# Ollama now supports Jev-style decision models

> Source: <https://ollama.com/blog/ollama-now-supports-jev-style-decision-models>
> Published: 2026-09-30 09:15:33+00:00

# Ollama now supports Jev-style decision models

## September 29, 2026

Ollama now supports decision models, based on [TypeSafe’s Jev API](https://typesafe.ai) 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:

## Available models

Three new decision models are available to run via Ollama:

- [`nimble`](https://ollama.com/library/nimble) : open-source 9B parameter decision model developed by Bespoke Labs
- [`tev1`](https://ollama.com/library/tev1) : an experimental 4B decision model from Together AI
- [`tev1:0.8b`](https://ollama.com/library/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.

## Get started

To get started, first [download](https://ollama.com/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}
}
```

**Setup**

```
uv add typesafe-sdk  # or: pip install typesafe-sdk
export TYPESAFE_BASE_URL=http://localhost:11434
export TYPESAFE_API_KEY=ollama
export TYPESAFE_DEFAULT_MODEL=nimble
```

**Request**

``` python
from typesafe_sdk import Choice, Noul, Score, TypeSafeClient

ticket = "I was charged twice. Please refund the extra payment."
questions = {
    "team": Choice(
        instructions="Which team should handle this ticket?",
        criteria={
            "billing": "Payments and refunds",
            "technical": "Bugs and integrations",
            "other": "None of the above",
        },
    ),
    "refund": Noul(
        instructions="Does the customer explicitly ask for a refund?",
    ),
    "urgency": Score(
        instructions="How urgent is this ticket?",
        criteria=["Routine", "Soon", "Urgent"],
    ),
}

with TypeSafeClient(timeout=120) as client:
    result = client.system_one(
        state={"ticket": ticket},
        questions=questions,
    )

print(result.choices["team"].choice)   # billing
print(result.nouls["refund"].noul)     # 0.997
print(result.scores["urgency"].score)  # 0.815
```

## 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