whatmodel.sh / decision / imajev-4b (DecisionBench c9e5f132)

imajev-4b (DecisionBench c9e5f132)

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imajev-4b (DecisionBench c9e5f132) is an open-weights decision model from mohit67890. Its published size is 4.782417408B parameters. Licence: apache-2.0.

Catalogue identity notes

DecisionBench v1 comparison: mohit67890/imajev-4b, revision c9e5f132465da85d31735ec502d5557982671a7d; decision-model; adapter imajev-serving-systemone-v1; probability source trained_readout_4_rotation_mean_temperature_calibrated_v1. 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.

60.5±1.8#107 of 355 decision modelsfrom 27 benchmark results across 1 source

$ how it compares

The decision models ranked either side of imajev-4b (DecisionBench c9e5f132), out of 357 with a score, then more from mohit67890.

#modelscorewhereRAMreleased
106reflex Qwen3.8-27B-FP8 (pmscr) (decision readout) kshetrajna1261±1source code
107OneJev1.5-9B OmniJev61±4model files2026-10-13
108imajev-4b (DecisionBench c9e5f132) mohit6789061±2model files
109AJev lora1 (Gemma 4 26B-A4B) andyzhang23260±1model files
110decider-35b-a3b (DecisionBench d9783c58) Mapika60±2model files2026-09-20
more from mohit67890
93imajev-4b (DecisionBench 712891d1) mohit6789062±2model files

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

$ what it's good at

workflows▁▁▁▂▁▂▂▆█▄▁▁ #24 of 114 · 99% of its score · 27 tests

Beats Nace.AI Drex Max v1 (27B), d1.1 (hosted), Span-02 (Gemma 4 26B-A4B) here, though they rank higher overall.

testresultvs the others in its rundifficultycountswhy
DecisionBench v1 · action selection (all-request accuracy) submitted61.6% ✎▂▁▂▃▄▃▁▁▂█▇▄ beats 45/56medium · avg 51%▇▇▇▇▇▇▇▇ 21%at its level
DecisionBench v1 · routing (all-request accuracy) submitted76.7% ✎▁▁▁▁▂▂▁▁▁▁▃█ beats 48/56medium · avg 64%▇▇▇▇▇ 13%sharp
DecisionBench v1 · navigation (all-request accuracy) submitted71.0% ✎▁▁·▃▁▁▁▁▁▁▃█ beats 37/56medium · avg 58%▇▇▇▇ 10%sharp · at its level
DecisionBench v1 · record classification (all-request accuracy) submitted44.0% ✎▃▂▁▂▂▂▄▁▃▆██ beats 36/56medium · avg 39%▇▇▇▇ 10%at its level
DecisionBench v1 · verification (all-request accuracy) submitted58.5% ✎▁▁▅▁▃▂▂▁▁▇▇█ beats 30/56medium · avg 56%▇▇▇ 6.7%blunt · at its level
DecisionBench v1 · document record classification (all-request accuracy) submitted54.7% ✎▂▂·▁▂▄▃▆▄█▁▁ beats 45/56medium · avg 46%▇▇ 5.6%at its level
DecisionBench v1 · rubric scoring prioritization (all-request accuracy) submitted60.0% ✎▁▁▅▅▃▃▅█▃▃·▁ beats 52/56medium · avg 45%▇▇ 4.7%at its level
DecisionBench v1 · triage (all-request accuracy) submitted85.0% ✎▁·▁·▂▃▄▃▂█▄▂ beats 37/56easy · avg 78%▇▇ 3.8%sharp
19 more tests
testresultvs the others in its rundifficultycountswhy
DecisionBench v1 · completion assessment (all-request accuracy) submitted90.0% ✎▁▁▂▃▄·▂▁▁▆█▂ beats 29/56very easy · avg 86%▇ 3.2%sharp · maxed out
DecisionBench v1 · review scoring (all-request accuracy) submitted63.0% ✎▁······▂▃▅▇█ beats 53/56medium · avg 52%▇ 3.0%at its level
DecisionBench v1 · content classification (all-request accuracy) submitted79.0% ✎▁····▁▁▁▁▁▁█ beats 34/56easy · avg 73%▇ 3.0%typical
DecisionBench v1 · retrieval ranking (all-request accuracy) submitted55.0% ✎▁▁·▁▁▂▃▂▃▁▁█ beats 37/56medium · avg 35%▇ 2.5%at its level · noisy
DecisionBench v1 · bounded extraction (all-request accuracy) submitted94.1% ✎▃▃▂·▁▁▁▁▁▁█▅ beats 42/56very easy · avg 86%▇ 2.5%sharp · maxed out · noisy
DecisionBench v1 · target selection (all-request accuracy) submitted45.0% ✎▁▁·▁▁▁▃▁▂▁▁█ beats 40/56hard · avg 29%▇ 2.5%at its level · noisy
DecisionBench v1 · risk scoring (all-request accuracy) submitted70.0% ✎▁▁·▁▂▂▁▃▃█▇▃ beats 48/56medium · avg 62%▇ 2.0%blunt · at its level
DecisionBench v1 · reasoning (all-request accuracy) submitted80.6% ✎▂·▁▁▆█▅▃▃▂▃▁ beats 54/56medium · avg 52%▇ 1.3%at its level
DecisionBench v1 · document workflows (all-request accuracy) submitted80.5% ✎█▄▃▁▁▃▃▂▁·▁▁ beats 48/56easy · avg 66%▇ 0.8%blunt · at its level
DecisionBench v1 · retrieval verification (all-request accuracy) submitted88.3% ✎█▁▂▁▁▁▁▁▂▅▁▁ beats 52/56easy · avg 74%▇ 0.7%noisy
DecisionBench v1 · guardrails moderation (all-request accuracy) submitted62.3% ✎▁▁▁▂▆▃▃█···▁ beats 42/56medium · avg 52%▇ 0.6%blunt · at its level · noisy
DecisionBench v1 · document classification (all-request accuracy) submitted39.0% ✎▂▁·▂▁▁▁▄▁▂▂█ beats 56/56hard · avg 21%▇ 0.4%blunt · at its level · noisy
DecisionBench v1 · safety gating (all-request accuracy) submitted95.0% ✎▂·····▆█▂▂▄▇ beats 40/56easy · avg 77%▇ 0.4%noisy
DecisionBench v1 · semantic filtering (all-request accuracy) submitted85.0% ✎▁·▁▁·▁▁█▂▃█▂ beats 46/56easy · avg 71%▇ 0.3%blunt · noisy
DecisionBench v1 · content moderation (all-request accuracy) submitted100.0% ✎▁····▃▅▃▂▂·█ beats 47/56easy · avg 82%▇ 0.2%noisy
DecisionBench v1 · context management (all-request accuracy) submitted87.0% ✎▁·····▁▇▆▁█▃ beats 35/56easy · avg 73%▇ 0.2%blunt · noisy
DecisionBench v1 · entity alignment (all-request accuracy) submitted99.9% ✎▁·▁▄▇▄▅▄▂▁▄█ beats 48/56easy · avg 66%▇ 0.2%at its level · noisy
DecisionBench v1 · function agent skill routing (all-request accuracy) submitted98.8% ✎▃▃▄▅▆▂▅▄▃▄█▃ beats 55/56easy · avg 68%▇ 0.1%blunt · at its level · noisy
DecisionBench v1 · routing triage (all-request accuracy) submitted90.7% ✎█▁···▁·▂▆▅▂▃ beats 54/56medium · avg 42%▇ 0.0%blunt · at its level · noisy

$ where to run it

Download model files ↗
Published model files verified. Includes adapters or readout weights; follow the model documentation for the required base model. 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/mohit67890 ↗

$ 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

sourceresultsshare of the scorelicence
Hanno Labs DecisionBench v1 reviewed results · submitted; independent execution unverified27CC0 1.0
prior · pull toward the middle
weighted average= 60.5

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