivalent Jev controls from the existence of a similar feature in another service. Jev’s current retention, residency, and compliance terms require direct confirmation from TypeSafe’s documentation or contract. If residency is a requirement, include it as a release gate rather than a checkbox added after the technical migration.
Which input limitations can break an otherwise correct migration?
Most migration failures occur at the boundary between your data pipeline and the new input contract, not in the question wording itself.
- Unsupported media: the current OpenAI guide excludes files and audio. Transcribe or extract required content before the request, and preserve provenance so reviewers know how it was transformed.
- Private image URLs: hosted images must be reachable by the service. Use an approved public URL or inline base64, while considering exposure, size, and expiration.
- Oversized batches: 400 questions is the documented maximum, but context and timeout limits can be reached earlier. Batch by shared evidence and business priority.
- Changed conversation context: tool calls and outputs are supported, but omitting a prior action can change the decision. Build the same context deliberately in both shadow requests.
- Implicit defaults: Jev and OpenAI may handle missing fields or unknown options differently. Validate inputs before submission and define an explicit fallback for every rejected result.
Is OpenAI better than Jev for my workload?
There is not enough comparable evidence to answer that as a general yes or no. OpenAI has the more fully documented current contract in the available material, including typed questions, supported input forms, a published request limit, pricing, and stated privacy options. That documentation advantage does not establish that it will be more accurate, faster, or better calibrated on your application.
Choose based on a weighted evaluation that reflects your constraints:
| Decision factor | Questions to answer before selecting |
|---|---|
| Semantic fit | Can the service express your truth tests, categories, and ordered rubrics without awkward conversions? |
| Integration effort | How much request-building, answer parsing, logging, and fallback code must change? |
| Modality | Can it accept the text, images, JSON, conversation, and tool evidence your workflow actually produces? |
| Quality | Which service meets your false-positive, false-negative, ranking, and review-rate targets on labeled data? |
| Calibration | Do probabilities support your operating thresholds and human-review band? |
| Latency | Does end-to-end time fit the user interaction after retries and downstream work? |
| Cost | What is the effective cost after caching, regional processing, context multipliers, and review? |
| Governance | Do retention, residency, HIPAA, and contractual terms satisfy your requirements? |
| Availability | Are the model, limits, versioning policy, and support commitments suitable for your launch date? |
What should I verify before putting a provider into production?
Run this release checklist with engineering, product, security, and operations owners:
- Every decision has an explicit type, definition, owner, and version.
- Choice options and score rubrics are validated before requests are sent.
- Question names map deterministically to your internal IDs.
- Unexpected, missing, or low-confidence answers have a safe fallback.
- Thresholds were selected on labeled examples and tested on held-out cases.
- Human-review routing and escalation are measured, not assumed.
- Payloads exclude unsupported files and audio or transform them with traceable preprocessing.
- Timeouts, retries, idempotency, rate limits, and duplicate actions are tested.
- Logging retains enough information for audit without violating data-minimization rules.
- Pricing assumptions include cache behavior, region, long context, and operational overhead.
- Security and residency approvals cover the exact endpoint and account configuration.
- A rollback switch can restore the previous provider without changing business semantics.
What is the practical verdict on Jev versus OpenAI Decisions?
OpenAI’s Decisions API is the better-documented option in the current material for teams that want a first-party, typed decision endpoint with multimodal and structured evidence, a 400-question request limit, transparent token pricing, and documented eligibility paths for data controls. Jev may still fit an existing integration or a workload where its established behavior performs better, but its current contract, image support, price, and privacy terms need direct verification.
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The responsible choice is therefore an evaluation result, not a headline winner: preserve decision semantics, run both services on the same labeled workload, retune thresholds, and select the provider that meets your product’s error, latency, governance, and cost limits.
Frequently Asked Questions
Can I send one OpenAI Decisions request for unrelated decisions?
You can submit up to 400 questions, but unrelated decisions may not share useful evidence or failure handling. Separate batches when they have different owners, privacy requirements, timeout budgets, or rollback behavior.
Do OpenAI decision probabilities equal calibrated confidence?
No. They are model-generated probability information, not a product guarantee. Check calibration against observed labels and set thresholds or a human-review band from your own data.
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The current Decisions guide does not list files or audio as supported inputs. Convert them to an accepted representation, such as reviewed text or an allowed image form, and retain provenance for the transformation.
Should I keep Jev running after switching to OpenAI?
Keep the old path during shadowing and the initial canary if the business action is reversible. Remove it only after error rates, review load, latency, cost, and rollback procedures are documented for the new version.
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