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What is Jev?

Jev is a low-latency System One Model from TypeSafe AI built for structured decision-making rather than open-ended text generation. TypeSafe describes System One Models as a new class of frontier models intended to make fast decisions that can be consumed directly by application code. Jev takes unstructured information as input and returns predefined typed outputs with probabilities and confidence scores instead of generating unrestricted natural-language strings. Its parallel sampling architecture produces outputs in a single query rather than sequentially generating one token at a time, which is designed to reduce latency and inference cost. The model is trained with Reinforcement Learning for Calibrated Decisions, which focuses on producing well-calibrated probabilities for structured System One tasks. TypeSafe positions Jev as a software-native intelligence layer for tasks including classification, routing, scoring, extraction, decision branching, and other workflows that benefit from probabilistic logic. It can also be applied to model evaluation and safety workflows by scoring, judging, verifying, guardrailing, or detecting jailbreaks in prompts, reasoning traces, and generated outputs. The company reports service response times of roughly 70 to 500 milliseconds and says this can make Jev suitable for interactive and real-time applications where traditional frontier models may introduce too much latency. Jev is designed to return consistent schemas without type errors, allowing its results to be incorporated into code without the same parsing and validation steps commonly required for free-form LLM responses. TypeSafe also highlights map-reduce style processing over large datasets as a potential use case, where the model can turn large amounts of unstructured information into structured features and insights.

What is Gemini Decision 1?

Gemini Decision 1 is a purpose-built artificial intelligence model developed by Google to support automated decision-making, classification, and evaluation within software systems. Rather than functioning as a conventional AI chatbot, the model examines supplied information and returns structured predictions based on questions defined by the developer. Its supported decision types include Boolean evaluations, multiple-choice classifications, and numerical scoring against predefined criteria. The system provides probability distributions and confidence information that applications can use to determine how strongly the available evidence supports a particular outcome. With a context capacity of approximately 262,000 tokens, Gemini Decision 1 can analyze extensive documents, records, and other contextual inputs. Developers can implement the model for use cases such as customer service ticket routing, content moderation, document classification, lead qualification, and automated quality assessments. It can also serve as a decision component within AI agent architectures by evaluating task conditions and selecting appropriate actions from predefined choices. The model supports integration through API-based development workflows, including access through Vercel AI Gateway. Its specialized output format allows applications to process model predictions programmatically without extracting decisions from lengthy generated explanations. By combining large-context analysis with probabilistic classification and scoring, Gemini Decision 1 enables organizations to incorporate structured AI evaluations into business processes and intelligent applications.

Media

Media

Integrations Supported

Integrations Supported

Gemini
Gemini platform
Google AI Studio
Google Cloud Platform

API Availability

API Availability

Has API

Pricing Information

Input: $0.042 / 1M tokens
Input tokens: $0.042 / 1 million tokens ($42 per billion tokens).

Output tokens: FREE (too cheap to meter).

Pricing Information

$0.10 per 1M tokens (input)
$0.10 per 1 million tokens (input), no charge for output tokens
Free Version

Supported Platforms

SaaS

Supported Platforms

SaaS

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

TypeSafe AI

Date Founded

2024

Company Location

United States

Company Website

typesafe.ai/

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

aistudio.google.com/docs/models/gemini-decision-1

Categories and Features

AI Decision Models

Not specified

AI Models

Not specified

Categories and Features

AI Decision Models

Not specified

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