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vLLM-SR Decision 2.0 Vega 27B

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vLLM-SR Decision 2.0 Vega 27B is an open-weights decision model from vLLM Semantic Router. Its published size is 27.8B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact Decision Index tested engine decision-2.0-vega-27b; reported model vLLM-SR Decision 2.0 Vega 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.

61.4±1.3#97 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of vLLM-SR Decision 2.0 Vega 27B, out of 357 with a score, then more from vLLM Semantic Router.

#	model	score	where
95	Qwen Decision 27B noncausal LoRA jsaurabh	61±1	model files
96	Lumma-fev-27B FrontiersMind	61±1	model files
97	vLLM-SR Decision 2.0 Vega 27B vLLM Semantic Router	61±1	model files
98	GEV-26B-Decide AutoTrust AI Lab	61±1	model files
99	litjev (DecisionBench 1d4bf0f2) zhengxuyu	61±2	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

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

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #107 of 250 · 6.6% of its score · 6 tests

Beats Senova-4B v2, SynACK Decide 26B-A4B v7, Tern-35B-A3B here, though they rank higher overall. Trails OneJev1.6-0.8B, TokenRhythm NeoHorse-Jev-4B, mmastrac diffgemma (decision readout) here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: API-Bank (accuracy) submitted	85.4% ✎	▁▁▁▁▁▁▁▂▃▂█▂ beats 180/236	easy · avg 70%	▇ 2.0%	sharp · noisy
Decision Index 0.4.1: BFCL (case exact accuracy) submitted	95.6% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 140/246	very easy · avg 92%	▇ 1.3%	sharp · maxed out · noisy
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	65.9% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 165/243	medium · avg 37%	▇ 1.2%	sharp · at its level · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	68.2% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 203/246	medium · avg 60%	▇ 1.1%	blunt · at its level
Decision Index 0.4.1: When2Call (accuracy) submitted	64.6% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 79/246	easy · avg 72%	▇ 1.0%	noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	79.6% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #74 of 258 · 7.2% of its score · 7 tests

Beats AutoTrust-JEV-27B v1.1, oenogarum (Gemma 4 31B), Decider 31B (NVFP4) here, though they rank higher overall. Trails StartLux-Decision v1.1 0.8B, Standard One 3B SH (v4), Standard One 8B SH (v4) here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: BRIGHT (nDCG@10) submitted	48.8% ✎	▁▁▁▁▁▁▁▂▂▄▇█ beats 213/246	medium · avg 38%	▇ 2.8%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	56.7% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 177/246	medium · avg 47%	▇ 2.5%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	82.6% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 168/246	easy · avg 74%	▇ 0.6%	blunt
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	61.4% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 91/245	easy · avg 70%	▇ 0.6%	noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	93.6% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 189/238	easy · avg 83%	▇ 0.3%	sharp · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	88.1% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 163/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	58.2% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #75 of 256 · 22% of its score · 9 tests

Beats Turion Jev 31B, AJev lm6q (Gemma 4 31B), oenogarum (Gemma 4 31B) here, though they rank higher overall. Trails Sieve 4B, Amber 4B, Decider 35B-A3B here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: ANLI (macro-F1) submitted	73.7% ✎	▁▂▃▅▅█▂····▁ beats 186/246	medium · avg 60%	▇▇ 5.5%	at its level
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	85.1% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 215/246	easy · avg 74%	▇▇ 5.3%	typical
Decision Index 0.4.1: HellaSwag (accuracy) submitted	94.1% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 175/246	very easy · avg 85%	▇▇ 3.8%	sharp · maxed out
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	56.2% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 166/246	medium · avg 44%	▇ 2.6%	at its level
Decision Index 0.4.1: ACOS (per-review F1) submitted	22.5% ✎	▁··▂▃▃▅▇█▇▃▃ beats 126/246	hard · avg 22%	▇ 1.5%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	59.9% ✎	▁▁▁▂▅▄▄▃▄▆█▄ beats 108/246	medium · avg 62%	▇ 1.1%	at its level
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	76.1% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 127/246	easy · avg 71%	▇ 1.0%	noisy
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	81.4% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 197/246	easy · avg 66%	▇ 0.8%	noisy

1 more test

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: FinEntity (macro-F1) submitted	79.8% ✎	▁···▁▁▁▁▃▆█▃ beats 68/246	easy · avg 84%	▇ 0.6%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #91 of 247 · 10% 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. Trails vLLM-SR vela3-fast-vision (Vela 3.0, 0.25B), Crivo-400M, Amber 0.4B here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: New Yorker (accuracy) submitted	73.1% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 173/246	medium · avg 61%	▇▇ 4.1%	at its level
Decision Index 0.4.1: Humicroedit (accuracy) submitted	62.0% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 149/246	medium · avg 60%	▇ 2.1%	blunt · at its level
Decision Index 0.4.1: cfcolor (accuracy) submitted	63.2% ✎	▁········▁▇█ beats 150/246	medium · avg 60%	▇ 1.2%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	36.5% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 156/237	hard · avg 21%	▇ 1.2%	sharp · at its level · noisy
Decision Index 0.4.1: BPoMP (accuracy) submitted	91.1% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 157/246	easy · avg 83%	▇ 1.0%	sharp · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	41.1% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 85/246	medium · avg 47%	▇ 0.4%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #87 of 263 · 44% of its score · 14 tests

Beats StartLux-Decision v1.1 4B, KnowLine 4B Gen5.5, Jeffan-4B-2010 here, though they rank higher overall. Trails Solar Decide, Clef-flash, Xor 1.2 here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: MMLU-Pro (accuracy) submitted	64.8% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 178/246	medium · avg 46%	▇▇▇▇ 11%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	82.3% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 162/246	easy · avg 70%	▇▇▇ 6.4%	sharp
Decision Index 0.4.1: BBH (accuracy) submitted	80.7% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 195/246	easy · avg 66%	▇▇ 6.2%	typical
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	76.4% ✎	▁··▄█▆▆▆▅▅▃▁ beats 213/246	medium · avg 45%	▇ 3.7%	sharp · at its level
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	42.4% ✎	▂▆▅▅▆█▆▃▁··▁ beats 133/246	medium · avg 39%	▇ 3.7%	blunt · at its level
Decision Index 0.4.1: WinoGrande (accuracy) submitted	85.5% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 166/246	easy · avg 78%	▇ 2.9%	typical
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	96.4% ✎	▂▂▂▂▂▂▁▃▄▆█▆ beats 174/246	very easy · avg 90%	▇ 2.6%	sharp · maxed out
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	31.7% ✎	▁▁▁▁▁▁▁▂▅█▂▁ beats 167/246	hard · avg 22%	▇ 1.8%	at its level

6 more tests

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: MuSR (accuracy) submitted	65.7% ✎	▁▁▂█▅▄▁▁▁▁▁▁ beats 182/246	medium · avg 60%	▇ 1.3%	blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	82.5% ✎	██▄▃▃▄▁▁···▁ beats 206/246	medium · avg 58%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	98.9% ✎	▁▁▂▁▂▂▂▂▃▆█▅ beats 175/246	very easy · avg 96%	▇ 1.1%	sharp · maxed out · noisy
Decision Index 0.4.1: CLadder (accuracy) submitted	74.7% ✎	▁███▆▅▃▃▁▁▁▁ beats 154/246	easy · avg 71%	▇ 1.0%	typical
Decision Index 0.4.1: ChessBench (accuracy) submitted	17.5% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 143/246	hard · avg 16%	▇ 0.8%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	10.2% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 34/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 9.5% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	55.3000 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 170/247	avg 41.206 points	▇▇ 6.1%	blunt
Decision Index 0.4.1: private new domains (equated points)	62.0700 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 192/247	avg 38.861 points	▇ 3.4%	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 99%

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	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 99%	CC BY 4.0
prior · pull toward the middle		▇ 1%
weighted average		= 61.4

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	79.6%	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]	58.2%	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