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What is OpenAI Decisions API?

The Decisions API is engineered to convert text and images into structured decisions that can be utilized by applications, boasting a processing speed approximately ten times faster than the Responses API. To use it effectively, developers are required to submit a model along with shared input evidence and one or more questions that define the evaluation parameters, after which the API provides organized responses that are ideal for various tasks such as content classification, request routing, prioritizing work, or making decisions based on established thresholds. It supports three types of inquiries: predicate questions, which determine the truth of a condition and yield a probability between 0 and 1; choice questions, which select a single option from a specified set while offering corresponding probabilities and confidence levels; and score questions, which evaluate an input against a series of ordered rubric levels, resulting in a probability-weighted score. The API's flexibility allows it to handle inputs as simple text or as user messages that incorporate both text and images, making it suitable for a wide array of scenarios including detecting visible product damage, interpreting request intent, classifying various document types, assessing the severity of issues, or gauging answer relevance. By enabling this level of processing, the system significantly enhances the ability of developers to build applications that are not only more responsive but also smarter, ultimately catering better to the needs of users. Furthermore, this advanced capability means that applications can adapt to changing user requirements in real-time, further increasing their effectiveness.

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.

Media

Media

Integrations Supported

GPT-6 Luna
OpenAI

Integrations Supported

API Availability

Has API

API Availability

Pricing Information

Pricing not provided

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).

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

OpenAI

Date Founded

2015

Company Location

United States

Company Website

developers.openai.com/api/docs/guides/decisions

Company Facts

Organization Name

TypeSafe AI

Date Founded

2024

Company Location

United States

Company Website

typesafe.ai/

Categories and Features

AI Decision Models

Not specified

Categories and Features

AI Decision Models

Not specified

AI Models

Not specified

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