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Darwin-27B-ZTC

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Darwin-27B-ZTC is an open-weights decision model from FINAL_Bench. Its published size is 25.62B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact Decision Index tested engine darwin-27b-ztc; reported model Darwin-27B-ZTC. 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.

66.3±1.4#45 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of Darwin-27B-ZTC, out of 357 with a score.

#	model	score	where	released
43	Decider 31B (NVFP4) Mapika	66±1	model files	2026-10
44	Lychee-27B LycheeAI	66±1	model files
45	Darwin-27B-ZTC FINAL_Bench	66±1	model files
46	FlexDM · Gemma-4-31B (decision readout) 1000luca	66±1	source code
47	Standard One 35B SH (v1) Standard Thinking	66±1	model files

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

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #73 of 250 · 5.8% of its score · 6 tests

Beats Sieve 27B v2 here, though they rank higher overall. Trails baselabs joey-1 (2B), Hopper 12B (trained), Solomon v1.1 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	84.8% ✎	▁▁▁▁▁▁▁▂▃▂█▂ beats 154/236	easy · avg 70%	▇ 1.7%	sharp · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	67.5% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 192/246	medium · avg 60%	▇ 1.1%	blunt · at its level
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	100.0% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 243/243	medium · avg 37%	▇ 1.1%	sharp · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	92.2% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 240/246	easy · avg 72%	▇ 1.0%	noisy
Decision Index 0.4.1: BFCL (case exact accuracy) submitted	96.7% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 172/246	very easy · avg 92%	▇ 1.0%	sharp · maxed out · noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	64.6% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #39 of 258 · 7.7% of its score · 7 tests

Beats Perplexity Decider v1.1 (27B), H2O Lightning 31B v1.1, AutoTrust-JEV-27B v1.1 here, though they rank higher overall. Trails StartLux-Decision v1.1 2B, GEV-26B-Decide, JEV-35B 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	48.4% ✎	▁▁▁▁▁▁▁▂▂▄▇█ beats 205/246	medium · avg 38%	▇ 3.2%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	59.9% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 211/246	medium · avg 47%	▇ 2.8%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	88.8% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 229/246	easy · avg 74%	▇ 0.6%	blunt
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	50.6% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 40/245	easy · avg 70%	▇ 0.6%	noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	91.8% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 209/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	94.6% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 199/238	easy · avg 83%	▇ 0.3%	sharp · maxed out · noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	36.0% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #16 of 256 · 22% of its score · 9 tests

Beats KnowLine 31B Gen1, Ainvest-Decision-27B v1.0, WeMM-JEV-27B here, though they rank higher overall. Trails Clef-omni, Clef 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	77.2% ✎	▁▂▃▅▅█▂····▁ beats 218/246	medium · avg 60%	▇▇ 5.5%	typical
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	86.3% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 230/246	easy · avg 74%	▇▇ 5.2%	typical
Decision Index 0.4.1: HellaSwag (accuracy) submitted	97.6% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 232/246	very easy · avg 85%	▇ 3.0%	sharp · maxed out
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	67.7% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 216/246	medium · avg 44%	▇ 2.8%	at its level
Decision Index 0.4.1: ACOS (per-review F1) submitted	37.3% ✎	▁··▂▃▃▅▇█▇▃▃ beats 191/246	hard · avg 22%	▇ 1.8%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	81.1% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 237/246	medium · avg 62%	▇ 1.1%	at its level
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	86.0% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 239/246	easy · avg 71%	▇ 1.0%	noisy
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	84.8% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 231/246	easy · avg 66%	▇ 0.7%	noisy

1 more test

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: FinEntity (macro-F1) submitted	73.5% ✎	▁···▁▁▁▁▃▆█▃ beats 51/246	easy · avg 84%	▇ 0.5%	maxed out · noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #49 of 247 · 10% of its score · 6 tests

Beats Jeffan-31b-1710, vLLM-SR d3-turbo v1.1 (27B), Nace.AI Drex Max v1 (27B) here, though they rank higher overall. Trails Kas 4B, baselabs joey-1 (2B), Clef-flash 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	81.8% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 229/246	medium · avg 61%	▇▇ 4.0%	typical
Decision Index 0.4.1: Humicroedit (accuracy) submitted	68.3% ✎	▁·▁▃▂▂▅█▄▃▁▁ beats 232/246	medium · avg 60%	▇ 2.3%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	24.8% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 135/237	hard · avg 21%	▇ 1.3%	sharp · at its level · noisy
Decision Index 0.4.1: cfcolor (accuracy) submitted	72.9% ✎	▁········▁▇█ beats 246/246	medium · avg 60%	▇ 1.3%	blunt · at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	95.3% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 197/246	easy · avg 83%	▇ 0.9%	sharp · maxed out · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	62.9% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 228/246	medium · avg 47%	▇ 0.4%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #50 of 263 · 43% of its score · 14 tests

Beats OneJev1.6-27B, baselabs elephant-1 (9B), StartLux-Decision v1.1 9B here, though they rank higher overall. Trails Solar Decide, Clef-flash, Clef-omni 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	68.0% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 195/246	medium · avg 46%	▇▇▇▇▇ 11%	sharp · at its level
Decision Index 0.4.1: BBH (accuracy) submitted	78.5% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 181/246	easy · avg 66%	▇▇ 5.9%	typical
Decision Index 0.4.1: MMLU (accuracy) submitted	87.7% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 215/246	easy · avg 70%	▇▇ 5.9%	sharp
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	57.6% ✎	▂▆▅▅▆█▆▃▁··▁ beats 241/246	medium · avg 39%	▇▇ 4.1%	blunt · at its level
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	52.6% ✎	▁··▄█▆▆▆▅▅▃▁ beats 143/246	medium · avg 45%	▇ 3.7%	sharp · at its level
Decision Index 0.4.1: WinoGrande (accuracy) submitted	93.0% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 217/246	easy · avg 78%	▇ 2.7%	typical
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	23.3% ✎	▁▁▁▁▁▁▁▂▅█▂▁ beats 93/246	hard · avg 22%	▇ 2.1%	at its level
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	97.1% ✎	▂▂▂▂▂▂▁▃▄▆█▆ beats 204/246	very easy · avg 90%	▇ 1.8%	sharp · maxed out

6 more tests

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: MuSR (accuracy) submitted	62.9% ✎	▁▁▂█▅▄▁▁▁▁▁▁ beats 157/246	medium · avg 60%	▇ 1.4%	blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	71.9% ✎	██▄▃▃▄▁▁···▁ beats 175/246	medium · avg 58%	▇ 1.2%	at its level · noisy
Decision Index 0.4.1: ChessBench (accuracy) submitted	17.1% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 132/246	hard · avg 16%	▇ 1.0%	blunt
Decision Index 0.4.1: CLadder (accuracy) submitted	75.0% ✎	▁███▆▅▃▃▁▁▁▁ beats 155/246	easy · avg 71%	▇ 1.0%	typical
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	99.4% ✎	▁▁▂▁▂▂▂▂▃▆█▅ beats 244/246	very easy · avg 96%	▇ 0.7%	sharp · maxed out · noisy
Decision Index 0.4.1: HLE (accuracy) submitted	8.8% ✎	▁▁▃▃▆█▇▅▅▃▁▁ beats 9/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 11% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	58.3400 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 197/247	avg 41.206 points	▇▇▇ 6.8%	blunt
Decision Index 0.4.1: private new domains (equated points)	70.0700 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 213/247	avg 38.861 points	▇▇ 3.8%	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/FINAL-Bench ↗

$ 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		= 66.3

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	64.6%	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]	36.0%	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