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What is Parallel Decisions API?

The Parallel Decisions API serves as a web-based classification solution tailored for developers engaged in software creation, data process improvement, and workflow development for AI agents. When a user submits a request, it comprises a specific context accompanied by one or more questions, and the API responds with structured answers that include probabilities and citations from pertinent online sources. This feature enables the grouping of several questions under a single context in one request, which supports various use cases like vendor evaluations, lead management, compliance assessments, content moderation, and the formulation of operational guidelines for agents. By harnessing real-time web data, it can assess facts that may have changed since a model's last training session. Developers can interact with the API through a RESTful endpoint using an API key or by employing the web_decisions tool via Parallel's MCP server. To aid testing and integration, a hosted playground along with detailed documentation is provided for user ease. Additionally, this API enhances decision-making for developers by supplying current information that boosts the dependability of their applications, ultimately leading to more accurate outcomes in diverse scenarios. Through this capability, developers are equipped to create more robust and adaptive software solutions.

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

Claude Code
Model Context Protocol (MCP)
Parallel

Integrations Supported

API Availability

Has API

API Availability

Pricing Information

$0.001/request
$0.001 per API request; a request can include up to 100 questions.
Free Version

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

Not specified

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Parallel

Date Founded

2023

Company Location

United States

Company Website

platform.parallel.ai/play/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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