Ollama 支持基于 Jev API 的决策模型，新增 nimble 等三款模型

September 29, 2026

Ollama now supports decision models, based on TypeSafe’s Jev API for fast, typed decisions:

No additional costs
Lower latency when run locally
Three new decision models available today via Ollama

This new API is available as of Ollama 0.35 by using the new /v1/systemone endpoint. Send text as state with a set of named questions, and a model running on your machine answers them all in one request. This is great for tasks that require fast decisions, such as ticket triage, model routing, and content or safety moderation.

Near-instant decisions

Decision models on Ollama are fast, as requests don’t have to travel over a network. Nimble 9B averaged 91ms per decision in the Pac-Man example below when running locally on an M5 Max. That’s fast enough to make rapid decisions such as playing a game or processing content in real time:

Move 1

Nimble 9B running on a MacBook Pro M5 Max, replayed at real-time speed.

Available models

Three new decision models are available to run via Ollama:

nimble: open-source 9B parameter decision model developed by Bespoke Labs
tev1: an experimental 4B decision model from Together AI
tev1:0.8b: an experimental 0.8B decision model from Together AI

More decision models are coming soon, including models served by Ollama’s cloud.

Decision models on Ollama

Bespoke Labs public benchmarks · accuracy, higher is better

Mean accuracy across 13 public data sets with human labels, covering 3,880 decisions. Nimble and Tev1 were evaluated on Ollama; Jev 1.13 is from Bespoke Labs' published run on the same decisions. See the Ollama evaluation results and benchmark suite.

Get started

To get started, first download or upgrade to the latest version of Ollama. Next, download a decision model such as nimble:

ollama pull nimble

You can make a request via curl or via TypeSafe’s official Python SDK.

Request

curl http://localhost:11434/v1/systemone -d '{
  "model": "nimble",
  "state": {
    "ticket": "I was charged twice. Please refund the extra payment."
  },
  "questions": {
    "team": {
      "type": "choice",
      "instructions": "Which team should handle this ticket?",
      "criteria": {
        "billing": "Payments and refunds",
        "technical": "Bugs and integrations",
        "other": "None of the above"
      }
    },
    "refund": {
      "type": "noul",
      "instructions": "Does the customer explicitly ask for a refund?"
    },
    "urgency": {
      "type": "score",
      "instructions": "How urgent is this ticket?",
      "criteria": ["Routine", "Soon", "Urgent"]
    }
  }
}'

Response

{
  "model": "nimble",
  "answers": {
    "team": {
      "type": "choice",
      "choice": "billing",
      "probabilities": {"billing": 0.985, "technical": 0.012, "other": 0.003},
      "confidence": 0.922
    },
    "refund": {"type": "noul", "noul": 0.997},
    "urgency": {
      "type": "score",
      "score": 0.815,
      "legend": {"0": "Routine", "1": "Soon", "2": "Urgent"},
      "probabilities": {"0": 0.378, "1": 0.429, "2": 0.193},
      "confidence": 0.046
    }
  },
  "usage": {"input_tokens": 841, "output_tokens": 4}
}

What’s next

This is the first of many releases to come adding decision model support to Ollama. Future updates will include:

Faster performance on Apple Silicon powered by MLX
More models specializing in different kinds of decision making