Crivo-400M

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Crivo-400M is an open-weights decision model from tardellirs. Its published size is 0.4B parameters. Licence: cc-by-nc-4.0.

Catalogue identity notes

Exact Decision Index tested engine crivo-400m; reported model Crivo-400M. 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.

37.0±1.1#277 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of Crivo-400M, out of 357 with a score, then more from tardellirs.

#	model	score	where	released
277	Gevva E2B davidburhans	37±1	model files
278	mojev (DecisionBench 0c8695b6) MoLeMo-Lab	37±2	request access
279	Crivo-400M tardellirs	37±1	model files
280	InternLM Intern-Decision Qwen3.5-2B Shanghai AI Laboratory (InternLM)	37±1	model files
281	Sieve 2B Sthānika AI	37±1	model files	2026-10-13
	more from tardellirs
253	Crivo-1B tardellirs	43±1	model files
301	Crivo-150M v2 tardellirs	32±1	model files
312	Crivo-68M tardellirs	30±1	model files

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

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #186 of 250 · 9.3% of its score · 6 tests

Beats JevK5 v0.3, FlyMy.AI Decision 4B v1.6, RSI-Jev v5.0 VL 3B here, though they rank higher overall. Trails JevK5 Lite, Mola-68M, Mola-150M 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	87.1% ✎	▁▂▁▁▁▂▃▄▅▆█▁ beats 63/246	very easy · avg 92%	▇▇ 4.8%	sharp · noisy
Decision Index 0.4.1: API-Bank (accuracy) submitted	69.3% ✎	▁▁▁▁▁▁▁▂▃▃█▂ beats 88/236	easy · avg 70%	▇ 2.3%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	70.7% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 96/246	easy · avg 72%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	64.3% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 120/246	medium · avg 60%	▇ 0.8%	blunt · at its level
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	71.6% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 176/243	medium · avg 37%	▇ 0.4%	sharp · noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	66.1% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #190 of 258 · 4.9% of its score · 7 tests

Beats JevK5 9B v0.3.3, Hopper (G) 1.2 Qwen3.5-4B LoRA, Kev 4B r10 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: Amazon ESCI (macro-F1) submitted	40.9% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 66/246	medium · avg 47%	▇ 1.5%	at its level
Decision Index 0.4.1: BRIGHT (nDCG@10) submitted	35.5% ✎	▁▁▁▁▁▁▁▂▃▄▇█ beats 75/246	medium · avg 38%	▇ 1.5%	blunt · at its level
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	88.7% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 108/238	easy · avg 83%	▇ 0.6%	sharp · at its level · noisy
Decision Index 0.4.1: HoVer (accuracy) submitted	81.9% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 163/246	easy · avg 74%	▇ 0.5%	blunt · at its level
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	52.6% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 60/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	86.0% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 142/238	easy · avg 79%	▇ 0.3%	at its level · noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	43.3% ✎	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▂▂▂▃▃▅▆▃█▆▄▃ #199 of 256 · 24% of its score · 9 tests

Beats TokenRhythm NeoHorse-Jev-4B, vLLM-SR vela3-fast (Vela 3.0, 0.4B), Saina Helm 2.0 v2 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	72.7% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 56/246	very easy · avg 85%	▇▇▇▇ 9.3%	sharp · at its level
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	61.9% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 46/246	easy · avg 74%	▇▇ 5.3%	at its level
Decision Index 0.4.1: ANLI (macro-F1) submitted	43.9% ✎	▁▂▃▅▅█▂····▁ beats 45/246	medium · avg 60%	▇▇ 4.2%	at its level
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	35.7% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 74/246	medium · avg 44%	▇ 1.4%	at its level
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	82.3% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 184/246	easy · avg 71%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: FinEntity (macro-F1) submitted	87.1% ✎	▁···▁▁▁▁▃▆█▃ beats 112/246	easy · avg 84%	▇ 0.9%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	75.3% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 173/246	medium · avg 62%	▇ 0.8%	at its level
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	66.1% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 81/246	easy · avg 66%	▇ 0.7%	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	32.0% ✎	▁··▂▃▃▅▇█▇▃▃ beats 170/246	hard · avg 22%	▇ 0.5%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #89 of 247 · 7.6% 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) 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	62.3% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 117/246	medium · avg 61%	▇ 3.2%	at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	91.6% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 163/246	easy · avg 83%	▇ 1.7%	sharp · at its level · noisy
Decision Index 0.4.1: Humicroedit (accuracy) submitted	51.6% ✎	▁·▁▃▃▂▅█▄▂▁▁ beats 36/246	medium · avg 60%	▇ 1.4%	blunt · at its level
Decision Index 0.4.1: cfcolor (accuracy) submitted	48.4% ✎	▁········▃▇█ beats 5/246	medium · avg 60%	▇ 0.8%	blunt · at its level
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	38.1% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 42/246	medium · avg 47%	▇ 0.2%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	2.4% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 49/237	hard · avg 21%	▇ 0.2%	sharp · too hard for it · noisy

knowledge & reasoning▁▂▂▃▂▃▃▅▅▆█▂ #224 of 263 · 48% of its score · 14 tests

Beats Decision-1.0-Eos-0.8B (DecisionBench 3c2d6326), Amber 0.4B here, though they rank higher overall. Trails laya (DecisionBench 1c5edc17), LFM2.5-2.6B-RLCD, InternLM Intern-Decision Qwen3.5-0.8B here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	57.8% ✎	▂▂▂▃▂▂▁▃▄▆█▆ beats 37/246	very easy · avg 90%	▇▇▇▇ 10%	sharp · at its level
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	77.1% ✎	▁▁▂▁▃▂▂▂▃▆█▅ beats 39/246	very easy · avg 96%	▇▇▇ 8.2%	sharp · noisy
Decision Index 0.4.1: MMLU (accuracy) submitted	40.9% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 35/246	easy · avg 70%	▇▇▇ 7.0%	sharp · at its level
Decision Index 0.4.1: MMLU-Pro (accuracy) submitted	16.9% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 28/246	medium · avg 46%	▇▇ 6.1%	sharp · at its level
Decision Index 0.4.1: BBH (accuracy) submitted	40.2% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 35/246	easy · avg 66%	▇▇ 5.7%	at its level
Decision Index 0.4.1: WinoGrande (accuracy) submitted	63.2% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 54/246	easy · avg 78%	▇ 3.5%	at its level
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	23.5% ✎	▃▆▅▅▆█▆▃▁··▁ beats 8/246	medium · avg 39%	▇ 2.0%	blunt · at its level
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	19.2% ✎	▁··▄█▆▆▆▅▅▃▁ beats 30/246	medium · avg 45%	▇ 2.0%	sharp · 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	54.8% ✎	▁▁▂█▅▄▁▁▁▁▁▁ beats 62/246	medium · avg 60%	▇ 0.9%	blunt · at its level
Decision Index 0.4.1: CLadder (accuracy) submitted	73.6% ✎	▁███▆▅▃▃▁▁▁▁ beats 149/246	easy · avg 71%	▇ 0.9%	at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	38.8% ✎	██▄▃▃▄▁▁···▁ beats 45/246	medium · avg 58%	▇ 0.8%	at its level · noisy
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	2.7% ✎	▁▁▁▃▁▁▁▂▅█▂▁ beats 23/246	hard · avg 22%	▇ 0.6%	typical
Decision Index 0.4.1: ChessBench (accuracy) submitted	8.2% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 27/246	hard · avg 16%	▇ 0.3%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	14.0% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 170/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 6.0% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	30.0000 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 65/247	avg 41.206 points	▇▇ 3.9%	blunt
Decision Index 0.4.1: private new domains (equated points)	14.3500 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 53/247	avg 38.861 points	▇ 2.1%	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/tardellirs ↗

$ 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		= 37.0

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	66.1%	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]	43.3%	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