Darwin-35B-ZTC-Vision

Darwin-35B-ZTC-Vision is a vision decision model. It does not generate text. Given one or more images, a state and a typed question, it returns a calibrated probability for every option in one forward pass: yes or no (noul), one of several options (choice), or a level on an ordered scale (score). It handles up to four images per request, for example "which of the two photos has the higher quality" or "is there a defect on this part".

It is a 35B mixture-of-experts model (about 3B active parameters per token) built on Darwin-35B-A3B-Opus with the Darwin ZTC decision readout. It is the vision member of the ZTC series; the text member is Darwin-27B-ZTC-v9.

Results

S1MB (System One Mosaic Benchmark), vision-v1

All 20 required vision benchmarks complete (the optional Box tasks are not supported), zero failed decisions. The ranking follows the leaderboard's own Borda method applied to the published vision results (16 complete models, 2026-10-17) with this model added. Submission pending maintainer review; see the vision leaderboard.

Rank	Model	Borda score	Mean baseline-adjusted score
1	Darwin-35B-ZTC-Vision	91.00	73.98
2	Jev 1.14 (Preview)	90.00	74.64
3	Perplexity Decider Flash 1.2	77.50	67.35
4	Imajev 4B	67.83	69.29
5	Clef-Flash	63.50	63.70

Selected benchmarks (baseline-adjusted score)

Benchmark	Darwin-35B-ZTC-Vision	Jev 1.14 (Preview)
tallyqa-score (counting)	0.720	0.546
visa-noul (anomaly detection)	0.603	0.467
koniq-score (image quality)	0.196	0.143
cvbench-relation-choice	0.965	0.944
pope-noul (object hallucination)	0.793	0.767
nights-choice (perceptual similarity, 3 images)	0.886	0.866
vsr-noul (spatial relations)	0.775	0.768
generalization-vision-easy-choice	0.976	0.967

Training and data

Full-weight fine-tuning with a decision readout on public train splits and our own synthetic data:

• COCO train2017 images (licensed subset) for verification, counting, relation and multi-hop spatial questions computed from the annotations, plus synthetic quality degradations rated on a five-level scale.
• VisA normal images from the official train split (CC BY 4.0) with synthetic surface defects, plus procedural textures.
• Public train splits of text and chart decision datasets, and the text decision data of the Darwin ZTC series.

No S1MB question text was used for training.

Usage

Python 3.12, a CUDA GPU with about 75 GB of free memory (BF16 weights), and the code shipped in this repository (autojev/).

import sys
from pathlib import Path
from huggingface_hub import snapshot_download

path = Path(snapshot_download("FINAL-Bench/Darwin-35B-ZTC-Vision"))
sys.path.insert(0, str(path))
from autojev.model import DecisionModel, answer

model = DecisionModel(checkpoint=path).eval()
question = {
    "type": "noul",
    "instructions": "Determine whether image_1 shows a defect such as a scratch, crack, hole or stain.",
    "criteria": {"true": "A defect is present", "false": "The surface is normal"},
}
row = {"state": {"images": ["image_1"]}, "images": ["part.jpg"], "question": question}
print(answer(question, model.predict([row])[0]))

decide_server.py serves POST /v1/systemone (images as data URLs, up to four per request):

ZTC_MODEL=/path/to/snapshot PORT=8410 python decide_server.py

s1mb_adapter/ztc_http.py is the S1MB evaluator adapter we used to measure the results above against this server.

License

Apache-2.0 for the weights and code in this repository. Training images keep their own licenses (COCO: per-image Flickr licenses and CC BY 4.0 annotations; VisA: CC BY 4.0).