whatmodel.sh / decision / mxbai-rerank-base-v2 (DecisionBench 3ea9d4df)

mxbai-rerank-base-v2 (DecisionBench 3ea9d4df)

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mxbai-rerank-base-v2 (DecisionBench 3ea9d4df) is an open-weights decision model from mixedbread-ai. Its published size is 0.494032768B parameters. Licence: apache-2.0.

Catalogue identity notes

DecisionBench v1 comparison: mixedbread-ai/mxbai-rerank-base-v2, revision 3ea9d4dffa7d12a4f366be8e275c349de9fc9865; classifier; adapter jev-compatible-server-v1; probability source native_probability_distribution. Kept separate from newer or unpinned Decision Index recipes; standard prompt, not compact. No unverified runtime or OpenRouter price imported. 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 ↗

Published served parameter count; not a RAM or download-size measurement.

27.4±1.6#320 of 355 decision modelsfrom 27 benchmark results across 1 source

$ how it compares

The decision models ranked either side of mxbai-rerank-base-v2 (DecisionBench 3ea9d4df), out of 357 with a score.

#	model	score	where	released
320	ZTC-Judge-4B (DecisionBench b0bb1603) FINAL-Bench	29±2	model files
321	Jev-Style 0.8B v3 chaoliangUNSW	28±1	model files	2026-09-24
322	mxbai-rerank-base-v2 (DecisionBench 3ea9d4df) mixedbread-ai	27±2	model files
323	open-jev-deberta-v3-large (DecisionBench 19bf9a64) com-kotobalabs	27±2	model files
324	Crivo-32M v2 tardellirs	27±1	model files

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

$ what it's good at

workflows▁▁▁▃▁▂▂▆█▄▁▁ #98 of 114 · 99% of its score · 27 tests

Trails GLiNER2.5-Decide here, though they rank lower overall.

test	result	vs the others in its run	difficulty	counts	why
DecisionBench v1 · action selection (all-request accuracy) submitted	32.3% ✎	▂▁▂▃▄▃▁▁▂█▇▄ beats 18/56	medium · avg 51%	▇▇▇▇▇▇ 15%	at its level
DecisionBench v1 · routing (all-request accuracy) submitted	33.7% ✎	▁▁▁▁▂▃▁▁▁▁▃█ beats 17/56	medium · avg 64%	▇▇▇▇▇ 13%	sharp · at its level
DecisionBench v1 · completion assessment (all-request accuracy) submitted	38.0% ✎	▁▁▂▃▄·▂▁▁▆█▂ beats 6/56	very easy · avg 86%	▇▇▇▇ 11%	sharp · at its level
DecisionBench v1 · bounded extraction (all-request accuracy) submitted	27.0% ✎	▃▃▂·▁▁▁▁▁▁█▅ beats 10/56	very easy · avg 86%	▇▇▇▇ 10%	sharp · at its level · noisy
DecisionBench v1 · navigation (all-request accuracy) submitted	17.0% ✎	▁▁·▃▁▁▁▁▁▁▃█ beats 9/56	medium · avg 58%	▇▇▇ 8.3%	sharp
DecisionBench v1 · triage (all-request accuracy) submitted	56.0% ✎	▁·▁·▂▃▄▃▂█▄▂ beats 16/56	easy · avg 78%	▇▇▇ 7.1%	sharp · at its level
DecisionBench v1 · record classification (all-request accuracy) submitted	24.0% ✎	▃▂▁▂▂▃▄▁▃▆██ beats 14/56	medium · avg 39%	▇▇ 6.0%	typical
DecisionBench v1 · verification (all-request accuracy) submitted	34.0% ✎	▁▃▅▁▃▂▂▁▁▇▇█ beats 3/56	medium · avg 56%	▇▇ 5.9%	blunt · at its level

19 more tests

test	result	vs the others in its run	difficulty	counts	why
DecisionBench v1 · content classification (all-request accuracy) submitted	41.0% ✎	▁····▁▁▁▃▁▁█ beats 10/56	easy · avg 73%	▇▇ 4.4%	at its level
DecisionBench v1 · document record classification (all-request accuracy) submitted	36.2% ✎	▂▂·▁▂▄▃▆▄█▁▁ beats 19/56	medium · avg 46%	▇▇ 3.9%	at its level
DecisionBench v1 · rubric scoring prioritization (all-request accuracy) submitted	26.6% ✎	▁▁▅▅▃▃▅█▃▃·▁ beats 14/56	medium · avg 45%	▇ 3.3%	at its level
DecisionBench v1 · review scoring (all-request accuracy) submitted	40.0% ✎	▁······▂▃▅▇█ beats 17/56	medium · avg 52%	▇ 2.4%	at its level
DecisionBench v1 · risk scoring (all-request accuracy) submitted	56.0% ✎	▁▁·▁▂▂▁▃▃█▇▃ beats 19/56	medium · avg 62%	▇ 1.9%	blunt · at its level
DecisionBench v1 · reasoning (all-request accuracy) submitted	36.3% ✎	▂·▁▁▆█▅▃▃▂▁▁ beats 14/56	medium · avg 52%	▇ 1.0%	at its level
DecisionBench v1 · retrieval ranking (all-request accuracy) submitted	8.0% ✎	▁▁·▁▁▃▃▂▃▁▁█ beats 7/56	medium · avg 35%	▇ 1.0%	noisy
DecisionBench v1 · retrieval verification (all-request accuracy) submitted	49.1% ✎	█▁▂▁▁▁▁▁▂▅▁▁ beats 4/56	easy · avg 74%	▇ 1.0%	at its level · noisy
DecisionBench v1 · target selection (all-request accuracy) submitted	11.0% ✎	▁▁·▁▁▁▃▃▂▁▁█ beats 20/56	hard · avg 29%	▇ 0.9%	noisy
DecisionBench v1 · document workflows (all-request accuracy) submitted	53.5% ✎	█▄▃▁▁▃▂▂▁·▁▁ beats 25/56	easy · avg 66%	▇ 0.8%	blunt · at its level
DecisionBench v1 · safety gating (all-request accuracy) submitted	34.0% ✎	▂·····▆█▂▂▄▇ beats 3/56	easy · avg 77%	▇ 0.6%	at its level · noisy
DecisionBench v1 · guardrails moderation (all-request accuracy) submitted	13.5% ✎	▃▁▁▂▆▃▃█···▁ beats 1/56	medium · avg 52%	▇ 0.5%	blunt · at its level · noisy
DecisionBench v1 · semantic filtering (all-request accuracy) submitted	84.0% ✎	▁·▁▁·▁▁█▂▃█▂ beats 45/56	easy · avg 71%	▇ 0.3%	blunt · at its level · noisy
DecisionBench v1 · content moderation (all-request accuracy) submitted	65.0% ✎	▁····▃▅▃▂▂·█ beats 17/56	easy · avg 82%	▇ 0.3%	at its level · noisy
DecisionBench v1 · context management (all-request accuracy) submitted	52.0% ✎	▁·····▁▇▆▁█▃ beats 10/56	easy · avg 73%	▇ 0.2%	blunt · at its level · noisy
DecisionBench v1 · document classification (all-request accuracy) submitted	28.0% ✎	▂▁·▂▁▁▁▄▁▂▃█ beats 36/56	hard · avg 21%	▇ 0.2%	blunt · noisy
DecisionBench v1 · entity alignment (all-request accuracy) submitted	100.0% ✎	▁·▁▄▇▄▅▄▂▁▄█ beats 49/56	easy · avg 66%	▇ 0.2%	at its level · noisy
DecisionBench v1 · function agent skill routing (all-request accuracy) submitted	96.7% ✎	▃▃▄▅▆▂▅▄▃▄█▃ beats 45/56	easy · avg 68%	▇ 0.1%	blunt · at its level · noisy
DecisionBench v1 · routing triage (all-request accuracy) submitted	66.8% ✎	█▁···▁·▂▆▅▂▂ beats 40/56	medium · avg 42%	▇ 0.0%	blunt · at its level · noisy

$ where to run it

Download model files ↗
Published model files verified. This is the revision recorded by the benchmark. 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/mixedbread-ai ↗

$ how the score was worked out · Hanno Labs DecisionBench v1 r… 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
Hanno Labs DecisionBench v1 reviewed results · submitted; independent execution unverified	27	▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 99%	CC0 1.0
prior · pull toward the middle		▇ 1%
weighted average		= 27.4

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data updated 2026-11-03 02:37 UTC · sources and licences · json