GLM-5.3 is live. Run Z.ai's latest model on Friendli Model APIs. Try it today ➜
OmniJev
Available on FriendliAI
Run this model inference on single tenant GPU with unmatched speed and reliability at scale.
Model Details
Model Provider
OmniJev
Model Tree
Qwen/Qwen3.5-4B
Input Modalities
Output Modalities
Supported Functionality
On the public part of Decision Index 0.3 (37 benchmarks, 140,178 requests, run and scored with the board's own kit), OneJev1.5-4B scores 54.89. Among the 83 models under 8B parameters on the board, only vLLM-SR d3-mini scores higher, by 0.01. OneJev1.5-4B has the best Language Understanding and Tools & Automation scores among the five models below, and the second best in the other three areas.
The four other models are the highest-scoring models of 3B to 6B parameters on the board. Their scores are their public-part scores on the board on 12 October 2026.
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.
bash
pip install "qev[torch] @ git+https://github.com/OmniJev/OneJev.git"qev serve --model OmniJev/OneJev1.5-4Bpython
from qev import Client, Choice, Noulfrom 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.
bibtex
@misc{onejev2026, title = {{OneJev}: A Multimodal System One Decision Model}, author = {{OmniJev Team}}, year = {2026}, howpublished = {\url{https://github.com/OmniJev/OneJev}}}Apache 2.0
from qev import Client, Choice, Noulfrom 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"})},)