whatmodel.sh / decision / vLLM-SR vela3-fast-vision (Vela 3.0, 0.25B)

vLLM-SR vela3-fast-vision (Vela 3.0, 0.25B)

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vLLM-SR vela3-fast-vision (Vela 3.0, 0.25B) is an open-weights decision model from vLLM Semantic Router. Its published size is 0.25B parameters. Licence: mit.

Catalogue identity notes

Exact Decision Index tested engine vela3-fast-vision-text; reported model vLLM-SR vela3-fast-vision (Vela 3.0, 0.25B). Type: full fine-tune; technique: encoder. 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.

34.9±1.1#282 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of vLLM-SR vela3-fast-vision (Vela 3.0, 0.25B), out of 357 with a score, then more from vLLM Semantic Router.

#	model	score	where	RAM	released
282	smalljev-semantic-v9 (DecisionBench 8e2bac7c) isHeSatoshi	36±2	model files
283	Strands Decider 2B (Hobson v21) Amazon Web Services	36±1	local	~6.6GB	2026-10
284	vLLM-SR vela3-fast-vision (Vela 3.0, 0.25B) vLLM Semantic Router	35±1	model files
285	wyat-1-800m teilomillet	34±1	model files		2026-10-18
286	tasksource Decider nano v5 (150M) tasksource	34±1	model files
	more from vLLM Semantic Router
8	vLLM-SR d3-ultra (Decision 3.0, 27B) vLLM Semantic Router	72±1	model files
9	vLLM-SR d3-turbo v1.1 (27B) vLLM Semantic Router	72±1	model files
25	vLLM-SR d3 (27B) vLLM Semantic Router	69±1	model files

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$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #201 of 250 · 9.5% of its score · 6 tests

Beats Cam1, mini-jev Qwen3-4B (decision readout), InternLM Intern-Decision Qwen3.5-2B here, though they rank higher overall. Trails JevK5 Lite, Mola-68M, Crivo-68M here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: BFCL (case exact accuracy) submitted	81.6% ✎	▁▂▁▁▁▃▂▄▅▆█▁ beats 48/246	very easy · avg 92%	▇▇ 5.1%	sharp · noisy
Decision Index 0.4.1: API-Bank (accuracy) submitted	37.4% ✎	▁▁▁▁▁▁▁▃▃▂█▂ beats 26/236	easy · avg 70%	▇ 2.2%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	53.9% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 51/246	easy · avg 72%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	56.5% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 46/246	medium · avg 60%	▇ 0.7%	blunt · at its level
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	58.0% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 143/243	medium · avg 37%	▇ 0.3%	sharp · noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	65.3% ✎	no other model yet		—	failed run: Publisher excludes this protocol because its prompt reveals GPT-4 as the best route. Evidence: https://huggingface.co/spaces/multimodalart/jev-decision-index/blob/c130faf65f82e81d3bdefb01e758c712dc5c5199/data/index.json

retrieval & classification▁▁▁·▁▁▁▁▃▃█▅ #181 of 258 · 4.8% of its score · 7 tests

Beats JevK5 9B v0.3.3, Decider 4B, JevK5 v0.3 here, though they rank higher overall. Trails Mola-68M, Bosun v3.1 1.7B LoRA, Dinah-1 here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	43.4% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 76/246	medium · avg 47%	▇ 1.4%	at its level
Decision Index 0.4.1: BRIGHT (nDCG@10) submitted	23.4% ✎	▁▁▁▁▁▃▁▂▂▄▇█ beats 27/246	medium · avg 38%	▇ 1.4%	blunt · at its level
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	76.6% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 54/238	easy · avg 83%	▇ 0.6%	sharp · at its level · noisy
Decision Index 0.4.1: HoVer (accuracy) submitted	81.0% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 154/246	easy · avg 74%	▇ 0.5%	blunt · at its level
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	89.4% ✎	█▅▅▄▃▃▁▁▁▁·▁ beats 210/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	71.3% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 43/238	easy · avg 79%	▇ 0.3%	at its level · noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	39.7% ✎	no other model yet		—	failed run: Publisher reports a builder bug exposing dialogue turns after the classified turn; awaiting corrected rerun. Evidence: https://huggingface.co/spaces/multimodalart/jev-decision-index/blob/c130faf65f82e81d3bdefb01e758c712dc5c5199/data/index.json

language understanding▂▂▂▃▂▅▆▃█▆▄▃ #212 of 256 · 24% of its score · 9 tests

Beats Valen-2B, Saina Helm 2 Omni (0.8B), Gevva E2B here, though they rank higher overall. Trails DecisionTune 1.0, vera-spark, Bekko System One v0 400M here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: HellaSwag (accuracy) submitted	59.7% ✎	▂▄▂▃▄▇▃▇█▅▃▁ beats 43/246	very easy · avg 85%	▇▇▇▇ 9.5%	sharp · at its level
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	62.0% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 47/246	easy · avg 74%	▇▇ 5.3%	at its level
Decision Index 0.4.1: ANLI (macro-F1) submitted	47.4% ✎	▁▂▃▅▅█▂····▁ beats 54/246	medium · avg 60%	▇▇ 4.1%	at its level
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	43.6% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 106/246	medium · avg 44%	▇ 1.3%	at its level
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	47.2% ✎	▁▁▁▁▁▁▃▂▂▇▇█ beats 31/246	easy · avg 71%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: FinEntity (macro-F1) submitted	83.2% ✎	▁···▁▁▁▁▃▆█▃ beats 88/246	easy · avg 84%	▇ 0.9%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	73.6% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 158/246	medium · avg 62%	▇ 0.8%	at its level
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	62.1% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 70/246	easy · avg 66%	▇ 0.7%	at its level · noisy

1 more test

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: ACOS (per-review F1) submitted	30.9% ✎	▁··▂▃▃▅▇█▇▃▃ beats 167/246	hard · avg 22%	▇ 0.4%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #88 of 247 · 7.4% of its score · 6 tests

Beats Jev 1.13, jev-brain · Gemma-4-31B (decision readout), Jeffan-4B-2010 here, though they rank higher overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: New Yorker (accuracy) submitted	57.0% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 81/246	medium · avg 61%	▇ 3.1%	at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	91.4% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 159/246	easy · avg 83%	▇ 1.7%	sharp · at its level · noisy
Decision Index 0.4.1: Humicroedit (accuracy) submitted	61.0% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 120/246	medium · avg 60%	▇ 1.4%	blunt · at its level
Decision Index 0.4.1: cfcolor (accuracy) submitted	52.2% ✎	▁········▁▇█ beats 37/246	medium · avg 60%	▇ 0.8%	blunt · at its level
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	55.4% ✎	▁▂▄█▇▅▁▃▄▄▁▁ beats 186/246	medium · avg 47%	▇ 0.2%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	63.9% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 186/237	hard · avg 21%	▇ 0.2%	sharp · too hard for it · noisy

knowledge & reasoning▁▂▃▂▂▃▃▅▅▆█▂ #236 of 263 · 48% of its score · 14 tests

Beats Decision-1.0-Eos-0.8B (DecisionBench 3c2d6326), Amber 0.4B here, though they rank higher overall. Trails laya (DecisionBench 1c5edc17), JevK5 Lite, LFM2.5-2.6B-RLCD here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	46.2% ✎	▂▂▃▂▂▂▁▃▄▆█▆ beats 27/246	very easy · avg 90%	▇▇▇▇ 10%	sharp · at its level
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	64.7% ✎	▁▁▂▃▂▂▂▂▃▆█▅ beats 25/246	very easy · avg 96%	▇▇▇▇ 9.3%	sharp · noisy
Decision Index 0.4.1: MMLU (accuracy) submitted	36.9% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 26/246	easy · avg 70%	▇▇▇ 6.9%	sharp · at its level
Decision Index 0.4.1: MMLU-Pro (accuracy) submitted	20.2% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 40/246	medium · avg 46%	▇▇ 5.6%	sharp · at its level
Decision Index 0.4.1: BBH (accuracy) submitted	38.9% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 33/246	easy · avg 66%	▇▇ 5.5%	at its level
Decision Index 0.4.1: WinoGrande (accuracy) submitted	62.0% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 51/246	easy · avg 78%	▇ 3.5%	at its level
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	32.1% ✎	▂▆▅▅▆█▆▃▁··▁ beats 66/246	medium · avg 39%	▇ 1.9%	blunt · at its level
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	27.1% ✎	▁··▄█▆▆▆▅▅▃▁ beats 66/246	medium · avg 45%	▇ 1.8%	sharp

6 more tests

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: MuSR (accuracy) submitted	44.1% ✎	▁▃▂█▅▄▁▁▁▁▁▁ beats 18/246	medium · avg 60%	▇ 0.9%	blunt · at its level
Decision Index 0.4.1: CLadder (accuracy) submitted	65.8% ✎	▁███▆▅▃▃▁▁▁▁ beats 100/246	easy · avg 71%	▇ 0.9%	at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	46.3% ✎	██▄▃▃▄▁▁···▁ beats 93/246	medium · avg 58%	▇ 0.8%	at its level · noisy
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	15.9% ✎	▁▁▁▁▁▁▁▃▅█▂▁ beats 59/246	hard · avg 22%	▇ 0.6%	typical
Decision Index 0.4.1: ChessBench (accuracy) submitted	15.6% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 118/246	hard · avg 16%	▇ 0.3%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	16.6% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 222/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 5.8% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	29.7700 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 63/247	avg 41.206 points	▇▇ 3.8%	blunt
Decision Index 0.4.1: private new domains (equated points)	10.6200 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 22/247	avg 38.861 points	▇ 2.1%	blunt

$ where to run it

Download model files ↗
Published model files verified. Includes adapters or readout weights; follow the model documentation for the required base model. 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/vllm-sr ↗

$ how the score was worked out · Decision Index 0.4.1 100%

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	licence
Decision Index 0.4.1 · submitted; independent execution unverified	42	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 100%	CC BY 4.0
prior · pull toward the middle		▇ 0%
weighted average		= 34.9

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	65.3%	failed run: Publisher excludes this protocol because its prompt reveals GPT-4 as the best route. Evidence: https://huggingface.co/spaces/multimodalart/jev-decision-index/blob/c130faf65f82e81d3bdefb01e758c712dc5c5199/data/index.json
Decision Index 0.4.1	Decision Index 0.4.1: SGD/SGD-X (macro-F1) [correct]	39.7%	failed run: Publisher reports a builder bug exposing dialogue turns after the classified turn; awaiting corrected rerun. Evidence: https://huggingface.co/spaces/multimodalart/jev-decision-index/blob/c130faf65f82e81d3bdefb01e758c712dc5c5199/data/index.json

$ improve this page

Update model information Add a place to run it Add a test result Propose a benchmark Correct a benchmark score Edit data on GitHub

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 2026-11-03 02:37 UTC · sources and licences · json