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What is Tev1?

Tev1 is an advanced model designed for quick decision-making and classification, created by Together AI and fine-tuned from Qwen3.5 4B. It features an intuitive interface that allows users to enter a state, pose a question, and select from 2 to 24 labeled options, leading to an output of a single letter that corresponds to a semantic key via software. This innovative structure is particularly advantageous for diverse production tasks, including the routing of support tickets, evaluating automated returns, implementing policy guidelines, conducting sentiment analysis, categorizing documents, and embedding precise decision-making within extensive agent workflows. With its impressive 4.7B parameter count and a context window of 32.8K tokens, Tev1 leverages non-thinking generation to deliver swift and dependable classifications. The model has been honed using a broad collection of 37,840 examples that encompass intent detection, boolean comprehension, sentiment evaluation, policy enforcement, and the categorization of news articles and academic research. For optimal performance, it is recommended to set the temperature to 0 and maintain a low output budget, which ensures that the results remain both consistent and easy to understand. Furthermore, Tev1 is recognized as a highly adaptable tool that meets a variety of classification requirements effectively and efficiently. Overall, its capabilities make it an essential asset in any decision-making framework.

What is Perplexity Decisions API?

Perplexity's Decisions API is crafted for swift and organized decision-making, making it readily applicable for software solutions. Instead of generating unstructured text, it processes diverse inputs like natural-language sentences, JSON data, or images, creating a unified context from which it offers structured, typed responses that come with associated probabilities. Developers can enhance their queries by adding multiple named questions to a single input, with the option to choose from three unique question formats. The "noul" type facilitates straightforward yes-or-no assessments, providing a probability score for a "yes" answer that ranges from 0 to 1. The "choice" type enables the specification of different options, returning the most likely choice along with the associated probabilities for each alternative and a measure of confidence. The "score" type, on the other hand, evaluates content against a set rubric, yielding a probability-weighted score, a comprehensive probability distribution, and an associated confidence level. This API is well-suited for numerous applications, such as classification, routing, rubric-based grading, severity assessments, sentiment analysis, and any decision-making process that can be measured against a defined threshold, thus positioning it as an essential tool for developers. Furthermore, its systematic methodology not only increases the dependability of the decisions generated by the software but also guarantees that the results are both precise and stable, enhancing the overall user experience. Such reliability in decision-making can significantly contribute to the success of various projects.

Media

Media

Integrations Supported

Python
TypeScript
Qwen3.5

Integrations Supported

Python
TypeScript
JSON
OpenAI
Perplexity Decider

API Availability

Has API

API Availability

Has API

Pricing Information

$0.04 per 1M tokens
Free Version

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

Windows
Mac
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Together AI

Date Founded

2022

Company Location

United States

Company Website

www.together.ai/models/tev1-4b-experimental

Company Facts

Organization Name

Perplexity

Date Founded

2022

Company Location

United States

Company Website

docs.perplexity.ai/docs/decisions/quickstart

Categories and Features

AI Decision Models

Not specified

Categories and Features

AI Decision Models

Not specified

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