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LatentMesh Blitz 2B v1

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LatentMesh Blitz 2B v1 is an open-weights decision model from LatentMesh Labs. Its published size is 2.27B parameters. Licence: other.

Catalogue identity notes

Exact Decision Index tested engine latentmesh-blitz-2b-v1; reported model LatentMesh Blitz 2B v1. 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.

46.2±1.2#233 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of LatentMesh Blitz 2B v1, out of 357 with a score.

#	model	score	where
233	Jev-Style 2B v4 chaoliangUNSW	47±1	model files
234	Saracura PT-BR 4B Felhen	46±1	model files
235	LatentMesh Blitz 2B v1 LatentMesh Labs	46±1	request access
236	Winnow-E4B EldanRing	46±1	model files
237	Kas 4B kpiya	46±1	model files

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

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #168 of 250 · 8.4% 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	91.1% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 80/246	very easy · avg 92%	▇ 3.1%	sharp · noisy
Decision Index 0.4.1: API-Bank (accuracy) submitted	73.4% ✎	▁▁▁▁▁▁▁▂▃▃█▂ beats 99/236	easy · avg 70%	▇ 2.5%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	79.4% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 135/246	easy · avg 72%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	62.9% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 102/246	medium · avg 60%	▇ 0.9%	blunt · at its level
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	36.4% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 110/243	medium · avg 37%	▇ 0.8%	sharp · at its level · noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	78.2% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #144 of 258 · 5.7% of its score · 7 tests

Beats Quyet-1.0-Large, Decider chat · Qwen3.6-27B (decision readout), Sieve 9B v2 here, though they rank higher overall. Trails Amber 0.4B, vLLM-SR vela3-fast (Vela 3.0, 0.4B), crystal-0.8b 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	35.4% ✎	▁▁▁▁▁▁▁▂▃▄▇█ beats 74/246	medium · avg 38%	▇ 1.9%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	51.4% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 113/246	medium · avg 47%	▇ 1.9%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	84.8% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 179/246	easy · avg 74%	▇ 0.6%	blunt · at its level
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	71.2% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 134/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	92.1% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 158/238	easy · avg 83%	▇ 0.5%	sharp · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	87.5% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 155/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	57.9% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #146 of 256 · 24% of its score · 9 tests

Beats Solar Decide, JoshuaSP diffgemma (decision readout), PurpleMIST-Flash-1.0 here, though they rank higher overall. Trails Decider 2B, Crivo-1B 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	84.4% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 108/246	very easy · avg 85%	▇▇▇ 7.4%	sharp
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	68.9% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 73/246	easy · avg 74%	▇▇ 5.4%	at its level
Decision Index 0.4.1: ANLI (macro-F1) submitted	56.3% ✎	▁▂▃▅▅█▂····▁ beats 90/246	medium · avg 60%	▇▇ 4.9%	at its level
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	34.9% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 72/246	medium · avg 44%	▇ 1.9%	at its level
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	82.6% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 190/246	easy · avg 71%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	74.0% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 159/246	medium · avg 62%	▇ 0.9%	at its level
Decision Index 0.4.1: FinEntity (macro-F1) submitted	92.2% ✎	▁···▁▁▁▁▃▆█▃ beats 180/246	easy · avg 84%	▇ 0.8%	noisy
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	77.9% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 155/246	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	39.2% ✎	▁··▂▃▃▅▇█▇▃▃ beats 197/246	hard · avg 22%	▇ 0.8%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #96 of 247 · 8.5% 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), Crivo-400M, 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: New Yorker (accuracy) submitted	62.1% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 114/246	medium · avg 61%	▇ 3.7%	at its level
Decision Index 0.4.1: Humicroedit (accuracy) submitted	62.0% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 146/246	medium · avg 60%	▇ 1.6%	blunt · at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	90.7% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 151/246	easy · avg 83%	▇ 1.5%	sharp · noisy
Decision Index 0.4.1: cfcolor (accuracy) submitted	59.5% ✎	▁········▁▇█ beats 97/246	medium · avg 60%	▇ 0.9%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	67.7% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 193/237	hard · avg 21%	▇ 0.5%	sharp · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	36.2% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 23/246	medium · avg 47%	▇ 0.3%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #192 of 263 · 46% of its score · 14 tests

Beats StartLux-Decision v1.1 0.8B, Sieve 2B v3 here, though they rank higher overall. Trails DM-JEPA 1.1, Sieve 2B, Valen-2B 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	31.4% ✎	▄▄▃▃▃▄▅▇██▅▁ beats 67/246	medium · avg 46%	▇▇▇ 8.5%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	57.5% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 71/246	easy · avg 70%	▇▇▇ 7.4%	sharp · at its level
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	80.2% ✎	▂▂▂▂▂▃▁▃▄▆█▆ beats 71/246	very easy · avg 90%	▇▇▇ 6.6%	sharp
Decision Index 0.4.1: BBH (accuracy) submitted	52.3% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 68/246	easy · avg 66%	▇▇▇ 6.3%	at its level
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	91.8% ✎	▁▁▂▁▂▂▃▂▃▆█▅ beats 73/246	very easy · avg 96%	▇▇ 4.2%	sharp · maxed out · noisy
Decision Index 0.4.1: WinoGrande (accuracy) submitted	68.3% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 70/246	easy · avg 78%	▇ 3.4%	at its level
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	40.5% ✎	▁··▄█▆▆▆▅▅▃▁ beats 111/246	medium · avg 45%	▇ 2.8%	sharp · at its level
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	32.1% ✎	▂▆▅▅▆█▆▃▁··▁ beats 67/246	medium · avg 39%	▇ 2.5%	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	55.2% ✎	▁▁▂█▅▄▁▁▁▁▁▁ beats 71/246	medium · avg 60%	▇ 1.1%	blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	33.9% ✎	██▄▃▃▄▁▁···▁ beats 18/246	medium · avg 58%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	26.9% ✎	▁▁▁▁▁▁▁▂▅█▂▁ beats 116/246	hard · avg 22%	▇ 0.9%	typical
Decision Index 0.4.1: CLadder (accuracy) submitted	78.1% ✎	▁███▆▅▃▃▁▁▁▁ beats 169/246	easy · avg 71%	▇ 0.9%	at its level
Decision Index 0.4.1: ChessBench (accuracy) submitted	15.7% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 119/246	hard · avg 16%	▇ 0.5%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	15.8% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 214/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 6.9% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	43.6800 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 118/247	avg 41.206 points	▇▇ 4.5%	blunt
Decision Index 0.4.1: private new domains (equated points)	20.5700 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 77/247	avg 38.861 points	▇ 2.5%	blunt

$ where to run it

Request access to model files ↗
Gated files: the publisher requires access approval. 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/LatentMesh-Labs ↗

$ 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		= 46.2

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	78.2%	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]	57.9%	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