OneJev1.5-9B is the 9B model of OneJev 1.5, a multimodal System One decision model. Give it a screenshot, a photo, a video or plain text along with a few typed questions, and it returns a calibrated probability for every option in a single forward pass. It is a full fine-tune of Qwen/Qwen3.5-9B on about 320,000 questions drawn from real agent runs, videos, images and the training splits of public decision datasets.
Decision Index
On the public part of Decision Index 0.3 (37 benchmarks, 140,178 requests, run and scored with the board's own kit), OneJev1.5-9B scores 59.03, the highest of any model up to 10B parameters on the board.
It also scores above 31 of the 43 models of 25B to 36B parameters on the board.
Other models' scores are their public-part scores on the board on 12 October 2026.
Results
Accuracy in percent. The OneJev test set holds questions of the training kinds that no model saw in training. Jev 1.13's scores are its published ones, and it reads text only.
Quick start
pip install "qev[torch] @ git+https://github.com/OmniJev/OneJev.git"
qev serve --model OmniJev/OneJev1.5-9B
from qev import Client, Choice, Noul
from qev.media import data_uri
r = Client("http://localhost:8000").system_one(
state={"task": "Pay the open invoice from ACME", "screen": "<image:1>"},
media=[{"type": "image", "data": data_uri("screenshot.png")}],
questions={"done": Noul("The invoice has been paid"),
"next": Choice("What should the agent do next?", {"click": "click an element", "stop": "stop"})},
)
The server speaks TypeSafe's System One API plus a media field for images and video. More examples and the code are on GitHub.
Citation
@misc{onejev2026,
title = {{OneJev}: A Multimodal System One Decision Model},
author = {{OmniJev Team}},
year = {2026},
howpublished = {\url{https://github.com/OmniJev/OneJev}}
}
License
Apache 2.0