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Bosun v3.1 0.6B LoRA

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Bosun v3.1 0.6B LoRA is an open-weights decision model from Hanno Labs, released 22 September 2026. Its published size is 0.6B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact Decision Index tested engine bosun-v3.1-0.6b; reported model Bosun v3.1 0.6B LoRA. Type: LoRA + head; technique: autoregressive. Implementation: https://github.com/Hanno-Labs/jev-compatible-server. 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

Release date ↗ · Weights licence ↗ · Parameter count ↗

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

24.6±1.0#328 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of Bosun v3.1 0.6B LoRA, out of 357 with a score, then more from Hanno Labs.

#	model	score	where	RAM	released
328	JevK5 Lite alibiserikbay	25±1	model files
329	Von 1.3 wfzyx	25±1	model files		2026-09-28
330	Bosun v3.1 0.6B LoRA Hanno Labs	25±1	model files		2026-09-22
331	laya (DecisionBench 1c5edc17) ConvAI Innovations	25±1	local	~1.7GB
332	kev-0.5b (DecisionBench 9ce2fd39) Jared Palmer	24±2	model files
	more from Hanno Labs
42	bosun-v3.1-1.7b (DecisionBench 2ef2d972) Hanno Labs	67±2	model files
65	bosun-v3.1-0.6b (DecisionBench aaa9dd06) Hanno Labs	64±2	model files		2026-09-22
316	Bosun v3.1 1.7B LoRA Hanno Labs	29±1	model files

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$ what it's good at

tools & automation▁▃▃▁▁▂▃▄▆██▆ #230 of 250 · 10% of its score · 6 tests

Beats DM-JEPA 1.1, JEV FLAN-T5 Large public v2 (342M), Bekko System One v0 400M here, though they rank higher overall. Trails Qwen-2.5-1B-RLCD (decision readout), Prumo (rule-based engine), Crivo-17M 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	46.6% ✎	▁▂▃▁▁▂▂▄▅▆█▁ beats 20/246	very easy · avg 92%	▇▇▇ 6.6%	sharp · at its level · noisy
Decision Index 0.4.1: API-Bank (accuracy) submitted	53.1% ✎	▁▁▁▁▁▁▁▂▃▂█▂ beats 57/236	easy · avg 70%	▇ 1.7%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	30.0% ✎	▁▁▃▂▃▅▅▆█▇█▃ beats 14/246	easy · avg 72%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	57.4% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 52/246	medium · avg 60%	▇ 0.7%	blunt · at its level
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	0.0% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 32/243	medium · avg 37%	▇ 0.1%	sharp · too hard for it · noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	64.1% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #187 of 258 · 4.5% of its score · 7 tests

Beats JevK5 9B v0.3.3, JevK5 v0.3, Hopper (G) 1.2 Qwen3.5-4B LoRA here, though they rank higher overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	43.0% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 75/246	medium · avg 47%	▇ 1.2%	at its level
Decision Index 0.4.1: BRIGHT (nDCG@10) submitted	31.7% ✎	▁▁▁▁▁▁▁▂▃▄▇█ beats 56/246	medium · avg 38%	▇ 1.2%	blunt
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	64.8% ✎	▁▁▁▁▁▁▁▃▃▆█▄ beats 37/238	easy · avg 83%	▇ 0.6%	sharp · at its level · noisy
Decision Index 0.4.1: HoVer (accuracy) submitted	50.5% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 8/246	easy · avg 74%	▇ 0.6%	blunt · at its level
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	50.0% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 14/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	76.1% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 69/238	easy · avg 79%	▇ 0.4%	at its level · noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	54.3% ✎	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▃▂▂▃▂▅▆▃█▆▄▃ #247 of 256 · 23% of its score · 9 tests

Trails CLM-v0.1-8B Qwen3-8B heads, jeff GLiFormer-large (decision readout), Verdict 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	32.8% ✎	▃▄▂▂▄▇▃▇█▅▃▁ beats 9/246	very easy · avg 85%	▇▇▇▇ 9.5%	sharp · at its level
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	49.7% ✎	▁▁▁▃▂▃▃▄▇▇█▄ beats 19/246	easy · avg 74%	▇▇ 5.4%	at its level
Decision Index 0.4.1: ANLI (macro-F1) submitted	27.5% ✎	▁▃▃▅▅█▂····▁ beats 8/246	medium · avg 60%	▇ 3.5%	at its level
Decision Index 0.4.1: FinEntity (macro-F1) submitted	48.0% ✎	▁···▁▁▁▃▃▆█▃ beats 16/246	easy · avg 84%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	5.5% ✎	▁·▁·▃▁▂▄▅█▇▄ beats 3/246	medium · avg 44%	▇ 1.0%	typical
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	21.6% ✎	▁▁▁▃▁▁▁▂▂▇▇█ beats 9/246	easy · avg 71%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	26.3% ✎	▁▁▃▂▅▄▄▂▄▆█▄ beats 5/246	medium · avg 62%	▇ 0.7%	at its level
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	29.2% ✎	▁▁▁▁▁▁▁▃▁▃▆█ beats 28/246	easy · avg 66%	▇ 0.6%	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	5.6% ✎	▁··▂▃▃▅▇█▇▃▃ beats 28/246	hard · avg 22%	▇ 0.3%	too hard for it · noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #238 of 247 · 7.0% of its score · 6 tests

Beats JEV Ettin 150M, JEV FLAN-T5 Large public v2 (342M), Kev 0.8B r15 here, though they rank higher overall. Trails Zev (decision readout), LFM2.5-350M-RLCD, Nirnaya 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	38.6% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 32/246	medium · avg 61%	▇ 2.7%	at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	48.9% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 9/246	easy · avg 83%	▇ 1.8%	sharp · at its level · noisy
Decision Index 0.4.1: Humicroedit (accuracy) submitted	50.9% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 26/246	medium · avg 60%	▇ 1.4%	blunt · at its level
Decision Index 0.4.1: cfcolor (accuracy) submitted	50.9% ✎	▁········▁▇█ beats 27/246	medium · avg 60%	▇ 0.8%	blunt · at its level
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	36.6% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 28/246	medium · avg 47%	▇ 0.2%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	3.5% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 64/237	hard · avg 21%	▇ 0.1%	sharp · too hard for it · noisy

knowledge & reasoning▁▃▂▂▂▃▃▅▅▆█▂ #244 of 263 · 49% of its score · 14 tests

Beats Amber 0.4B, Dinah-1, Crivo-68M here, though they rank higher overall. Trails Qwen-2.5-1B-RLCD (decision readout), laya (DecisionBench 1c5edc17) here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	56.1% ✎	▁▁▃▁▂▂▂▂▃▆█▅ beats 15/246	very easy · avg 96%	▇▇▇▇▇▇ 14%	sharp · at its level · noisy
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	43.3% ✎	▂▂▃▂▂▂▁▃▄▆█▆ beats 21/246	very easy · avg 90%	▇▇▇▇ 11%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	34.7% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 20/246	easy · avg 70%	▇▇ 6.0%	sharp · at its level
Decision Index 0.4.1: BBH (accuracy) submitted	34.3% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 14/246	easy · avg 66%	▇▇ 4.8%	at its level
Decision Index 0.4.1: MMLU-Pro (accuracy) submitted	17.7% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 32/246	medium · avg 46%	▇▇ 3.8%	sharp
Decision Index 0.4.1: WinoGrande (accuracy) submitted	49.7% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 2/246	easy · avg 78%	▇ 3.7%	at its level
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	27.6% ✎	▂▆▅▅▆█▆▃▁··▁ beats 43/246	medium · avg 39%	▇ 1.6%	blunt
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	18.1% ✎	▁··▄█▆▆▆▅▅▃▁ beats 15/246	medium · avg 45%	▇ 1.1%	sharp

6 more tests

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: MuSR (accuracy) submitted	44.3% ✎	▁▃▂█▅▄▁▁▁▁▁▁ beats 19/246	medium · avg 60%	▇ 0.9%	blunt · at its level
Decision Index 0.4.1: CLadder (accuracy) submitted	49.5% ✎	▁███▆▅▃▃▁▁▁▁ beats 1/246	easy · avg 71%	▇ 0.9%	at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	35.8% ✎	██▄▃▃▄▁▁···▁ beats 30/246	medium · avg 58%	▇ 0.7%	at its level · noisy
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	1.5% ✎	▁▁▃▁▁▁▁▂▅█▂▁ beats 18/246	hard · avg 22%	▇ 0.4%	typical
Decision Index 0.4.1: ChessBench (accuracy) submitted	8.3% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 29/246	hard · avg 16%	▇ 0.3%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	15.0% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 201/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 5.7% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	13.2100 points ✎	▂▂▃▃▃▃▄▅▅▇█▃ beats 21/247	avg 41.206 points	▇ 3.7%	blunt
Decision Index 0.4.1: private new domains (equated points)	7.8000 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 9/247	avg 38.861 points	▇ 2.0%	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/Hanno-Labs ↗

$ 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		= 24.6

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	64.1%	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]	54.3%	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