On October 1, 2026, Perplexity put its first decision model on Hugging Face: pplx-decider-v1-27b, Apache 2.0, 26B weights. A company known for search entered the arena not with another general LLM, but with the least glamorous and most expensive part of agent workflows: making decisions.
What is a decision model
As Cloudflare explained in its same-week release, a decision model classifies and judges: feed in a customer support message, get typed answers with probabilities — is it urgent, which team should handle it — and your code routes, escalates, or defers to a human directly. The division of labor with LLMs: LLMs generate openly but non-deterministically; decision models produce bounded, cheap, fast, consistent outputs. The category was ignited by the Jev model from Typesafe AI, and Cloudflare openly described the market as "increasingly saturated" — within one week, the GLiDE model from Fastino, the Clef model from Cloudflare, and the Decider model from Perplexity all shipped, with iteration measured in weeks.
The scorecard: edges Jev overall, wins on retrieval truth
The official model card benchmarks 11 tasks: overall 85.71%, slightly ahead of Jev at 84.51% and well above the Qwen3.8-27B base at 74.76%. The per-task pattern is more telling. Decider wins on RAGTruth (88.80% vs 77.27%, an 11-point lead), FinancialPhraseBank, TabFact, and Circa — tasks about judging given material. Jev holds WinoGrande, BBH, JudgeBench, TruthfulQA, and its own JevBench. A search company fine-tuned a model whose strongest suits are exactly retrieval-grounded judgment — data DNA shows. Caveat: these numbers were measured through the Perplexity API and published by Perplexity itself, so treat them as self-reported.
Open source, with a catch
Decider supports choice and noul output types, returns calibrated probabilities, and can take images for visual decisions. But 26B BF16 weights need roughly 49 GiB plus working memory, with a CUDA GPU and Python 3.12 as the floor; the model card showed only 165 downloads in the last month. In other words, this is a component for engineering teams with GPUs, not a drop-in API replacement.
So what
Decision models are becoming a distinct layer in the agent stack: routing, classification, guardrails — high-frequency small judgments that no longer need to consult a frontier LLM every time. The Perplexity entry says two things. First, judgment data accumulated by a search company converts directly into model advantage. Second, the window for this category will not stay open long; once API giants bundle decision-making into their platforms, open weights are the entry ticket. If you build agents, ask yourself: how many of your LLM calls are really just multiple-choice questions? Model card on Hugging Face.