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Description

Jev is TypeSafe AI’s first public System One Model, a class of AI designed to make fast, structured decisions that software can consume directly. Instead of generating arbitrary strings like a traditional large language model, Jev produces predefined type-safe values accompanied by calibrated probabilities and confidence estimates. Its architecture generates outputs in parallel rather than autoregressively producing one token at a time, allowing the model to prioritize speed and computational efficiency. TypeSafe trains Jev using Reinforcement Learning for Calibrated Decisions, an approach intended to optimize for accurate uncertainty estimates and consistent structured outputs. The model can be embedded into conventional software as an intelligent decision layer for classification, scoring, routing, extraction, branching, and other tasks where hand-written rules would be too rigid. Jev can also be used to judge, verify, guardrail, or detect problematic behavior in outputs from other AI systems. TypeSafe reports typical end-to-end response times between 70 and 500 milliseconds and positions the model for applications where low latency is important. The company also emphasizes schema guarantees, meaning Jev’s outputs are constrained to the structures defined by the application rather than requiring developers to parse and validate unrestricted generated text. Jev is aimed at developers and organizations building automation, real-time software, large-scale data workflows, and production systems that require dependable structured AI decisions.

Description

Mercury 2 represents a groundbreaking advancement in reasoning models, specifically designed for real-time voice interaction as it can quickly answer phone calls. Unlike traditional autoregressive models that leave callers in silence while generating responses one token at a time, Mercury 2 employs a diffusion large language model architecture capable of producing over 1000 tokens per second with standard NVIDIA GPUs. This remarkable speed allows it to complete a full reasoning process and begin speaking within a timeframe that aligns with natural conversational flow, effectively shortening the typical wait time from several seconds to approximately 300 milliseconds. The operational mechanism of Mercury models involves transforming clear text into noise, after which a conventional Transformer is trained to reverse this transformation and predict the original text across all positions at once. By utilizing a denoising approach that engages multiple tokens simultaneously, generation becomes more efficient, enabling speeds akin to custom silicon on NVIDIA H100s while improving responsiveness in voice applications. As a result, Mercury 2 not only enhances user experience but also sets a new standard for interactive voice technologies.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Cerebras
GPT-4.1
Groq
Inception Labs
LiveKit
OpenAI
Pipecat
Retell AI
Vapi AI

Integrations

Cerebras
GPT-4.1
Groq
Inception Labs
LiveKit
OpenAI
Pipecat
Retell AI
Vapi AI

Pricing Details

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

Output tokens: FREE (too cheap to meter).
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

TypeSafe AI

Founded

2024

Country

United States

Website

typesafe.ai/

Vendor Details

Company Name

Inception

Country

United States

Website

www.inceptionlabs.ai/blog/mercury-2-the-first-reasoning-model-fast-enough-to-pick-up-the-phone

Product Features

Product Features

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