Jevstral 8B

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Jevstral 8B is an open-weights decision model from AmirBraham. Its published size is 8B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact Decision Index tested engine jevstral-8b; reported model Jevstral 8B. 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 (estimated by publisher); includes the served backbone, not just adapter size.

44.4±1.1#246 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of Jevstral 8B, out of 357 with a score.

#	model	score	where	RAM	released
246	OneJev1.6-0.8B OmniJev	45±1	model files		2026-10-17
247	GLiClass v4 edge (0.4B) Knowledgator Engineering	45±1	local	~2.7GB
248	Jevstral 8B AmirBraham	44±1	model files
249	crystal-0.8b Kyunghwan-kim	44±1	model files
250	QEV-0.8B ken-jo	44±4	model files

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$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #167 of 250 · 8.6% of its score · 6 tests

Beats LiquidAI d1-3B, vLLM-SR Decision 2.0 Nox 4B, Decider 4B here, though they rank higher overall. Trails JevK5 Lite, Gevva E2B, 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: BFCL (case exact accuracy) submitted	91.4% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 82/246	very easy · avg 92%	▇ 3.5%	sharp · noisy
Decision Index 0.4.1: API-Bank (accuracy) submitted	60.8% ✎	▁▁▁▁▁▁▁▂▃▂█▂ beats 73/236	easy · avg 70%	▇ 2.5%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	52.0% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 47/246	easy · avg 72%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	60.7% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 71/246	medium · avg 60%	▇ 0.8%	blunt · at its level
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	17.1% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 80/243	medium · avg 37%	▇ 0.7%	sharp · noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	78.4% ✎	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▁▁▁·▁▁▁▃▃▃█▅ #207 of 258 · 5.5% of its score · 7 tests

Beats SemIf Qwen3.5-4B Base (decision readout), Metask-Jev-4B, Jobe Qwen3.5-4B (decision readout) here, though they rank higher overall. Trails Bosun v3.1 0.6B LoRA, Mola-68M, Bosun v3.1 1.7B LoRA 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	38.3% ✎	▁▁▁▁▁▁▁▂▂▄▇█ beats 87/246	medium · avg 38%	▇ 1.8%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	36.8% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 51/246	medium · avg 47%	▇ 1.8%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	55.2% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 24/246	easy · avg 74%	▇ 0.6%	blunt · at its level
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	51.6% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 53/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	74.2% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 49/238	easy · avg 83%	▇ 0.5%	sharp · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	83.1% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 120/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	59.7% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #195 of 256 · 24% of its score · 9 tests

Beats 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) submitted	74.1% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 60/246	very easy · avg 85%	▇▇▇ 7.9%	sharp
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	73.8% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 91/246	easy · avg 74%	▇▇ 5.4%	at its level
Decision Index 0.4.1: ANLI (macro-F1) submitted	50.0% ✎	▁▂▃▅▅█▂····▁ beats 68/246	medium · avg 60%	▇▇ 4.7%	at its level
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	43.3% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 105/246	medium · avg 44%	▇ 1.8%	at its level
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	59.3% ✎	▁▁▁▁▁▁▁▃▂▇▇█ beats 48/246	easy · avg 71%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	43.7% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 51/246	medium · avg 62%	▇ 0.9%	at its level
Decision Index 0.4.1: FinEntity (macro-F1) submitted	78.1% ✎	▁···▁▁▁▁▃▆█▃ beats 64/246	easy · avg 84%	▇ 0.8%	noisy
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	13.4% ✎	▁▁▁▁▁▃▁▁▁▃▆█ beats 15/246	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: ACOS (per-review F1) submitted	9.8% ✎	▁··▂▃▃▅▇█▇▃▃ beats 54/246	hard · avg 22%	▇ 0.7%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #209 of 247 · 8.3% of its score · 6 tests

Beats Valen-4B, Saracura PT-BR 4B, TokenRhythm NeoHorse-Jev-4B here, though they rank higher overall. Trails LFM2.5-350M-RLCD, system-one-gemma, Qwen-2.5-1B-RLCD (decision readout) 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	57.2% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 82/246	medium · avg 61%	▇ 3.6%	at its level
Decision Index 0.4.1: Humicroedit (accuracy) submitted	52.8% ✎	▁·▁▃▃▂▅█▄▂▁▁ beats 44/246	medium · avg 60%	▇ 1.6%	blunt · at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	54.1% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 38/246	easy · avg 83%	▇ 1.5%	sharp · noisy
Decision Index 0.4.1: cfcolor (accuracy) submitted	55.3% ✎	▁········▁▇█ beats 52/246	medium · avg 60%	▇ 0.9%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	5.1% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 74/237	hard · avg 21%	▇ 0.4%	sharp · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	37.8% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 37/246	medium · avg 47%	▇ 0.3%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #165 of 263 · 46% of its score · 14 tests

Beats StartLux-Decision v1.1 2B, Solomon v1.1, Standard One 3B SH (v4) here, though they rank higher overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: MMLU-Pro (accuracy) submitted	47.9% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 104/246	medium · avg 46%	▇▇▇ 8.0%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	67.8% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 89/246	easy · avg 70%	▇▇▇ 7.4%	sharp · at its level
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	89.9% ✎	▂▂▂▂▂▂▁▃▄▆█▆ beats 94/246	very easy · avg 90%	▇▇▇ 7.2%	sharp
Decision Index 0.4.1: BBH (accuracy) submitted	57.1% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 76/246	easy · avg 66%	▇▇ 6.2%	at its level
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	96.5% ✎	▁▁▂▁▂▂▂▂▃▆█▅ beats 96/246	very easy · avg 96%	▇▇ 4.8%	sharp · maxed out · noisy
Decision Index 0.4.1: WinoGrande (accuracy) submitted	71.1% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 93/246	easy · avg 78%	▇ 3.4%	at its level
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	42.2% ✎	▁··▄█▆▆▆▅▅▃▁ beats 120/246	medium · avg 45%	▇ 2.6%	sharp · at its level
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	37.8% ✎	▂▆▅▅▆█▆▃▁··▁ beats 99/246	medium · avg 39%	▇ 2.4%	blunt · 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	61.4% ✎	▁▁▂█▅▄▁▁▁▁▁▁ beats 146/246	medium · avg 60%	▇ 1.0%	blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	42.1% ✎	██▄▃▃▄▁▁···▁ beats 69/246	medium · avg 58%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: CLadder (accuracy) submitted	64.7% ✎	▁███▆▅▃▃▁▁▁▁ beats 94/246	easy · avg 71%	▇ 0.9%	at its level
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	23.9% ✎	▁▁▁▁▁▁▁▂▅█▂▁ beats 99/246	hard · avg 22%	▇ 0.9%	typical
Decision Index 0.4.1: ChessBench (accuracy) submitted	8.5% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 32/246	hard · avg 16%	▇ 0.4%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	11.8% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 94/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 6.7% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	35.6800 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 80/247	avg 41.206 points	▇▇ 4.3%	blunt
Decision Index 0.4.1: private new domains (equated points)	27.0700 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 89/247	avg 38.861 points	▇ 2.4%	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/AmirBraham ↗

$ 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		= 44.4

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	78.4%	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]	59.7%	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