# Jev

Returns typed, confidence-scored decisions for code instead of text.

- Category: Developer Tools
- Pricing: From $42
- Tags: API, Machine Learning, Automation Tools, Workflow Automation
- Website: https://typesafe.ai/?via=aigregator
- Aigregator page: https://aigregator.com/tools/jev
- API: https://x402.aigregator.com/v1/tools/jev

## Overview
Jev is the first publicly available model from TypeSafe AI, a San Francisco company founded in 2024 by Diogo Almeida, who co-invented the RLHF and InstructGPT methods behind ChatGPT while at OpenAI. Instead of generating text, Jev takes a block of unstructured state plus a set of typed questions and returns structured answers, each carrying a calibrated probability and a confidence value. TypeSafe calls this class of model a System One Model, trained with a method it names Reinforcement Learning for Calibrated Decisions.

The intended user is a developer placing a judgement call inside ordinary code: classifying, routing, scoring, extracting, or branching where hand-written rules are too brittle. Because the output schema is fixed in advance, the model cannot return a type error, so code can act automatically above a chosen confidence threshold and escalate below it. Jev is served from a single endpoint with Python and JavaScript SDKs, priced at $0.042 per million input tokens with output tokens free.

The trade-off is deliberate. Jev writes no text at all, so it cannot chat, draft, or produce code, and it accepts text input only. It is in early access behind a waitlist, and the headline speed and cost comparisons published alongside it are the company's own benchmarks.
## Key Features
- Typed outputs: Choice, Score and nested structures defined in advance
- Calibrated probability and confidence score on every answer
- Parallel sampling: all questions evaluated against one state in a single request
- Single endpoint (POST /v1/systemone) with model aliases jev-latest and jev-preview
- 64k token context per request; 32k for state plus the longest question
- Rate limits of 250,000 tokens per second and 1,200 requests per minute
- Python and JavaScript/TypeScript SDKs with automatic retry and backoff
- Open-source adapter that wraps LLMs in the same System One interface

## Use Cases
- Route, classify or score inputs inside application code instead of hand-written rules
- Gate automation on confidence, acting when high and escalating for review when low
- Extract structured fields from unstructured text at low cost per call
- Score, judge and guardrail the prompts, reasoning traces and outputs of other LLMs
- Add AI decisions to real-time features where response time matters

## Pricing Plans
- Jev API: $42/credit

## Alternatives
- [Cohere](https://aigregator.com/tools/cohere)
- [Mistral AI](https://aigregator.com/tools/mistral-ai)
- [Claude](https://aigregator.com/tools/claude)

---
Source: Aigregator — AI tools directory. https://aigregator.com/tools/jev