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), OneJev1.6-4B scores 62.01, up from 54.89 for OneJev1.5-4B. Every model on the board that scores higher has 25B parameters or more. It has the best score in four of the five areas among the five models below, and the second best in Language Understanding.
The four other models are the highest-scoring models of 3B to 6B parameters on the board.
It also scores above 42 of the 57 models of 25B to 36B parameters on the board.
Other models' scores are their public-part scores on the board on 16 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
uv venv -p 3.12 && source .venv/bin/activateuv pip install flide torchvisionflide serve --model OmniJev/OneJev1.6-4BIn another terminal, run this example with your own screenshot.png:
bash
IMG=$(base64 -w0 screenshot.png)curl http://localhost:8000/v1/systemone -H "Content-Type: application/json" -d @- <<EOF{ "state": {"task": "Pay the open invoice from ACME", "screen": "<image:1>"}, "media": [{"type": "image", "data": "data:image/png;base64,$IMG"}], "questions": { "done": {"type": "noul", "instructions": "The invoice has been paid"}, "next": {"type": "choice", "instructions": "What should the agent do next?", "criteria": {"click": "click an element", "type": "type text", "scroll": "scroll", "stop": "stop"}}, "progress": {"type": "score", "instructions": "How far along is the task?", "criteria": ["not started", "halfway", "almost done", "done"]} }}EOFanswers.done.noul is the probability of yes, answers.next.probabilities gives one probability per option and answers.progress.score is the expected level.
Flide serves TypeSafe's System One API plus a media field for images and video. More examples 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
uv venv -p 3.12 && source .venv/bin/activateuv pip install flide torchvisionflide serve --model OmniJev/OneJev1.6-4BIMG=$(base64 -w0 screenshot.png)curl http://localhost:8000/v1/systemone -H "Content-Type: application/json" -d @- <<EOF{ "state": {"task": "Pay the open invoice from ACME", "screen": "<image:1>"}, "media": [{"type": "image", "data": "data:image/png;base64,$IMG"}], "questions": { "done": {"type": "noul", "instructions": "The invoice has been paid"}, "next": {"type": "choice", "instructions": "What should the agent do next?", "criteria": {"click": "click an element", "type": "type text", "scroll": "scroll", "stop": "stop"}}, "progress": {"type": "score", "instructions": "How far along is the task?", "criteria": ["not started", "halfway", "almost done", "done"]} }}EOF@misc{onejev2026, title = {{OneJev}: A Multimodal System One Decision Model}, author = {{OmniJev Team}}, year = {2026}, howpublished = {\url{https://github.com/OmniJev/OneJev}}}