2 Integrations with Perplexity Decider

View a list of Perplexity Decider integrations and software that integrates with Perplexity Decider below. Compare the best Perplexity Decider integrations as well as features, ratings, user reviews, and pricing of software that integrates with Perplexity Decider. Here are the current Perplexity Decider integrations in 2026:

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    Qwen3.8-27B
    Qwen3.8-27B is a compact open-weights model in Alibaba’s Qwen3.8 family, aimed at developers and researchers who want strong local AI performance without using the full Max-scale model. Reports from Alibaba’s Qwen3.8 launch state that Qwen3.8-27B was planned for open-weight release alongside Qwen3.8-Max, expanding access for builders working on AI applications. The model is positioned for coding, research, professional workflows, and local deployment scenarios where a 27B model can be more practical than frontier-scale systems. Qwen3.8’s broader launch emphasizes software development, document processing, data analysis, and professional “cowork” use cases. Qwen3.8-27B is especially relevant for teams that need a capable open model for experimentation, coding agents, assistant workflows, and self-hosted inference. Built for practical deployment, Qwen3.8-27B gives developers a smaller Qwen3.8 option for building AI tools, testing agents, and running advanced language model workflows.
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    Perplexity Decisions API
    Perplexity’s Decisions API is built for fast, structured decisions that software can use directly. Instead of generating free-form text, it reads natural-language text, JSON, or images as shared state and returns typed answers with probabilities. Developers can attach one or more named questions to the same input and choose among three question types. noul handles yes-or-no judgments and returns the probability of yes from 0 to 1. choice selects from developer-defined options and returns the most likely option, the probability of every option, and a confidence score. score evaluates content against an ordered rubric and returns a probability-weighted score, the full probability distribution, and confidence. The API is designed for tasks such as classification, routing, grading against a rubric, severity assessment, sentiment analysis, or any decision that can be compared against a threshold.
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