whatmodel.sh / decision / Sieve-Vision-4B

Sieve-Vision-4B is an open-weights decision model from Sthānika AI. Its published size is 4.54B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact Decision Index tested engine sieve-vision-4b; reported model Sieve-Vision-4B. Type: full fine-tune; technique: autoregressive. Comparison entry; no current run command, API price or speed inferred from the benchmark. Current repository metadata checked separately; an unpinned alias is not evidence that today’s weights reproduce this historical run.

Model information sources

· Weights licence ↗ · Parameter count ↗

Publisher parameter count; includes the served backbone, not just adapter size.

55.9±3.5#145 of 355 decision modelsfrom 12 benchmark results across 1 source

$ how it compares

The decision models ranked either side of Sieve-Vision-4B, out of 357 with a score, then more from Sthānika AI.

#modelscorewhereRAMreleased
145Amber Hybrid 4B (decision readout) MLukauskas57±1model files
146Solar Decide Upstage57±1api price unknown2026-09-22
147Sieve-Vision-4B Sthānika AI56±4model files
148xor-26b-a4b-v1.1 Juspay Technologies56±4model files2026-10-09
149decisio 0.4.0 (Qwen3.6-35B-A3B) (decision readout) aminry56±1source code2026-10-04
more from Sthānika AI
3Sieve 31B v3 Sthānika AI74±2model files2026-10-27
37Sieve 27B v2 Sthānika AI67±1model files2026-10-18
79Sieve-Vision-27B Sthānika AI63±4model files

see all decision models · find the ones that fit your machine

$ what it's good at

vision▁·▃▂▃▃▇▄▄█▄▃ #46 of 79 · 95% of its score · 12 tests

Beats Clef-omni here, though they rank higher overall. Trails LiquidAI d1-3B, JPT-9B Qwen3.5-9B LoRA, Solomon v1.1 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Decision Index Vision 0.4 · Mind2Web (chance-corrected points) submitted92.6000 points ✎▁·▁▁▃▁▄▄▃▅█▃ beats 58/61avg 63.423 points▇▇▇▇▇ 12%blunt
Decision Index Vision 0.4 · MMMU-Pro vision (chance-corrected points) submitted29.1900 points ✎▁▁▅▁▄▁▄█▄▃▇▇ beats 26/61avg 22.797 points▇▇▇▇ 11%blunt
Decision Index Vision 0.4 · Winoground (chance-corrected points) submitted75.5000 points ✎▁·▁▁▁▁·▂▁▃▅█ beats 19/61avg 66.987 points▇▇▇▇ 10%blunt
Decision Index Vision 0.4: private same skills (points)52.0200 points ✎▁▁▃▃▁▂▄▆▃▃▅█ beats 25/61avg 48.067 points▇▇▇▇ 10%blunt
Decision Index Vision 0.4 · Moderation (Hateful Memes) (chance-corrected points) submitted22.5100 points ✎▆▃▃▆▄▄▇██▁▆▆ beats 12/61avg 28.443 points▇▇▇▇ 8.8%blunt
Decision Index Vision 0.4 · RealWorldQA (chance-corrected points) submitted60.3200 points ✎▁·▂▂▁▃▃▅▅▃▂█ beats 30/61avg 53.651 points▇▇▇ 8.0%blunt
Decision Index Vision 0.4 · CharXiv (chance-corrected points) submitted63.1600 points ✎▂▁▁▁▁▃▁▅▅▆██ beats 15/61avg 62.137 points▇▇▇ 7.8%blunt
Decision Index Vision 0.4 · InfographicVQA (chance-corrected points) submitted92.3500 points ✎▁·▁▂▁▁▃▁▂▃▅█ beats 30/61avg 83.859 points▇▇ 6.2%blunt
4 more tests
testresultvs the others in its rundifficultycountswhy
Decision Index Vision 0.4 · R-Bench-M (chance-corrected points) submitted20.4100 points ✎▂▄▇▆█▄▄▇▂··▁ beats 25/61avg 19.676 points▇▇ 6.2%blunt
Decision Index Vision 0.4 · BLINK (chance-corrected points) submitted51.9900 points ✎▁···▁▂▂▂█▄▂▃ beats 22/61avg 44.994 points▇▇ 5.9%blunt
Decision Index Vision 0.4 · CV-Bench (chance-corrected points) submitted69.8900 points ✎▁▁▁▁▂▁▁▃▄█▃▃ beats 17/61avg 67.662 points▇▇ 4.7%blunt
Decision Index Vision 0.4 · KIE (CORD+FUNSD) (chance-corrected points) submitted89.5800 points ✎▁··▁▁▁▁▁▂▄█▁ beats 19/61avg 85.184 points▇ 3.4%blunt

$ where to run it

Download model files ↗
Published model files verified. This is a verified repository snapshot; the benchmark does not establish an identical weight revision. A local run command and hardware requirements have not been verified for this entry. Source · checked 2026-10-31.

Model repository and documentation: huggingface.co/sthanika-ai ↗

$ how the score was worked out · Decision Index Vision 0.4 95%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
Decision Index Vision 0.4 · submitted; independent execution unverified12CC BY 4.0
prior · pull toward the middle
weighted average= 55.9

$ improve this page

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data updated · sources and licences · json