Perplexity announced two new decision models in a blog post on 12 November. Decider v1.3 is based on Qwen3.8-27B. The smaller Decider Flash v1.3 was trained from Qwen3.5-4B to imitate the larger model’s answers.

The model does not write an answer; it returns probabilities. Users give it text or an image together with questions and their answer options. A question can be a yes-or-no choice, multiple choice or an ordered scale. Perplexity itself uses the model to filter web pages by quality and to check AI agents’ security logs, for example.

How Decider works

Perplexity turns a large language model into a classifier. A decision head replaces next-token prediction and reads the answer at the position of a special marker token. Some of the model’s layers read the input in both directions. In version 1.3, Perplexity isolated the questions from one another so that an answer depends less on what else is asked at the same time.

The model card on Hugging Face says that the model’s software library nevertheless runs each question separately by default. In that case, other questions do not affect the answer. The shared run used in training is cheaper when many questions share the same long input, but in that mode 1.5–2.2 per cent of top answers differ from the default mode, the card says.

The new Locate feature extracts requested spans, such as personal names or personal data, verbatim with their character positions. Perplexity keeps the feature as a separate add-on because merging it into the model weakened ordinary classification answers. Locate works only with text. If a request contains both extraction and classification questions, the model needs a separate compute pass for each, which adds latency.

The company’s own results

The figures are Perplexity’s own measurements. Unite.AI’s report is based on the company’s blog and does not verify them independently.

Perplexity tested search-result reranking on 72 tasks. The metric was nDCG@10, which measures how well the most relevant results rise into the top ten. Decider v1.3 scored a mean of 83.99 points. That is 1.81 points more than Qwen3-Reranker-8B and 2.24 more than TypeSafe AI’s Jev 1.14, which Perplexity names as Decider’s inspiration.

On 63 search-query classification tasks, mean accuracy was 91.6 per cent. That is 6.54 percentage points higher than Perplexity’s current classifiers, but a large language model had labelled the correct answers. In a sample of 1 million real API requests, v1.3 agreed with the language-model judges on 93.98 per cent of questions, v1.2 on 93.68 per cent and Jev 1.14 on 93.35 per cent. The figure measures agreement with another model, not hits against a verified correct answer.

Unite.AI reported that Perplexity put the cost of the same sample at $26.47 for v1.3 and $13.24 for Flash. Perplexity ran Decider on its own hardware. The figures do not show how hardware costs were calculated, and latency was not measured side by side.

Availability and requirements

Perplexity says the models can be downloaded from Hugging Face under the Apache licence and can also be used through OpenRouter and the company’s Decisions API. However, the model card, created on 10 November before the announcement, describes the repository as private, and downloading requires a token with access to it.

The large model needs a CUDA-compatible GPU with room for about 49 GiB of BF16 weights plus working memory. The model card says v1.3 requires its own request format: the prompt format of earlier versions should not be reused because the answers change. Perplexity advises tuning decision thresholds on a small labelled dataset of your own, because the right threshold varies by use case.