Seb-9B v2

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Seb-9B v2 is an open-weights decision model from ironbcc. Its published size is 9.65B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact Decision Index tested engine seb-9b-v2; reported model Seb-9B v2. Type: LoRA; 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.

58.4±1.3#125 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of Seb-9B v2, out of 357 with a score.

#	model	score	where	RAM	released
125	Jebadiah 27B v2.1 (Ollaya Q4_K_M) frontier-infra	59±4	local	~16GB	2026-10
126	vLLM-SR d3-mini (4B) vLLM Semantic Router	58±1	local	~11GB
127	Seb-9B v2 ironbcc	58±1	model files
128	JPT-35B-A3B kirp	58±1	model files
129	Mica v0.5 4B akivet	58±1	model files		2026-10

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

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #52 of 250 · 7.0% of its score · 6 tests

Beats vLLM-SR d3-ultra (Decision 3.0, 27B), AutoTrust-JEV-27B v1.1, OneShot 27B here, though they rank higher overall. Trails baselabs joey-1 (2B), Solomon v1.1, Bespoke Nimble 9B v3 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 171/236	easy · avg 70%	▇ 2.2%	sharp · noisy
Decision Index 0.4.1: BFCL (case exact accuracy) submitted	97.3% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 191/246	very easy · avg 92%	▇ 1.6%	sharp · maxed out · noisy
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	80.7% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 190/243	medium · avg 37%	▇ 1.2%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	91.1% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 230/246	easy · avg 72%	▇ 1.1%	noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	67.3% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 187/246	medium · avg 60%	▇ 1.0%	blunt · at its level
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	80.0% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #82 of 258 · 6.8% 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), ezjev 4B s2 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	44.3% ✎	▁▁▁▁▁▁▁▂▂▄▇█ beats 143/246	medium · avg 38%	▇ 2.6%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	56.0% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 167/246	medium · avg 47%	▇ 2.4%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	88.1% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 217/246	easy · avg 74%	▇ 0.6%	blunt
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	65.5% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 106/245	easy · avg 70%	▇ 0.6%	noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	93.1% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 178/238	easy · avg 83%	▇ 0.4%	sharp · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	92.4% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 220/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	26.5% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #57 of 256 · 22% of its score · 9 tests

Beats Ainvest-Decision-27B v1.0, Turion Jev 31B, MISA AI 2.0 Decision Plus · oak 1.1 (31B) here, though they rank higher overall. Trails Decider 35B-A3B, Sieve 9B v2, Clef-flash 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	63.8% ✎	▁▂▃▅▅█▂····▁ beats 120/246	medium · avg 60%	▇▇ 5.4%	at its level
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	82.2% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 173/246	easy · avg 74%	▇▇ 5.3%	typical
Decision Index 0.4.1: HellaSwag (accuracy) submitted	95.2% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 196/246	very easy · avg 85%	▇▇ 4.5%	sharp · maxed out
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	57.3% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 170/246	medium · avg 44%	▇ 2.5%	at its level
Decision Index 0.4.1: ACOS (per-review F1) submitted	22.5% ✎	▁··▂▃▃▅▇█▇▃▃ beats 127/246	hard · avg 22%	▇ 1.3%	at its level · noisy
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	84.8% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 224/246	easy · avg 71%	▇ 1.0%	noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	74.1% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 163/246	medium · avg 62%	▇ 1.0%	at its level
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	82.8% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 209/246	easy · avg 66%	▇ 0.8%	at its level · noisy

1 more test

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: FinEntity (macro-F1) submitted	88.5% ✎	▁···▁▁▁▁▃▆█▃ beats 131/246	easy · avg 84%	▇ 0.6%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #86 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 Amber 0.4B, Crivo-1B, GLiClass v4 edge (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	65.5% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 131/246	medium · avg 61%	▇▇ 4.0%	at its level
Decision Index 0.4.1: Humicroedit (accuracy) submitted	61.9% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 144/246	medium · avg 60%	▇ 2.0%	blunt · at its level
Decision Index 0.4.1: cfcolor (accuracy) submitted	64.7% ✎	▁········▁▇█ beats 171/246	medium · avg 60%	▇ 1.1%	blunt · at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	91.5% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 160/246	easy · avg 83%	▇ 1.1%	sharp · noisy
Decision Index 0.4.1: POP909-CL (accuracy) submitted	79.8% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 219/237	hard · avg 21%	▇ 1.0%	sharp · at its level · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	45.3% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 138/246	medium · avg 47%	▇ 0.3%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #115 of 263 · 44% of its score · 14 tests

Beats OneJev1.6-4B here, though they rank higher overall. Trails PurpleMIST-Flash-1.0, Jevfire Qwen3.8-27B (decision readout), JevK5 9B v0.3.3 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	53.2% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 125/246	medium · avg 46%	▇▇▇▇ 11%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	77.0% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 132/246	easy · avg 70%	▇▇▇ 6.7%	sharp
Decision Index 0.4.1: BBH (accuracy) submitted	71.8% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 145/246	easy · avg 66%	▇▇▇ 6.3%	typical
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	55.2% ✎	▁··▄█▆▆▆▅▅▃▁ beats 149/246	medium · avg 45%	▇ 3.6%	sharp · at its level
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	42.4% ✎	▂▆▅▅▆█▆▃▁··▁ beats 128/246	medium · avg 39%	▇ 3.4%	blunt · at its level
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	95.7% ✎	▂▂▂▂▂▂▁▃▄▆█▆ beats 155/246	very easy · avg 90%	▇ 3.2%	sharp · maxed out
Decision Index 0.4.1: WinoGrande (accuracy) submitted	88.4% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 188/246	easy · avg 78%	▇ 3.0%	typical
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	30.1% ✎	▁▁▁▁▁▁▁▂▅█▂▁ beats 146/246	hard · avg 22%	▇ 1.6%	at its level

6 more tests

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	98.4% ✎	▁▁▂▁▂▂▂▂▃▆█▅ beats 150/246	very easy · avg 96%	▇ 1.5%	sharp · maxed out · noisy
Decision Index 0.4.1: MuSR (accuracy) submitted	67.8% ✎	▁▁▂█▅▄▁▁▁▁▁▁ beats 202/246	medium · avg 60%	▇ 1.3%	blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	55.6% ✎	██▄▃▃▄▁▁···▁ beats 131/246	medium · avg 58%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: CLadder (accuracy) submitted	80.9% ✎	▁███▆▅▃▃▁▁▁▁ beats 183/246	easy · avg 71%	▇ 1.0%	typical
Decision Index 0.4.1: ChessBench (accuracy) submitted	22.2% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 192/246	hard · avg 16%	▇ 0.7%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	8.4% ✎	▁▃▂▃▆█▇▅▅▃▁▁ beats 6/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 8.9% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	52.4100 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 157/247	avg 41.206 points	▇▇ 5.7%	blunt
Decision Index 0.4.1: private new domains (equated points)	37.0100 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 111/247	avg 38.861 points	▇ 3.1%	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/ironbcc ↗

$ 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		= 58.4

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	80.0%	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]	26.5%	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