---
title: "Jev (model)"
type: entity
tags: [jev, model, pricing, limits, system-one]
created: 2026-09-17
updated: 2026-09-17
confidence: high
sources:
  - raw/docs/models.md
  - raw/docs/introduction.md
  - raw/docs/model-jaggedness__jev-1.13.md
  - raw/site/blog-introducing-system-one.txt
jev_version: "jev-1.13.0"
summary: "Jev is TypeSafe's first System One model: text in, typed decisions with calibrated probabilities out, $0.042/MTok input, 64k context, 70-500 ms."
---

# Jev (model)

> **TL;DR** Jev is TypeSafe's flagship and first System One model. Current version `jev-1.13.0` (aliases `jev-latest`, `jev-preview`). Text-only input, typed decision output, $0.042 per million input tokens with output free, 64k total context (32k for `state` plus the longest question), 250,000 tokens/sec and 1,200 requests/min rate limits. It cannot generate text and by construction cannot return a value outside your schema — but it can return the wrong valid one.

## Facts

| Field | Value |
|---|---|
| Name | Jev — named after William Stanley Jevons ("a nod to Jevons Paradox") |
| Model class | System One model ([[concepts/system-one]]) |
| Current model ID | `jev-1.13.0` |
| Aliases | `jev-latest` → `jev-1.13.0` (most recent stable release; SDK default); `jev-preview` → `jev-1.13.0` (most recent release, stable or not; currently identical — "There is no preview build available right now") |
| Price | $42 per Btok / $0.042 per Mtok, charged on **input tokens only**; output tokens are free ("too cheap to meter") |
| Rate limits | 250,000 tokens per second; 1,200 requests per minute. Over either → `429 Too Many Requests`. Docs warn limits "can change without notice" |
| Context length | 64k tokens per request (state + all questions); 32k tokens for `state` plus the single longest question |
| Modality | Text only — string, JSON object, or array of text values. No image, audio, or video input |
| Endpoint | `POST /v1/systemone` (all models share it); `GET /v1/models` lists the aliases |
| Latency (claimed) | 70 ms – 500 ms end-to-end, versus "3 to 329 seconds" claimed for frontier LLMs |
| Availability | Early access, waitlisted, since 2026-09-15 |
| Customization | None per account: not fine-tuned or LoRA-adapted with customer data; "the same weights serve every account" |
| Training | RLCD — Reinforcement Learning for Calibrated Decisions |
| Data handling | "Jev is not trained on customer requests or responses"; ZDR available for enterprise |
| Languages | Accepts natural-language text; English is "the primary training language and where accuracy is currently best"; other languages including CJK "handled but not equally well" |

## What it does

Jev evaluates typed *questions* against one *state* and returns structured results — no text generation, no parsing. Three primitives ([[concepts/primitives]]):

| Question type | Goal | Returns |
|---|---|---|
| Choice | Choose an option from a list | `choice`, `probabilities`, `confidence` |
| Score | Score the state on a rubric | `score`, `probabilities`, `confidence` |
| Noul | Is this statement true? | `noul` (0–1) |

All three types can be mixed in one call. "Every *question* is evaluated in parallel and in isolation against the same *state* in one go. Adding questions barely changes the response time… so adding more questions does not create context-rot" (raw/docs/introduction.md). Jev ingests the `state` once and evaluates every question against it in parallel, which is why the context budget is split the way it is.

The launch blog's framing: "Think of Jev as a frontier-intelligence function call: unstructured state in, typed probabilistic decisions out." Sampling is parallel rather than autoregressive: "Generates all outputs in a single query."

## What it does not do

- **No string generation.** "While Jev gives up string generation, it's optimized for structured outputs and *can't* hallucinate" (blog). Concretely: possible outputs and structure are defined in advance and "The model never makes type errors." The blog is explicit that the 0% type-error figure "is not empirical. Schema matching is guaranteed, thus we can confidently add 0% into the plots."
- **No arithmetic, counting, or date ordering.** "Jev is not a calculator… `jev-1.13` does not count reliably." Dates should be extracted as components and compared in code.
- **No indirection-heavy reasoning.** `jev-1.13` "may struggle with tasks that require additional levels of indirection. It can be quite literal in its understanding. It struggles with tasks that require numeric precision."
- **No lookups.** Jev only knows the `state` you send.

The nine documented failure modes of `jev-1.13` — literal reading, math and numbers, date and time comparison, indirection, large state full of irrelevant detail, adversarial content, contradictory instructions and criteria, common-sense structural invariants, and generation — are catalogued in [[concepts/jaggedness-jev-1-13]] (jaggedness page "Last reviewed 2026-09-17").

Note the distinction the sources keep: schema safety is guaranteed; *judgment* is not. Jev can return a wrong but valid answer.

## Version history

The docs do not publish a model changelog. What the sources establish:

| Version | Evidence |
|---|---|
| `jev-1.12` | Used in cookbooks whose results are dated 2026-08-11 and 2026-08-12 (`TYPESAFE_MODEL = "jev-1.12"`, "$ per 1M tokens (input, output); TypeSafe jev-1.12 as of 2026-09") |
| `jev-1.13` / `jev-1.13.0` | Current release as of 2026-09-17; jaggedness page scoped to `jev-1.13`; both aliases resolve here |

Aliases move when a new release ships, so "the answers behind it can change without a change on your side." The response's `model` field reports the versioned ID that answered — pin the version if you have tuned confidence thresholds. See [[syntheses/version-timeline]].

## Related

- [[concepts/system-one]] — the model class
- [[reference/models-and-pricing]] — the full contract for prices, aliases, and limits
- [[reference/http-api]] — request and response shapes
- [[concepts/jaggedness-jev-1-13]] — failure modes in detail
- [[concepts/confidence]] — how to use the confidence channel
- [[entities/typesafe-ai]] — who makes it

## Sources

- raw/docs/models.md (https://docs.typesafe.ai/models)
- raw/docs/introduction.md (https://docs.typesafe.ai/introduction)
- raw/docs/model-jaggedness__jev-1.13.md (https://docs.typesafe.ai/model-jaggedness/jev-1.13)
- raw/site/blog-introducing-system-one.txt (https://typesafe.ai/blog/introducing-system-one-models-and-jev)