Wald-26B-A4B

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Wald-26B-A4B is an open-weights decision model from ORG2 AI. Its published size is 25.81B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact Decision Index tested engine wald-26b-a4b; reported model Wald-26B-A4B. Type: full fine-tune; 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.

61.7±1.3#91 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of Wald-26B-A4B, out of 357 with a score.

#	model	score	where	RAM
89	Shisa DE-2.1 (repeat) Shisa.AI	62±1	model files
90	Perplexity Decider Flash v1.2 (4B) Perplexity	62±1	local	~10GB
91	Wald-26B-A4B ORG2 AI	62±1	model files
92	Blink v0.3 26B-A4B (NVFP4) Pixilab.ai	62±1	model files
93	imajev-4b (DecisionBench 712891d1) mohit67890	62±2	model files

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$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #90 of 250 · 6.5% of its score · 6 tests

Beats Sieve 27B v2, Jev 1.13, Xor 26B-A4B v1.2 here, though they rank higher overall. Trails OneJev1.6-0.8B, Jev-Omni, baselabs joey-1 (2B) here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: API-Bank (accuracy) submitted	85.0% ✎	▁▁▁▁▁▁▁▂▃▂█▂ beats 161/236	easy · avg 70%	▇ 2.0%	sharp · noisy
Decision Index 0.4.1: BFCL (case exact accuracy) submitted	96.1% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 153/246	very easy · avg 92%	▇ 1.3%	sharp · maxed out · noisy
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	88.6% ✎	█▁▁▃▄▄▆▄▃▂▁▃ beats 204/243	medium · avg 37%	▇ 1.2%	sharp · at its level · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	67.0% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 183/246	medium · avg 60%	▇ 1.1%	blunt · at its level
Decision Index 0.4.1: When2Call (accuracy) submitted	87.9% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 194/246	easy · avg 72%	▇ 1.0%	noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	77.4% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #76 of 258 · 7.2% of its score · 7 tests

Beats AutoTrust-JEV-27B v1.1, oenogarum (Gemma 4 31B), Decider 31B (NVFP4) here, though they rank higher overall. Trails StartLux-Decision v1.1 0.8B, Standard One 3B SH (v4), Standard One 8B SH (v4) 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	45.2% ✎	▁▁▁▁▁▁▁▂▂▄▇█ beats 157/246	medium · avg 38%	▇ 2.8%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	56.4% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 174/246	medium · avg 47%	▇ 2.5%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	85.6% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 188/246	easy · avg 74%	▇ 0.6%	blunt
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	93.0% ✎	█▅▅▄▃▂▃▁▁▁·▁ beats 224/245	easy · avg 70%	▇ 0.6%	noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	86.9% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 91/238	easy · avg 83%	▇ 0.3%	sharp · noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	86.4% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 148/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	42.0% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #111 of 256 · 22% of its score · 9 tests

Beats jev-brain · Gemma-4-31B (decision readout), Decider chat · Gemma-4-31B (decision readout), garum (Gemma 4 31B) here, though they rank higher overall. Trails Decider 4B, jiwo 4B v2, Sieve 4B here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: ANLI (macro-F1) submitted	73.0% ✎	▁▂▃▅▅█▂····▁ beats 176/246	medium · avg 60%	▇▇ 5.5%	at its level
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	78.7% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 141/246	easy · avg 74%	▇▇ 5.3%	typical
Decision Index 0.4.1: HellaSwag (accuracy) submitted	92.0% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 141/246	very easy · avg 85%	▇▇ 3.8%	sharp · maxed out
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	69.2% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 229/246	medium · avg 44%	▇ 2.6%	at its level
Decision Index 0.4.1: ACOS (per-review F1) submitted	29.7% ✎	▁··▂▃▃▅▇█▇▃▃ beats 164/246	hard · avg 22%	▇ 1.5%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	74.7% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 167/246	medium · avg 62%	▇ 1.1%	at its level
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	76.3% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 130/246	easy · avg 71%	▇ 1.0%	noisy
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	85.4% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 235/246	easy · avg 66%	▇ 0.8%	noisy

1 more test

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: FinEntity (macro-F1) submitted	87.3% ✎	▁···▁▁▁▁▃▆█▃ beats 116/246	easy · avg 84%	▇ 0.6%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #77 of 247 · 10% of its score · 6 tests

Beats Jev 1.13, Quyet-1.0-Large, jev-brain · Gemma-4-31B (decision readout) here, though they rank higher overall. Trails Amber 0.4B, Crivo-1B, Kas 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	69.1% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 148/246	medium · avg 61%	▇▇ 4.1%	at its level
Decision Index 0.4.1: Humicroedit (accuracy) submitted	62.5% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 161/246	medium · avg 60%	▇ 2.1%	blunt · at its level
Decision Index 0.4.1: cfcolor (accuracy) submitted	62.6% ✎	▁········▁▇█ beats 136/246	medium · avg 60%	▇ 1.2%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	64.8% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 188/237	hard · avg 21%	▇ 1.2%	sharp · at its level · noisy
Decision Index 0.4.1: BPoMP (accuracy) submitted	92.2% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 169/246	easy · avg 83%	▇ 1.0%	sharp · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	60.1% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 204/246	medium · avg 47%	▇ 0.4%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #89 of 263 · 44% of its score · 14 tests

Beats StartLux-Decision v1.1 4B, KnowLine 4B Gen5.5, Jeffan-4B-2010 here, though they rank higher overall. Trails Solar Decide, Clef-flash, Xor 1.2 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	61.0% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 161/246	medium · avg 46%	▇▇▇▇ 11%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	83.6% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 174/246	easy · avg 70%	▇▇▇ 6.4%	sharp
Decision Index 0.4.1: BBH (accuracy) submitted	77.3% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 173/246	easy · avg 66%	▇▇ 6.2%	typical
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	71.0% ✎	▁··▄█▆▆▆▅▅▃▁ beats 189/246	medium · avg 45%	▇ 3.7%	sharp · at its level
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	47.4% ✎	▂▆▅▅▆█▆▃▁··▁ beats 170/246	medium · avg 39%	▇ 3.7%	blunt · at its level
Decision Index 0.4.1: WinoGrande (accuracy) submitted	84.1% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 154/246	easy · avg 78%	▇ 2.9%	typical
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	95.9% ✎	▂▂▂▂▂▂▁▃▄▆█▆ beats 160/246	very easy · avg 90%	▇ 2.5%	sharp · maxed out
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	39.9% ✎	▁▁▁▁▁▁▁▂▅█▃▁ beats 226/246	hard · avg 22%	▇ 1.8%	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	68.6% ✎	▁▁▂█▅▄▁▁▁▁▁▁ beats 205/246	medium · avg 60%	▇ 1.3%	blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	69.1% ✎	██▄▃▃▄▁▁···▁ beats 165/246	medium · avg 58%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	98.8% ✎	▁▁▂▁▂▂▂▂▃▆█▅ beats 166/246	very easy · avg 96%	▇ 1.1%	sharp · maxed out · noisy
Decision Index 0.4.1: CLadder (accuracy) submitted	76.3% ✎	▁███▆▅▃▃▁▁▁▁ beats 160/246	easy · avg 71%	▇ 1.0%	typical
Decision Index 0.4.1: ChessBench (accuracy) submitted	19.9% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 168/246	hard · avg 16%	▇ 0.8%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	11.4% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 78/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 10% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	55.9800 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 178/247	avg 41.206 points	▇▇ 6.2%	blunt
Decision Index 0.4.1: private new domains (equated points)	54.4100 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 170/247	avg 38.861 points	▇ 3.4%	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/org2ai ↗

$ 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		= 61.7

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	77.4%	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]	42.0%	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