Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Ratings and Reviews 0 Ratings

Total
ease
features
design
support

This software has no reviews. Be the first to write a review.

Write a Review

Alternatives to Consider

  • AnalyticsCreator Reviews & Ratings
    46 Ratings
    Company Website
  • LM-Kit.NET Reviews & Ratings
    29 Ratings
    Company Website
  • Ethena Reviews & Ratings
    131 Ratings
    Company Website
  • HERE Enterprise Browser Reviews & Ratings
    2 Ratings
    Company Website
  • Gemini Enterprise Agent Platform Reviews & Ratings
    1,161 Ratings
    Company Website
  • Admin By Request Endpoint Privilege Management Reviews & Ratings
    105 Ratings
    Company Website
  • Canditech Reviews & Ratings
    111 Ratings
    Company Website
  • Dynamo Software Reviews & Ratings
    71 Ratings
    Company Website
  • RouteGenie Reviews & Ratings
    49 Ratings
    Company Website
  • Planview ProjectAdvantage Reviews & Ratings
    121 Ratings
    Company Website

What is Microsoft-Decision-1?

Microsoft-Decision-1 is a highly effective decision-scoring model specifically designed for structured outputs, allowing software systems to take swift actions. This innovative model supports a range of functionalities including routing, classification, prioritization, verification, and workflow management, applicable across various applications, agents, and automated systems. Developed through post-training of Qwen3.5-9B, it excels in single-pass decision scoring and delivers calibrated probabilities for a defined set of response choices. The model is adept at addressing yes/no inquiries, multiple-choice situations, rating scales, and rubric-based assessments of AI-generated outputs and agent performances via a structured API. Comprehensive evaluations carried out by Microsoft across 36 benchmarks covering routing, ranking, long-context understanding, multilingual functions, and tasks outside conventional distributions demonstrate that it achieved top accuracy levels with impressively low latency. Additionally, the model places a strong emphasis on robustness, with extensive testing conducted to ensure decision-making stability even when prompts undergo rephrasing or modifications. This commitment to stability highlights its potential for delivering reliable performance in diverse real-world scenarios, making it a versatile tool for various industries.

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

Microsoft Foundry
OpenRouter
Qwen3.5

Integrations Supported

API Availability

Has API

API Availability

Pricing Information

$0.042 per 1M tokens

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

Microsoft

Date Founded

1975

Company Location

United States

Company Website

commandline.microsoft.com/microsoft-decision-1-model-foundry/

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

Popular Alternatives

Clef-flash Reviews & Ratings

Clef-flash

Cloudflare

Popular Alternatives

Clef-flash Reviews & Ratings

Clef-flash

Cloudflare
Clef Reviews & Ratings

Clef

Cloudflare
Clef Reviews & Ratings

Clef

Cloudflare
Jev Reviews & Ratings

Jev

TypeSafe AI
Prisma ORM Reviews & Ratings

Prisma ORM

Prisma.io