DM-JEPA 1.1

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DM-JEPA 1.1 is an open-weights decision model from Danger Labs. Its published size is 0.15B parameters. Licence: mit.

Catalogue identity notes

Exact Decision Index tested engine dm-jepa-1.1; reported model DM-JEPA 1.1. 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.

32.6±1.0#294 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of DM-JEPA 1.1, out of 357 with a score.

#	model	score	where	RAM	released
294	vLLM-SR d3-edge (0.6B) vLLM Semantic Router	33±1	local	~2.6GB
295	Mola-150M codavidgarcia	33±1	model files		2026-10-14
296	DM-JEPA 1.1 Danger Labs	33±1	model files
297	Laya (english) (JevBench mini) Convai Innovations	33±2	source code
298	Kodiak v0.2 1B (accuracy) Cortex Agent LLC	32±1	model files

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

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #242 of 250 · 10% of its score · 6 tests

Trails LFM2.5-350M-RLCD, CLM-v0.1-8B Qwen3-8B heads, Verdict 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)	38.1% ✎	▁▃▁▁▁▂▂▄▅▆█▁ beats 8/246	very easy · avg 92%	▇▇ 5.5%	sharp · at its level · noisy
Decision Index 0.4.1: API-Bank (accuracy)	2.8% ✎	▁▁▃▁▁▁▁▂▃▂█▂ beats 8/236	easy · avg 70%	▇ 2.1%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy)	32.0% ✎	▁▁▁▃▃▅▅▆█▇█▃ beats 15/246	easy · avg 72%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: ToolRet (nDCG@10)	13.0% ✎	▁▃▁▁▁·▁▁▁▄█▁ beats 5/246	medium · avg 60%	▇ 0.7%	blunt · at its level
Decision Index 0.4.1: Home appliance simulator (case exact accuracy)	12.5% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 72/243	medium · avg 37%	▇ 0.2%	sharp · too hard for it · noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective))	66.0% ✎	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▁▁▃·▁▁▁▁▃▃█▅ #253 of 258 · 4.7% of its score · 7 tests

Trails Zev (decision readout), Nirnaya 0.4B, Supersonic Labs Julia 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)	12.1% ✎	▁▁▁▃▁▁▂▂▃▃█▅ beats 3/246	medium · avg 47%	▇ 1.4%	at its level
Decision Index 0.4.1: BRIGHT (nDCG@10)	8.8% ✎	▃▁▁▁▁▁▁▂▂▄▇█ beats 1/246	medium · avg 38%	▇ 1.4%	blunt · at its level
Decision Index 0.4.1: CLINC150+OOS (macro-F1)	92.9% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 174/238	easy · avg 83%	▇ 0.6%	sharp · at its level · noisy
Decision Index 0.4.1: HoVer (accuracy)	50.4% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 6/246	easy · avg 74%	▇ 0.5%	blunt · at its level
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy)	47.4% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 4/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: BANKING77 (macro-F1)	93.5% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 230/238	easy · avg 79%	▇ 0.3%	at its level · noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1)	22.9% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #189 of 256 · 24% of its score · 9 tests

Beats Nari Decision 4B v3.2, Solar Decide Flash, TokenRhythm NeoHorse-Jev-4B here, though they rank higher overall. Trails DecisionTune 1.0, vera-spark, JEV FLAN-T5 Large public v2 (342M) here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: HellaSwag (accuracy)	76.7% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 68/246	very easy · avg 85%	▇▇▇▇ 10%	sharp · at its level
Decision Index 0.4.1: NLI4CT (macro-F1)	28.4% ✎	▃▁▁▂▂▃▃▄▇▇█▄ beats 1/246	easy · avg 74%	▇▇ 5.3%	at its level
Decision Index 0.4.1: ANLI (macro-F1)	98.6% ✎	▁▂▃▅▅█▂····▃ beats 246/246	medium · avg 60%	▇▇ 3.9%	at its level
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English)	25.1% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 37/246	medium · avg 44%	▇ 1.2%	at its level
Decision Index 0.4.1: ContractNLI (macro-F1)	28.7% ✎	▁▁▁▁▃▁▁▂▂▇▇█ beats 13/246	easy · avg 71%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: FinEntity (macro-F1)	18.7% ✎	▁···▁▃▁▁▃▆█▃ beats 4/246	easy · avg 84%	▇ 0.9%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1)	24.7% ✎	▁▃▁▂▅▄▄▂▄▆█▄ beats 3/246	medium · avg 62%	▇ 0.8%	at its level
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class)	20.3% ✎	▁▁▁▁▁▁▃▁▁▃▆█ beats 22/246	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)	3.1% ✎	▁··▃▃▃▅▇█▇▃▃ beats 10/246	hard · avg 22%	▇ 0.4%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #190 of 247 · 7.3% of its score · 6 tests

Beats Kev 9B v2, vLLM-SR d3-nano (2B), Valen-4B here, though they rank higher overall. Trails Qwen-2.5-1B-RLCD (decision readout), Bekko System One v0 68M, Crivo-17M 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)	21.6% ✎	▃▁▂▃▃▃▇▅█▅▃▁ beats 6/246	medium · avg 61%	▇ 3.0%	at its level
Decision Index 0.4.1: BPoMP (accuracy)	64.0% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 57/246	easy · avg 83%	▇ 1.7%	sharp · at its level · noisy
Decision Index 0.4.1: Humicroedit (accuracy)	49.4% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 14/246	medium · avg 60%	▇ 1.4%	blunt · at its level
Decision Index 0.4.1: cfcolor (accuracy)	49.7% ✎	▁········▁▇█ beats 10/246	medium · avg 60%	▇ 0.8%	blunt · at its level
Decision Index 0.4.1: Habermas Machine (accuracy)	44.5% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 126/246	medium · avg 47%	▇ 0.2%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy)	0.0% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 0/237	hard · avg 21%	▇ 0.1%	sharp · too hard for it · noisy

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #189 of 263 · 48% of its score · 14 tests

Beats Aurelis-2B, StartLux-Decision v1.1 0.8B, Sieve 2B v3 here, though they rank higher overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: ARC-Easy (accuracy)	85.8% ✎	▁▁▂▁▂▃▂▂▃▆█▅ beats 55/246	very easy · avg 96%	▇▇▇▇ 11%	sharp · noisy
Decision Index 0.4.1: ARC-Challenge (accuracy)	85.2% ✎	▂▂▂▂▂▂▃▃▄▆█▆ beats 77/246	very easy · avg 90%	▇▇▇▇ 10%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy)	66.6% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 81/246	easy · avg 70%	▇▇▇ 6.7%	sharp · at its level
Decision Index 0.4.1: BBH (accuracy)	40.3% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 36/246	easy · avg 66%	▇▇ 5.3%	at its level
Decision Index 0.4.1: MMLU-Pro (accuracy)	23.4% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 47/246	medium · avg 46%	▇▇ 5.1%	sharp
Decision Index 0.4.1: WinoGrande (accuracy)	97.5% ✎	█▅█▅▄█▆▄▃▃▁▃ beats 245/246	easy · avg 78%	▇ 3.5%	at its level
Decision Index 0.4.1: GPQA Diamond (accuracy)	28.2% ✎	▂▆▅▅▆█▆▃▁··▁ beats 48/246	medium · avg 39%	▇ 1.8%	blunt · at its level
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice))	57.1% ✎	▁··▄█▆▆▆▅▅▃▁ beats 153/246	medium · avg 45%	▇ 1.6%	sharp

6 more tests

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: MuSR (accuracy)	39.5% ✎	▃▁▂█▅▄▁▁▁▁▁▁ beats 10/246	medium · avg 60%	▇ 0.9%	blunt · at its level
Decision Index 0.4.1: CLadder (accuracy)	49.7% ✎	▁███▆▅▃▃▁▁▁▁ beats 5/246	easy · avg 71%	▇ 0.9%	at its level
Decision Index 0.4.1: CRUXEval (accuracy)	35.6% ✎	██▄▃▃▄▁▁···▁ beats 28/246	medium · avg 58%	▇ 0.8%	at its level · noisy
Decision Index 0.4.1: SATA-Bench (case exact accuracy)	0.1% ✎	▃▁▁▁▁▁▁▂▅█▂▁ beats 0/246	hard · avg 22%	▇ 0.5%	typical
Decision Index 0.4.1: ChessBench (accuracy)	10.6% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 67/246	hard · avg 16%	▇ 0.3%	blunt
Decision Index 0.4.1: HLE (accuracy)	15.2% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 203/246	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)	3.1700 points ✎	▃▂▂▃▃▃▄▅▅▇█▃ beats 4/247	avg 41.206 points	▇ 3.7%	blunt
Decision Index 0.4.1: private new domains (equated points)	7.6400 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 8/247	avg 38.861 points	▇ 2.0%	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/DangerLabs ↗

$ 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	42	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 100%	CC BY 4.0
prior · pull toward the middle		▇ 0%
weighted average		= 32.6

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	66.0%	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]	22.9%	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