Decisions Your Code Can Read
Define the possible answers before the call. Jev returns a typed choice, score, or yes/no probability, so downstream logic can inspect a defined result instead of extracting a label from generated prose.
Chat, coding agents & document work. Priced per 1M tokens — input, cached input and output are billed separately. GPTProto is 20% below official rates.
TypesafeAI · ≈ 100M tokens/mo (0M cached)
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Jev Latest is TypeSafe AI's moving alias for its current stable System One model. Give it a state and a set of well-scoped questions; receive answers that your application can inspect and act on. Use it where you need to classify a record, choose a route, or estimate whether a condition is true. Keep tools, permissions, arithmetic, and final actions in your own code.
Define the possible answers before the call. Jev returns a typed choice, score, or yes/no probability, so downstream logic can inspect a defined result instead of extracting a label from generated prose.
Use Choice to select from named options, Score for a defined scale, and Noul for a yes/no probability. Choice and Score also include a separate confidence field; Noul does not.
Ask multiple independent questions about the same state in one request. For a support ticket, decide the department, check urgency, and score frustration without sending the record separately for each judgment.
jev-latest can move to a newer stable release. Log the versioned model ID returned with each result, and pin a tested version when your decision thresholds depend on consistent behavior.
TypeSafe AI calls Jev a System One model: its job is to evaluate bounded questions about supplied information, rather than write an open-ended response. An application can send a customer message, a retrieved document excerpt, or a structured object as state. Under questions, it specifies what to judge and, for Choice or Score, the permitted options or levels. Answers come back under the corresponding question IDs.
As of September 23, 2026, TypeSafe's jev-latest alias points to jev-1.13.0. The alias may advance without a change to your request. This matters for developers who set thresholds for automatic actions: retest representative cases when the underlying version changes, and keep the actual returned model ID in logs. The official request budget is 64K tokens overall, with a separate 32K limit for the state plus the longest individual question; remove irrelevant text even when a request fits those limits.
| Specification | Jev Latest |
|---|---|
| Provider | TypeSafe AI |
| Current stable alias target | jev-1.13.0 as of September 23, 2026; subject to change |
| Input | Text, or text-shaped state provided as a string, object, or array |
| Output | Typed decisions and probability values; not generated prose |
| Decision types | Choice, Score, Noul |
| Choice options | Up to 255 predefined options |
| Official request limits | 64K tokens total; state plus longest question up to 32K tokens |
| GPTProto model string and endpoint | See the live API Usage tab; confirm Jev's decision request format before publishing |
Support routing: Ask a Choice question for billing, technical, sales, or other, with a short definition for each. Send a separate Noul question about urgency. Route routine cases automatically only after measuring errors on your actual tickets; send uncertain or consequential cases to a review queue.
Agent action selection: Have an agent enumerate allowed actions, then ask Jev which option fits the observed state. Let the surrounding agent verify that the action is permitted, execute it, and check the outcome. Jev chooses from your options; it does not plan a whole task or run tools on its own.
Document triage: Classify an extracted text passage or score how closely it matches a rubric. If a document arrives as an image or PDF, extract its text first. Jev does not accept image, audio, or video input natively.
Both can participate in an agent workflow, but they expose different interfaces. Grok 4.7 is a generative model for coding, explanations, image understanding, and tool-using agents. Jev handles bounded judgments about text-shaped state. Treat the table as a task-selection guide, not a claim that either model wins the same benchmark.
| Decision factor | Jev Latest | Grok 4.7 |
|---|---|---|
| Best starting task | Pick an allowed action; classify or score supplied state | Write code or explanations; reason through broader work |
| Input | Text or structured text-shaped state | Text and images |
| Result | Typed Choice, Score, or Noul; probabilities | Generated text; structured output and tool calling are supported |
| Agent role | Decision component inside a larger workflow | Generative worker or reasoning component |
| Appropriate test | Accuracy, calibration, abstention policy, latency, cost per decision | Task completion, tool behavior, output quality, latency, total cost |
For a mixed workflow, a generative model can draft a reply after Jev has routed a ticket. The application still owns policy checks, review thresholds, and any final action. Evaluate both models on the same real records before moving traffic; their API prices and output types are not directly interchangeable.
Write one narrow question for each judgment. For a refund workflow, ask whether the customer requests a refund and which department should receive the ticket. Let code check order totals, eligibility dates, and authorization rules. TypeSafe documents weaknesses in arithmetic, counting, date comparisons, indirect questions, and long states filled with irrelevant material.
Define an other option for Choice when the allowed categories may not cover every case. Test common ambiguous inputs and adversarial text. Review the model's probabilities on labeled examples before selecting a threshold: a high confidence score is a signal for routing, not proof that the decision is correct. Log the question wording, returned version, decision, and later outcome so you can find regressions when an alias changes.
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