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Jebadiah 27B v2.1 (T1e)

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Jebadiah 27B v2.1 (T1e) is an open-weights decision model from frontier-infra, released October 2026. Its published size is 27.781B parameters. Licence: apache-2.0.

Catalogue identity notes

Exact Decision Index 0.4.1 text-board engine jebadiah-27b-v2.1-t1e. Produces probabilities over supplied choices, not conversational text. Only public per-benchmark results are used; no Full index, private scores or latency is treated as an ability measurement. Model and runtime documentation reviewed 2026-10-31; no fresh hardware execution. T1e uses repeated payloads, forward/reverse choice scoring with threshold 0.7, and the shipped calibration. Standard-read Ollama/GGUF/MLX does not reproduce these scores; no such variant is attached.

Model information sources

Release date ↗ · Weights licence ↗ · Parameter count ↗

HF safetensors metadata for full weights; Strands uses its documented approximate served backbone size. Memory estimate: BF16 weights plus 20% runtime allowance and 2 GB; not a measured requirement.

65.1±1.4#57 of 355 decision modelsfrom 42 benchmark results across 1 source

$ how it compares

The decision models ranked either side of Jebadiah 27B v2.1 (T1e), out of 357 with a score, then more from frontier-infra.

#	model	score	where	RAM	released
55	Quyet-1.0-Large chinhnc	65±1	model files
56	Mercury Decide (paid endpoint) Inception	65±1	api price unknown
57	Jebadiah 27B v2.1 (T1e) frontier-infra	65±1	local	~69GB	2026-10
58	OneJev1.5-27B OmniJev	65±4	model files		2026-10-13
59	jev-brain · Gemma-4-31B (decision readout) wcole3	65±1	source code
	more from frontier-infra
125	Jebadiah 27B v2.1 (Ollaya Q4_K_M) frontier-infra	59±4	local	~16GB	2026-10
143	Jebadiah 9B v2.1 (T1e) frontier-infra	57±1	local	~25GB	2026-10
160	Jebadiah 9B v2.1 (Ollaya Q8_0) frontier-infra	55±3	local	~9.1GB	2026-10

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

$ what it's good at

tools & automation▁▃▁▁▁▂▃▄▆██▆ #62 of 250 · 6.0% of its score · 6 tests

Beats OneShot 27B, mogan-decision-31B-it, Sieve 27B v2 here, though they rank higher overall. Trails baselabs joey-1 (2B), Solomon v1.1, Amber Hybrid 4B (decision readout) 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 164/236	easy · avg 70%	▇ 1.8%	sharp · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	66.1% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 162/246	medium · avg 60%	▇ 1.1%	blunt · at its level
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	80.7% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 196/243	medium · avg 37%	▇ 1.1%	sharp · noisy
Decision Index 0.4.1: BFCL (case exact accuracy) submitted	97.2% ✎	▁▂▁▁▁▂▂▄▅▆█▁ beats 186/246	very easy · avg 92%	▇ 1.0%	sharp · maxed out · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	77.0% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 124/246	easy · avg 72%	▇ 1.0%	noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	79.8% ✎	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▁▁▁·▁▁▁▁▃▃█▅ #64 of 258 · 7.6% of its score · 7 tests

Beats AutoTrust-JEV-27B v1.1, oenogarum (Gemma 4 31B), Aleria Judge 1.1.1 (Gemma 4 31B) (decision readout) here, though they rank higher overall. Trails StartLux-Decision v1.1 0.8B, Standard One 8B SH (v4), StartLux-Decision v1.1 2B 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	50.1% ✎	▁▁▁▁▁▁▁▂▂▄▇█ beats 232/246	medium · avg 38%	▇ 3.1%	blunt · at its level
Decision Index 0.4.1: Amazon ESCI (macro-F1) submitted	57.9% ✎	▁▁▁▁▁▁▂▂▃▃█▅ beats 190/246	medium · avg 47%	▇ 2.7%	at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	79.3% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 140/246	easy · avg 74%	▇ 0.6%	blunt
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	67.7% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 119/245	easy · avg 70%	▇ 0.6%	noisy
Decision Index 0.4.1: BANKING77 (macro-F1) submitted	79.6% ✎	▁·▁▁▁▁▁▁▂█▇█ beats 93/238	easy · avg 79%	▇ 0.3%	noisy
Decision Index 0.4.1: CLINC150+OOS (macro-F1) submitted	89.3% ✎	▁▁▁▁▁▁▁▂▃▆█▄ beats 115/238	easy · avg 83%	▇ 0.3%	sharp · maxed out · noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	21.3% ✎	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▂▂▂▃▂▅▆▃█▆▄▃ #56 of 256 · 22% of its score · 9 tests

Beats Ainvest-Decision-27B v1.0, Turion Jev 31B, MISA AI 2.0 Decision Plus · oak 1.1 (31B) here, though they rank higher overall. Trails Decider 35B-A3B, Sieve 9B v2, Clef-flash 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	79.0% ✎	▁▂▃▅▅█▃····▁ beats 229/246	medium · avg 60%	▇▇ 5.5%	typical
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	86.1% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 225/246	easy · avg 74%	▇▇ 5.2%	typical
Decision Index 0.4.1: HellaSwag (accuracy) submitted	95.0% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 193/246	very easy · avg 85%	▇ 3.2%	sharp · maxed out
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	62.0% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 195/246	medium · avg 44%	▇ 2.7%	at its level
Decision Index 0.4.1: ACOS (per-review F1) submitted	23.9% ✎	▁··▂▃▃▅▇█▇▃▃ beats 135/246	hard · avg 22%	▇ 1.7%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	77.3% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 187/246	medium · avg 62%	▇ 1.1%	at its level
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	74.5% ✎	▁▁▁▁▁▁▁▂▂▇▇█ beats 109/246	easy · avg 71%	▇ 1.0%	noisy
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	80.9% ✎	▁▁▁▁▁▁▁▁▁▃▆█ beats 190/246	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	90.6% ✎	▁···▁▁▁▁▃▆█▃ beats 155/246	easy · avg 84%	▇ 0.5%	maxed out · noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #41 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), 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: New Yorker (accuracy) submitted	76.7% ✎	▁▁▂▃▃▃▇▅█▅▃▁ beats 203/246	medium · avg 61%	▇▇ 4.1%	typical
Decision Index 0.4.1: Humicroedit (accuracy) submitted	61.6% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 139/246	medium · avg 60%	▇ 2.3%	blunt · at its level
Decision Index 0.4.1: POP909-CL (accuracy) submitted	35.9% ✎	▄▅▅▃▆▅▃▅▂▅▃█ beats 155/237	hard · avg 21%	▇ 1.3%	sharp · at its level · noisy
Decision Index 0.4.1: cfcolor (accuracy) submitted	66.1% ✎	▁········▁▇█ beats 208/246	medium · avg 60%	▇ 1.3%	blunt · at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	95.8% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 206/246	easy · avg 83%	▇ 0.9%	sharp · maxed out · noisy
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	47.0% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 161/246	medium · avg 47%	▇ 0.4%	blunt · at its level

knowledge & reasoning▁▂▂▂▂▃▃▅▅▆█▂ #51 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	68.4% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 199/246	medium · avg 46%	▇▇▇▇▇ 11%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	86.9% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 207/246	easy · avg 70%	▇▇ 6.0%	sharp
Decision Index 0.4.1: BBH (accuracy) submitted	82.4% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 203/246	easy · avg 66%	▇▇ 6.0%	typical
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	54.6% ✎	▂▆▅▅▆█▆▃▁··▁ beats 231/246	medium · avg 39%	▇▇ 4.0%	blunt · at its level
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	68.5% ✎	▁··▄█▆▆▆▅▅▃▁ beats 182/246	medium · avg 45%	▇ 3.7%	sharp · at its level
Decision Index 0.4.1: WinoGrande (accuracy) submitted	90.8% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 201/246	easy · avg 78%	▇ 2.8%	typical
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	25.3% ✎	▁▁▁▁▁▁▁▂▅█▂▁ beats 106/246	hard · avg 22%	▇ 2.0%	at its level
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	97.3% ✎	▂▂▂▂▂▂▁▃▄▆█▆ beats 215/246	very easy · avg 90%	▇ 2.0%	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	65.0% ✎	▁▁▂█▅▄▁▁▁▁▁▁ beats 178/246	medium · avg 60%	▇ 1.4%	blunt · at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	81.9% ✎	██▄▃▃▄▁▁···▁ beats 203/246	medium · avg 58%	▇ 1.2%	at its level · noisy
Decision Index 0.4.1: CLadder (accuracy) submitted	77.9% ✎	▁███▆▅▃▃▁▁▁▁ beats 167/246	easy · avg 71%	▇ 1.0%	typical
Decision Index 0.4.1: ChessBench (accuracy) submitted	16.1% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 122/246	hard · avg 16%	▇ 1.0%	blunt
Decision Index 0.4.1: ARC-Easy (accuracy) submitted	99.2% ✎	▁▁▂▁▂▂▂▂▃▆█▅ beats 217/246	very easy · avg 96%	▇ 0.8%	sharp · maxed out · noisy
Decision Index 0.4.1: HLE (accuracy) submitted	10.6% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 52/246	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)	58.9800 points ✎	▂▂▂▃▃▃▄▅▅▇█▃ beats 205/247	avg 41.206 points	▇▇▇ 6.7%	blunt
Decision Index 0.4.1: private new domains (equated points)	72.5200 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 222/247	avg 38.861 points	▇ 3.7%	blunt

$ where to run it

Other ways to run it

Run it as published · needs ~68.7 GB, NVIDIA only · from huggingface.co/frontier-infra (official)
pip install torch==2.8.0 transformers==5.17.0 accelerate safetensors git+https://github.com/apolinario/decision-index.git@25c6ec7b6b5f44a216c63c3a700de3a9c6d6699d && git clone https://github.com/getainode/jebadiah.git jebadiah-t1e && git -C jebadiah-t1e checkout 678f6e2cc2bb1b51f34407d00255b5e13109ce3f && PYTHONPATH=jebadiah-t1e/eval/decision-index python -c 'from jebadiah_engine import JebadiahEngine; e=JebadiahEngine(model="frontier-infra/jebadiah-27b-v2-1", revision="e38b48b82bcda8943d0958072c042c5867284838", device="cuda", dtype="bfloat16", repeat_payload=True, reverse_avg_below=0.7, prefix_cache=True, max_batch=64, batch_tokens=32768); print(e("The invoice is overdue.", {"overdue":{"type":"noul","instructions":"Is the invoice overdue?"}}))'

Download model files ↗
Published model files verified. This is a verified repository snapshot; the benchmark does not establish an identical weight revision. See the local setup below for the reviewed runtime. Source · checked 2026-10-31.

Model repository and documentation: huggingface.co/frontier-infra ↗

$ 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		= 65.1

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	79.8%	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]	21.3%	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