Ollama now supports Jev-style decision models
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:
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.
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}
}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=nimbleRequest
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) # 0.815What’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