Jev Omni

Jev Omni by akhilaaa3, a text-classification model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.

Comparison

Feature	Jev Omni	Interfaze
Input Modalities	text, image, audio, video	image, text, audio, video, document
Native OCR	No	Yes
Long Document Processing	No	Yes
Language Support	140 partial	162+
Native Speech-to-Text	No	Yes
Native Object Detection	No	Yes
Guardrail Controls	No	Yes
Context Input Size	262.1K	1M
Tool Calling	No	Tool calling supported + built in browser, code execution and web search

Scaling

Feature	Jev Omni	Interfaze
Scaling	Self-hosted/Provider-hosted with quantization	Unlimited

View model card on Hugging Face

A multimodal decision classifier for text, images, audio and video. Supply a question and options; receive a probability for each option—not a generated explanation.

Built on Gemma 4 12B IT, with a 30,000-question fine-tuning run.

Results

Benchmark	Accuracy¹	Micro accuracy
DecisionBench Medium · 80 scenarios / 293 questions	87.57%	86.01%
JevBench · matched 195 groups / 231 decisions	86.15%	87.45%
MMAU · 1,000 questions	—	63.10%
MVBench · 14 evaluated tasks / 2,786 questions	53.10%	53.09%

Merged-model results. ¹Equal-weight scenario/group average.

Open-weight comparison

Model	Parameters	MMAU	MVBench	Modalities
Jev-Omni	12B	63.10%	53.10%	Text, image, audio, video
Inkling	975B total / 41B active	77.20%	—	Text, image, audio
Qwen3.5-397B-A17B	397B total / 17B active	—	77.60%	Text, image, video

Reference scores are officially reported by their developers and may use different evaluation protocols.

Jev-Omni cost uses its recorded input tokens at OpenRouter's Gemma 3 12B input rate ($0.05/M); its classifier generates no output tokens.

Both classifiers are priced one call per question: a classifier answers one question at a time, so the state is re-sent for each of them and there is no discount for asking several at once. The three chat models are priced one call per state, with all of that state's questions together, which is their own cheapest shape. Splitting a state into per-question calls multiplies input tokens by 2.82x on this set, so pricing the chat models the same way would move all three marks right by roughly that factor without changing the order.

DecisionBench dataset

Quick start

CUDA GPU required. FP32 weights use about 50 GB before runtime overhead; inference uses BF16 autocast.

pip install -r https://huggingface.co/akhilaaa3/Jev-Omni/resolve/main/requirements.txt

File: requirements.txt

from huggingface_hub import snapshot_download

path = snapshot_download("akhilaaa3/Jev-Omni")
import sys; sys.path.insert(0, path)
from jev_omni import load_jev_omni

classifier = load_jev_omni()
result = classifier.predict(
    state="The meeting starts at 10 AM. It is now 9 AM.",
    question="Has the meeting started?",
    options=["Yes", "No"],
)
print(result)

For an image, audio file or video, add media="/path/to/file" and modality="image", "audio" or "video". The loader downloads the original Gemma 4 multimodal components automatically. Audio is capped at 30 seconds; video uses 16 frames.

Speed

Warm H200 inference: 83 ms (~2k-token text), 26 ms (image), 31 ms (13-second audio), 504 ms (16-frame video). Medians over 20 optimized-backend requests; preprocessing and network time are extra.

Limits

Best supported at ≤20 options. The head accepts 256, but quality above 20 is not established.

License

Apache-2.0, following Gemma 4. Dataset rights remain separate.

Calibration

Medium ECE: 0.0400 (10 bins; lower is better). The graph uses five bins for readability.

Note: Jev-Omni is an independent open model that implements the typed-decision interface — noul (yes/no), choice and score questions answered with calibrated probabilities. It is not affiliated with, endorsed by, sponsored by, or derived from TypeSafe AI or its Jev model, and nothing in it was trained on Jev output.