pplx-decider is a 27B decision model from Perplexity, fine-tuned from Qwen3.8-27B. Give it a state (text, JSON, or images) and a set of typed questions, and it returns a calibrated probability for every answer in a single forward pass. It doesn’t generate text.

It’s compatible with the Jev and System One APIs, so it works through Ollama’s /v1/systemone endpoint and ollama run --ask.

ollama pull pplx-decider

Highlights

• Accurate: 85.7% across 11 public benchmarks in Perplexity’s evaluation, ahead of Jev (84.5%) and its Qwen3.8-27B base (74.8%).
• Multimodal: decide over text, JSON, or images such as screenshots, receipts, and forms.
• One forward pass: every option of every question is scored together, with no output tokens.
• Apache 2.0 license

What you can build

CLI

ollama run pplx-decider --ask "Is a refund being requested?" < ticket.txt

ollama run pplx-decider --ask "Which team should handle this?" \
  --choice billing="Payments and refunds" \
  --choice technical="Outages and errors" < ticket.txt

ollama run pplx-decider --ask "How urgent is this?" \
  --level "Routine" \
  --level "Soon" \
  --level "Immediate" < ticket.txt

The state can also follow the model name instead of coming from stdin.

API

Decision models use Ollama’s /v1/systemone endpoint, documented here. Put the text you want judged in state and your questions in questions. Ollama builds pplx-decider’s prompt for you.

Every question has a type, instructions, and usually criteria:

confidence runs from 0 to 1 and shows how strongly the probabilities favor one answer. It isn’t the chance that the answer is right.

cURL

curl http://localhost:11434/v1/systemone -d '{
  "model": "pplx-decider",
  "state": "Our app crashes on launch for every user since this morning'\''s update. Please fix it now.",
  "questions": {
    "urgent": {"type": "noul", "instructions": "Is this support request urgent?"},
    "team": {
      "type": "choice",
      "instructions": "Which team should handle this request?",
      "criteria": {
        "billing": "Payments, invoices, and refunds",
        "technical": "Outages, errors, and configuration",
        "sales": "Plans and upgrades"
      }
    },
    "severity": {
      "type": "score",
      "instructions": "How severe is the customer impact?",
      "criteria": ["No impact", "Minor", "Major", "Critical"]
    }
  }
}'

Response:

{
  "model": "pplx-decider",
  "answers": {
    "urgent": {"type": "noul", "noul": 0.994},
    "team": {
      "type": "choice",
      "choice": "technical",
      "probabilities": {"billing": 0.002, "technical": 0.996, "sales": 0.002},
      "confidence": 0.994
    },
    "severity": {
      "type": "score",
      "score": 2.975,
      "legend": {"0": "No impact", "1": "Minor", "2": "Major", "3": "Critical"},
      "probabilities": {"0": 0.001, "1": 0.002, "2": 0.017, "3": 0.98},
      "confidence": 0.975
    }
  },
  "usage": {"input_tokens": 338, "output_tokens": 0}
}

Python

Use TypeSafe’s official Python SDK and point it at Ollama. The SDK requires an API key, but Ollama ignores it, so any value works.

pip install typesafe-sdk
export TYPESAFE_BASE_URL=http://localhost:11434
export TYPESAFE_API_KEY=ollama
export TYPESAFE_DEFAULT_MODEL=pplx-decider

from typesafe_sdk import Choice, Noul, Score, TypeSafeClient

questions = {
    "urgent": Noul(instructions="Is this support request urgent?"),
    "team": Choice(
        instructions="Which team should handle this request?",
        criteria={"billing": "Payments, invoices, and refunds", "technical": "Outages, errors, and configuration", "sales": "Plans and upgrades"},
    ),
    "severity": Score(instructions="How severe is the customer impact?", criteria=["No impact", "Minor", "Major", "Critical"]),
}

with TypeSafeClient(timeout=120) as client:
    result = client.system_one(state="Our app crashes on launch for every user since this morning's update. Please fix it now.", questions=questions)

print(result.choices["team"].choice)  # technical

Images

curl http://localhost:11434/v1/systemone -d '{
  "model": "pplx-decider",
  "state": "The agent wants to submit the attached form.",
  "images": ["<base64-encoded image>"],
  "questions": {
    "complete": {"type": "noul", "instructions": "Are all required fields filled in?"},
    "kind": {
      "type": "choice",
      "instructions": "What kind of document is this?",
      "criteria": {"invoice": null, "receipt": null, "contract": null, "other": null}
    }
  }
}'

Benchmarks

Accuracy reported by Perplexity, measured through the Perplexity API. See the model card for all 11 benchmarks.

License

Apache 2.0