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Together Tev1 Qwen3.5-0.8B

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Together Tev1 Qwen3.5-0.8B is an open-weights decision model from Together. Its published size is 0.87B parameters.

Catalogue identity notes

Exact Decision Index tested engine tev1-0.8b; reported model Together Tev1 Qwen3.5-0.8B. Type: full fine-tune; technique: autoregressive. Implementation: https://github.com/togethercomputer/tev1. 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

· Parameter count ↗

Publisher parameter count; includes the served backbone, not just adapter size.

30.6±1.1#304 of 355 decision modelsfrom 38 benchmark results across 1 source

$ how it compares

The decision models ranked either side of Together Tev1 Qwen3.5-0.8B, out of 357 with a score, then more from Together.

#	model	score	where	RAM	released
304	vLLM-SR Decision 2.0 Eos 0.8B vLLM Semantic Router	31±1	local	~5.8GB
305	DecisionTune 1.0 DecisionTune	31±1	model files
306	Together Tev1 Qwen3.5-0.8B Together	31±1	model files
307	Dinah-1 Lukitaduarte	30±1	model files
308	Kev 0.8B r15 Jared Palmer	30±1	local	~4.6GB	2026-09-24
	more from Together
154	Tev1-4B-experimental (DecisionBench 0b7becf0) Together	56±2	model files
202	Together Tev1 Qwen3.5-4B Together	51±1	model files

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

$ what it's good at

tools & automation▁▃▁▁▃▂▃▄▆██▆ #220 of 250 · 8.1% of its score · 5 tests

Beats DM-JEPA 1.1, Kodiak v0.2 1B (accuracy), JEV FLAN-T5 Large public v2 (342M) here, though they rank higher overall. Trails Prumo (rule-based engine), JevK5 Lite, Crivo-32M v2 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	66.1% ✎	▁▂▁▁▃▂▂▄▅▆█▁ beats 29/246	very easy · avg 92%	▇▇ 6.0%	sharp · at its level · noisy
Decision Index 0.4.1: When2Call (accuracy) submitted	66.8% ✎	▁▁▁▂▃▅▅▆█▇█▃ beats 85/246	easy · avg 72%	▇ 1.1%	at its level · noisy
Decision Index 0.4.1: ToolRet (nDCG@10) submitted	57.2% ✎	▁▁▁▁▁·▁▁▁▄█▁ beats 51/246	medium · avg 60%	▇ 0.7%	blunt · at its level
Decision Index 0.4.1: Home appliance simulator (case exact accuracy) submitted	1.1% ✎	█▁▁▃▄▄▆▄▁▂▁▃ beats 50/243	medium · avg 37%	▇ 0.2%	sharp · too hard for it · noisy
Decision Index 0.4.1: RouterBench (selected quality (quality objective)) submitted	58.7% ✎	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▁▁▁·▁▃▁▁▃▃█▅ #245 of 258 · 3.8% of its score · 5 tests

Beats DM-JEPA 1.1 here, though they rank higher overall. Trails Laya, Prumo (rule-based engine), GLiNER2.5-Decide 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	21.8% ✎	▁▁▁▁▁▃▂▂▃▃█▅ beats 11/246	medium · avg 47%	▇ 1.4%	at its level
Decision Index 0.4.1: BRIGHT (nDCG@10) submitted	29.4% ✎	▁▁▁▁▁▁▁▃▂▄▇█ beats 47/246	medium · avg 38%	▇ 1.3%	blunt · at its level
Decision Index 0.4.1: HoVer (accuracy) submitted	53.1% ✎	▆▆▇▅▄▄▃▇▄█▆▄ beats 18/246	easy · avg 74%	▇ 0.6%	blunt · at its level
Decision Index 0.4.1: PhishNChips phishing decisions (accuracy) submitted	50.0% ✎	█▅▅▄▃▂▁▁▁▁·▁ beats 17/245	easy · avg 70%	▇ 0.5%	at its level · noisy
Decision Index 0.4.1: SGD/SGD-X (macro-F1) submitted	59.7% ✎	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▂▂▃▃▂▅▆▃█▆▄▃ #228 of 256 · 24% of its score · 9 tests

Beats Valen-0.8B, vLLM-SR Decision 2.0 Eos 0.8B here, though they rank higher overall. Trails JevK5 Lite, LFM2.5-2.6B-RLCD, JEV Ettin 400M 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	44.8% ✎	▂▄▂▂▄▇▃▇█▅▃▁ beats 29/246	very easy · avg 85%	▇▇▇▇ 10%	sharp · at its level
Decision Index 0.4.1: NLI4CT (macro-F1) submitted	61.3% ✎	▁▁▁▂▂▃▃▄▇▇█▄ beats 44/246	easy · avg 74%	▇▇ 5.4%	at its level
Decision Index 0.4.1: ANLI (macro-F1) submitted	37.6% ✎	▁▂▃▅▅█▂····▁ beats 30/246	medium · avg 60%	▇▇ 3.9%	at its level
Decision Index 0.4.1: iSarcasmEval (Sarcasm F1 · track A, English) submitted	21.3% ✎	▁·▁·▁▁▂▄▅█▇▄ beats 27/246	medium · avg 44%	▇ 1.2%	typical
Decision Index 0.4.1: ContractNLI (macro-F1) submitted	46.1% ✎	▁▁▁▁▁▁▃▂▂▇▇█ beats 30/246	easy · avg 71%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: FinEntity (macro-F1) submitted	57.8% ✎	▁···▁▁▁▃▃▆█▃ beats 19/246	easy · avg 84%	▇ 1.0%	at its level · noisy
Decision Index 0.4.1: VAST (macro-F1) submitted	44.6% ✎	▁▁▁▂▅▄▄▂▄▆█▄ beats 57/246	medium · avg 62%	▇ 0.8%	at its level
Decision Index 0.4.1: RAGTruth (F1 on hallucinated class) submitted	18.3% ✎	▁▁▁▁▁▁▃▁▁▃▆█ beats 21/246	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	5.4% ✎	▁··▂▃▃▅▇█▇▃▃ beats 27/246	hard · avg 22%	▇ 0.3%	noisy

arts & human taste▁▇▄▃▅▅█▅▄▂▁▁ #227 of 247 · 7.3% of its score · 5 tests

Beats JEV Ettin 150M, JPT-0.8B Qwen3.5-0.8B LoRA, Sifr 0.8B v3.1 here, though they rank higher overall. Trails LFM2.5-350M-RLCD, system-one-gemma, Bekko System One v0 17M 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	30.7% ✎	▁▁▃▃▃▃▇▅█▅▃▁ beats 19/246	medium · avg 61%	▇ 3.0%	at its level
Decision Index 0.4.1: BPoMP (accuracy) submitted	50.7% ✎	▁█▄▄▅█▆▅▄▁▁▁ beats 25/246	easy · avg 83%	▇ 1.8%	sharp · at its level · noisy
Decision Index 0.4.1: Humicroedit (accuracy) submitted	51.3% ✎	▁·▁▃▂▂▅█▄▂▁▁ beats 34/246	medium · avg 60%	▇ 1.4%	blunt · at its level
Decision Index 0.4.1: cfcolor (accuracy) submitted	53.7% ✎	▁········▁▇█ beats 49/246	medium · avg 60%	▇ 0.8%	blunt · at its level
Decision Index 0.4.1: Habermas Machine (accuracy) submitted	40.0% ✎	▁▂▄█▇▅▁▁▄▄▁▁ beats 63/246	medium · avg 47%	▇ 0.2%	blunt · at its level

knowledge & reasoning▁▂▂▃▂▃▃▅▅▆█▂ #219 of 263 · 50% of its score · 14 tests

Beats vLLM-SR vela3-fast (Vela 3.0, 0.4B), 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-Easy (accuracy) submitted	82.0% ✎	▁▁▂▁▃▂▂▂▃▆█▅ beats 46/246	very easy · avg 96%	▇▇▇▇▇ 12%	sharp · at its level · noisy
Decision Index 0.4.1: ARC-Challenge (accuracy) submitted	62.0% ✎	▂▂▂▃▂▂▁▃▄▆█▆ beats 43/246	very easy · avg 90%	▇▇▇▇ 11%	sharp · at its level
Decision Index 0.4.1: MMLU (accuracy) submitted	45.9% ✎	▁▄▄▃▃▂▅▇▅█▇▁ beats 47/246	easy · avg 70%	▇▇▇ 6.7%	sharp · at its level
Decision Index 0.4.1: BBH (accuracy) submitted	43.6% ✎	▄▆▄▄█▇▇▄▃▁▁▁ beats 47/246	easy · avg 66%	▇▇ 5.3%	at its level
Decision Index 0.4.1: MMLU-Pro (accuracy) submitted	22.9% ✎	▄▄▃▃▂▄▅▇██▅▁ beats 44/246	medium · avg 46%	▇▇ 4.9%	sharp
Decision Index 0.4.1: WinoGrande (accuracy) submitted	52.3% ✎	█▅█▅▄█▆▄▃▃▁▁ beats 21/246	easy · avg 78%	▇ 3.7%	at its level
Decision Index 0.4.1: GPQA Diamond (accuracy) submitted	27.6% ✎	▂▆▅▅▆█▆▃▁··▁ beats 44/246	medium · avg 39%	▇ 1.8%	blunt · at its level
Decision Index 0.4.1: GSM8K (accuracy (4- and 10-choice)) submitted	19.9% ✎	▁··▄█▆▆▆▅▅▃▁ beats 36/246	medium · avg 45%	▇ 1.5%	sharp

6 more tests

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: MuSR (accuracy) submitted	52.0% ✎	▁▁▃█▅▄▁▁▁▁▁▁ beats 42/246	medium · avg 60%	▇ 0.9%	blunt · at its level
Decision Index 0.4.1: CLadder (accuracy) submitted	50.5% ✎	▁███▆▅▃▃▁▁▁▁ beats 18/246	easy · avg 71%	▇ 0.9%	at its level
Decision Index 0.4.1: CRUXEval (accuracy) submitted	38.1% ✎	██▄▃▃▄▁▁···▁ beats 43/246	medium · avg 58%	▇ 0.8%	at its level · noisy
Decision Index 0.4.1: SATA-Bench (case exact accuracy) submitted	5.8% ✎	▁▁▁▁▁▃▁▂▅█▂▁ beats 36/246	hard · avg 22%	▇ 0.5%	typical
Decision Index 0.4.1: ChessBench (accuracy) submitted	19.5% ✎	▄▇▄▄▄▆▄█▄▁▁▁ beats 163/246	hard · avg 16%	▇ 0.3%	blunt
Decision Index 0.4.1: HLE (accuracy) submitted	14.9% ✎	▁▁▂▃▆█▇▅▅▃▁▁ beats 194/246	very hard · avg 13%	▇ 0.0%	blunt

other tests2 not in a task · 5.8% of its score

test	result	vs the others in its run	difficulty	counts	why
Decision Index 0.4.1: private same skills (equated points)	11.9400 points ✎	▂▃▂▃▃▃▄▅▅▇█▃ beats 18/247	avg 41.206 points	▇▇ 3.8%	blunt
Decision Index 0.4.1: private new domains (equated points)	13.2700 points ✎	▃█▃▃▂▆▃▃▄▄▄▄ beats 44/247	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/togethercomputer ↗

$ 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	38	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 100%	CC BY 4.0
prior · pull toward the middle		▇ 0%
weighted average		= 30.6

not counted (2)

Decision Index 0.4.1	Decision Index 0.4.1: RouterBench (selected quality (quality objective)) [correct]	58.7%	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]	59.7%	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