# Perplexity's Decisions API Answers With Probabilities Instead of Text, at $0.04 per Million Input Tokens. What It Does and Where It Stops

Send content plus up to 128 named questions and get back yes/no probabilities, choice distributions, or scored rubrics. Output tokens are free, and only one model is available.

By Priya Nair (AI Industry Reporter) — published 2026-10-03, updated 2026-10-03
Source: https://aiscoutdaily.com/news/perplexity-decisions-api-probabilities-not-prose

## TL;DR
Perplexity's Decisions API, documented for the model pplx-decider-v1-27b, returns probabilities rather than generated text. You send content and 1 to 128 named questions of three types: yes/no, multiple choice, or a scored rubric. Input costs $0.04 per million tokens and output tokens are free. Requests can be up to 262,144 tokens, with a rate limit of 10 requests per second per organisation. Perplexity's documentation does not state a benchmark score; an 85.71% figure circulating in news roundups could not be traced to a method.

Most language model APIs return text, and developers then parse that text to decide what to do. Perplexity's Decisions API skips the parsing: you ask a question about your content and get back a number.

## How it works

You POST to https://api.perplexity.ai/v1/decisions with the model pplx-decider-v1-27b, a state (text, a JSON object, or an array, which can include base64-encoded images), and 1 to 128 named questions, each with a type and criteria. The response contains an answer per question name.

| Question type | What you ask | What comes back |
| --- | --- | --- |
| Yes/no | Does this content meet a condition? | A probability from 0 to 1 |
| Choice | Which of your options fits? | The most likely option, probabilities for every option, and a confidence value |
| Score | Where does this land on your rubric? | A probability-weighted average level, the full distribution, and a confidence value |

*As documented by Perplexity.*

## Price and limits

- Price: "$0.04 per million input tokens. Output tokens are free." Input tokens are reported in the response's usage.input_tokens field.
- Context: under 262,144 input tokens per request, with a 32 MiB maximum body.
- Images: PNG, JPEG, and WebP, sent only as base64 data URLs (no HTTP or HTTPS links). Perplexity documents 2,048 tiles of 32 by 32 pixels, at about 1,000 tokens per megapixel.
- Rate limit: 10 requests per second per organisation.
- Models: only pplx-decider-v1-27b is available.

## What it isn't telling you

The quickstart we read does not give accuracy figures or a benchmark. AI Weekly's roundup cites an 85.71% score for the model, but we couldn't find that number in Perplexity's documentation or a description of how it was measured, so we aren't relying on it. Probabilities from a model are not guarantees; the usable test is whether they are calibrated on your content, which you have to check yourself.

## What this means if you build with models

Good fits are the jobs where you currently ask a model for yes, no, or a label and then parse the sentence: routing support tickets, flagging policy violations, grading drafts against a rubric. Because you get a probability, you can set your own threshold and send borderline items to a person. At $0.04 per million input tokens with free output, a pilot over a few thousand of your own documents is cheap; run it against a labelled sample before wiring it into anything that acts automatically.

## Sources
- [Perplexity Docs: Decisions API quickstart](https://docs.perplexity.ai/docs/decisions/quickstart.md)
- [AI Weekly: AI News Today, October 2](https://aiweekly.co/ai-news-today)