Perplexity has released two new "decision models." These are general-purpose classifiers built from generative LLM backbones. You give them an input (the "state") and one or more questions, and they return a probability distribution over the answer options. They don't generate text token by token. Models pplx-decider-v1.3: fine-tuned from Qwen3.8-27B. Needs about 49 GiB for the BF16 weights. pplx-decider-flash-v1.3: Qwen3.5-4B, distilled from the 27B using a KL objective on the teacher's distributions. Needs about 8.5 GiB for the BF16 weights. Both are Apache 2.0, support text and images, and are also available through OpenRouter and Perplexity's API. What it does Question types: yes/no, multiple choice (up to 255 options), ordered scores, and a new locate type. locate extracts verbatim spans with character offsets, for NER, PII detection, and extractive QA. Several questions can share one state. By default each question runs in its own sequence. An "isolated" mode reads the state once and is cheaper for many questions, but answers can shift slightly depending on which other questions are in the request. The backbone's full-attention layers were made bidirectional. Because of that, you need their pplx_decider package; plain causal inference won't reproduce the outputs. Training used sequences up to about 30k tokens. New in v1.3: locate Besides classifying, v1.3 can now extract spans. You add a question with "type": "locate" and describe the field, either with a short label (“person”, “email address”) or an instruction (“the person who signed the contract”). The model returns verbatim spans from the state, each with start and end character offsets and a score, or an empty list if nothing matches. It works on text only (no images), with up to 128 locate questions per request. It runs on a separate LoRA adapter plus a span head, which Perplexity left unmerged because merging hurt classification. As a result, a request that mixes locate and decision questions needs extra forward passes. On 17 out-of-domain NER, PII, extractive-QA and structured-extraction tasks, Perplexity reports an average of 69.0 (v1.3) and 63.6 (Flash), compared with 43.3 for Liquid d1, 41.5 for Cloudflare Clairic and 33.6 for GLiNER large v2.5. The closest existing tools are GLiNER, which does zero-shot span extraction from labels, and Clairic, which extracts typed values rather than verbatim spans. Reported results (mostly Perplexity's own evals) Multilingual classification across 8 languages: 88.16% (v1.3) and 86.50% (Flash), vs. 85.41% for TypeSafe's Jev 1.14. Reranking across 72 retrieval tasks: 83.99 nDCG@10, +1.81 over Qwen3-Reranker-8B. Locate average across 17 out-of-domain tasks: 69.0 / 63.6, vs. 43.3 for Liquid d1 and 33.6 for GLiNER large v2.5. Artificial Analysis Decision Index: roughly 70 for v1.3 at about $68 per 1M tasks, and roughly 64 for Flash at about $34. Both sit on the cost/score Pareto line. A few models score higher at higher cost, such as Drex Max v1.1 and SubQ Snap 1.2. https://preview.redd.it/hbrwsf96od1i1.png?width=2464&format=png&auto=webp&s=5a7025b4cda1dc955fbe1e3329cd7b0d07c75fe2 Caveats from the authors The locate adapters are kept unmerged, because merging them regressed the decision tasks. The returned probabilities should be calibrated and thresholded on your own data. Links Blog: https://www.perplexity.ai/hub/blog/perplexity-decider HF collection: https://huggingface.co/collections/perplexity-ai/decider-v13 https://huggingface.co/perplexity-ai/pplx-decider-v1.3 https://huggingface.co/perplexity-ai/pplx-decider-flash-v1.3 AA Decision Index: https://artificialanalysis.ai/evaluations/artificial-analysis-decision-index   submitted by   /u/Balance- [link]   [comments]