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Sieve-Vision-27B
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Sieve-Vision-27B is an open-weights decision model from Sthānika AI. Its published size is 26.09B parameters. Licence: cc-by-nc-4.0.
Catalogue identity notes
Exact Decision Index tested engine sieve-vision-27b; reported model Sieve-Vision-27B. Type: LoRA + head; 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.
$ how it compares
The decision models ranked either side of Sieve-Vision-27B, out of 357 with a score, then more from Sthānika AI.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 77 |  Mercury Decide (free endpoint) | 63±2 | api price unknown | ||
| 78 |  Nace.AI Drex v2.0 (35B-A3B) | 63±1 | model files | ||
| 79 | Sieve-Vision-27B | 63±4 | model files | ||
| 80 | reflex-27b (DecisionBench 1d4bf0f2) | 63±2 | model files | ||
| 81 |  decisio 0.6.0 (Gemma 4 31B) (decision readout) | 63±1 | source code | ||
| more from Sthānika AI | |||||
| 3 | Sieve 31B v3 | 74±2 | model files | ||
| 37 | Sieve 27B v2 | 67±1 | model files | ||
| 137 | Sieve 9B v2 | 57±1 | model files | ||
see all decision models · find the ones that fit your machine
$ what it's good at
vision▁·▃▂▃▃▇▄▄█▄▃ #32 of 79 · 95% of its score · 12 tests
Beats StartLux-Decision v1.1 9B, RSI-Jev v6.1 VL 27B (effort auto), StartLux-Decision v1.1 4B here, though they rank higher overall. Trails Clef-flash, GPT-6 Luna Decisions, Perplexity Decider Flash v1.2 (4B) here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Decision Index Vision 0.4 · Mind2Web (chance-corrected points) submitted | 94.3800 points ✎ | ▁·▁▁▃▁▄▄▃▅█▃ beats 61/61 | 12% |
| Decision Index Vision 0.4 · MMMU-Pro vision (chance-corrected points) submitted | 39.4000 points ✎ | ▁▁▅▁▄▁▄█▄▃▇▇ beats 41/61 | 11% |
| Decision Index Vision 0.4 · Winoground (chance-corrected points) submitted | 80.2500 points ✎ | ▁·▁▁▁▁·▂▁▃▅█ beats 33/61 | 10% |
| Decision Index Vision 0.4: private same skills (points) | 57.2200 points ✎ | ▁▁▃▃▁▂▄▆▃▃▅█ beats 32/61 | 10% |
| Decision Index Vision 0.4 · Moderation (Hateful Memes) (chance-corrected points) submitted | 23.1700 points ✎ | ▆▃▃▆▄▄▇██▁▆▆ beats 13/61 | 8.8% |
| Decision Index Vision 0.4 · RealWorldQA (chance-corrected points) submitted | 75.5800 points ✎ | ▁·▂▂▁▃▃▅▅▃▂█ beats 59/61 | 8.0% |
| Decision Index Vision 0.4 · CharXiv (chance-corrected points) submitted | 72.1800 points ✎ | ▂▁▁▁▁▃▁▅▅▆██ beats 31/61 | 7.8% |
| Decision Index Vision 0.4 · InfographicVQA (chance-corrected points) submitted | 97.8100 points ✎ | ▁·▁▂▁▁▃▁▂▂▅█ beats 49/61 | 6.2% |
4 more tests
| test | result | vs the others in its run | counts |
|---|---|---|---|
| Decision Index Vision 0.4 · R-Bench-M (chance-corrected points) submitted | 26.7300 points ✎ | ▂▄▇▆█▄▄▇▂··▁ beats 37/61 | 6.2% |
| Decision Index Vision 0.4 · BLINK (chance-corrected points) submitted | 68.4300 points ✎ | ▁···▁▂▂▂█▄▃▃ beats 51/61 | 5.9% |
| Decision Index Vision 0.4 · CV-Bench (chance-corrected points) submitted | 80.9000 points ✎ | ▁▁▁▁▂▁▁▁▄█▃▃ beats 57/61 | 4.7% |
| Decision Index Vision 0.4 · KIE (CORD+FUNSD) (chance-corrected points) submitted | 97.4600 points ✎ | ▁··▁▁▁▁▁▂▄█▃ beats 61/61 | 3.4% |
$ 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
| source | results | share of the score |
|---|---|---|
 Decision Index Vision 0.4 · submitted; independent execution unverified | 12 | 95% |
| prior · pull toward the middle | 5% | |
| weighted average | = 62.7 |
$ improve this page
Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.
data updated · sources and licences · json