Jev (TypeSafe AI) logo

Jev (TypeSafe AI)

System One model that returns typed decisions with calibrated probabilities instead of text

4
freemium closed-source Development Verified Sep 21, 2026

API

Available

Mobile App

No

Users

Early access (waitlist)

Free tier

Free playground with login; early-access waitlist at typesafe.ai

Paid plans

$42 per billion input tokens ($0.042/M); output tokens free

Integrations

Vercel AI Gateway Cloudflare Workers AI LiteLLM Pydantic AI OpenRouter Netlify AI Gateway

Key Features

  • Typed decisions (Choice, Score, Noul) with calibrated probabilities
  • No text generation, no parsing needed
  • 70-500ms end-to-end response time
  • Many questions evaluated in parallel per call
  • Confidence estimates on every answer
  • Cost from $0.042/M input tokens, free output tokens

Overview

Jev is TypeSafe AI’s first System One model — a new class of AI model purpose-built for decisions inside software, not text generation. You send it a state (any text) plus a set of typed questions, and it returns structured answers — a boolean probability, a choice from a list, or a rubric score — each with a calibrated confidence value. There’s no parsing, no prompt scaffolding, and no hallucinated prose: the model’s output is constrained to exactly the schema you declare. It launched in public early access on September 15, 2026.

Read our full Jev AI review.

Key Features

  • Three primitives: Choice (pick an option), Score (rate on a rubric), Noul (0–1 truth probability) — mix them freely in one call
  • Parallel evaluation: every question runs against the same state in a single pass; adding questions barely changes latency
  • Calibrated confidence: every answer reports how sure the model is, so your software can act at high confidence and escalate at low confidence
  • No hallucinations structurally: outputs can’t be malformed, undeclared, or wrong-typed — the schema is enforced at the model level
  • Fast and cheap: 70–500ms latency, about 193x faster and 440x cheaper than frontier LLMs on System One-style tasks

Use Cases

  • Support triage: classify tickets by department, urgency, and refund-triggering sentiment in one call
  • Agent orchestration: decide which tool or subagent to invoke next, or whether to continue, retry, or stop
  • Guardrails and moderation: verify model outputs, detect policy violations, and block risky actions
  • Scoring and ranking: rate fit, risk, or quality with a custom rubric
  • Routing: choose the right pipeline, provider, or queue per request
  • Email and message triage: flag urgency, intent, spam-likelihood, and sentiment

Pricing

  • Input tokens: $42 per billion ($0.042/M) — charged per input token
  • Output tokens: free (the model returns tiny structured answers)
  • Rate limits: 250,000 tokens/second, 1,200 requests/minute
  • Context: 64k tokens per request (32k for state plus the longest question)
  • Access starts on the early-access waitlist at typesafe.ai; API keys are issued in console.typesafe.ai
Visit Website

Reviews & Comments

Sign in with GitHub to review or comment on this tool.

Related Development

More tools in the same category

Google Colab logo

Google Colab

4.7

Free cloud-based Jupyter notebook environment for Python and machine learning

freemium closed-source development
LangChain logo

LangChain

4.7

Framework for developing applications with large language models

free open-source development
LangFuse logo

LangFuse

4.7

Open-source observability and analytics platform for LLM applications

freemium open-source development