TypeSafe / typesafe/jev
Jev 1.13 - access through LLMTR
This model id stays on Jev 1.13, for integrations whose thresholds are tuned to that version; new work can start on typesafe/jev-1.14.0. It evaluates typed questions against a state you send (text, JSON or images) and returns one structured answer per question: a choice from your options, a score along your levels, or the probability that a statement is true. It generates no free text, and choices and scores come with their probability distribution. Images and the long-context mode are experimental features the provider can still change; the Structured Decisions documentation covers them with the request format and limits.
Technical specifications
| Canonical ID | typesafe/jev |
|---|---|
| Provider | TypeSafe |
| Context window | 32,000 tokens |
| Operations | SYSTEM_ONE |
| Modalities | text, image |
Pricing
An 8% platform margin applies to credit top-ups; model usage prices are not separately marked up.
| Operation | Metric | Unit | Price |
|---|---|---|---|
| SYSTEM_ONE | INPUT_TEXT | PER_1M_TOKENS | $0.042000 |
| SYSTEM_ONE | OUTPUT_TEXT | PER_1M_TOKENS | Not available |
Example usage
With existing OpenAI SDK flows, change only the base URL and model identifier.
curl https://llmtr.com/v1/chat/completions -H "Authorization: Bearer llmtr-your_key" -H "Content-Type: application/json" -d '{"model":"typesafe/jev","messages":[{"role":"user","content":"Hello"}]}'Guides about this model
- Jev 1.14 or Jev 1.13? Differences, pricing and migration - On LLMTR the two versions of TypeSafe's Jev sit under two ids: typesafe/jev-1.14.0 (Jev 1.14) and typesafe/jev, pinned to Jev 1.13. Both run on /v1/systemone at $0.042 per 1M input tokens and $0 for output; Box questions exist only on Jev 1.14, and the two versions can return slightly different probabilities for the same question.
- Choosing Noul, Choice, and Score with Jev - Explains how yes/no, unordered-option and ordered-level decisions map to Jev, the criteria format of each type, and how the three types are used together in one request.
- Routing support requests to the right team with Jev - Explains building support routing from one Choice question, supporting Noul questions and a confidence threshold, and how the routing is measured afterwards.
- Designing human approval with a Jev confidence threshold - Explains how confidence comes out of the probability distribution, why Noul needs a separate approach, and how thresholds are tuned on your own data.
- Multiple independent questions over one document with Jev - Calculates the cost difference between many questions in one request and a request per question, and explains the limits of independent questions, the role of question keys and the request limits.
- Explaining a Jev decision with a chat model - Explains a two-step flow that takes the decision from Jev and the user-facing text from a chat model, including the context sent to the chat model, data policy, and the balance of cost and latency.
- What is Jev? TypeSafe's choice-based decision model for browser agents - How TypeSafe's Jev model makes choice-based decisions, how jev-ultrafast applies that to browser agents, and the speed difference measured in the source project's own documentation, limits included.
- Running jev-ultrafast's text-generation step through LLMTR - Environment variable configuration, the actual request shape, and model-selection criteria for running jev-ultrafast's TYPE_TEXT step through LLMTR.
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