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JevBench by Benchmark Heaven · v1.4.2.2 · individual system

Jev-Omni (akhilaaa3, Gemma-4-12B merged)

Jev rebuild · by akhilaaa3

JevBench v1.4.2.2 score

51.3

Rank #11 of 91 ranked systems.

12.0 points behind Jev 1.13.0's 63.3.

Where it sits among the 91 ranked systems. The marked tick is Jev 1.13.0 (63.3).

Published axes

intelligence
46.8
calibration
64.1
speed
81.5
cost
53.0

Against Jev 1.13.0

Four radars compare this fixed pair across the score axes, accuracy per tier, and accuracy by family on the hard tier and sealed set. Further out is better on every spoke.

  • A: Jev-Omni — Jev rebuild · Score 51.3 (#11)
  • B: Jev 1.13.0 — Jev (TypeSafe, closed) · Score 63.3 (#4)

The four score axes

Radar: the four score axes, two systemsThe four score axes, Jev-Omni vs Jev 1.13.0. Intelligence: 46.8 vs 53.1; Calibration: 64.1 vs 76.3; Speed: 81.5 vs 83.3; Cost: 53.0 vs 52.0.50100Intelligence46.8 · 53.1Calibration64.1 · 76.3Speed81.5 · 83.3Cost53.0 · 52.0
0–100, the values in the table. A label-only system has no calibration (counted as 0).

Accuracy per tier, incl. sealed

Radar: accuracy per tier, incl. sealed, two systemsAccuracy per tier, incl. sealed, Jev-Omni vs Jev 1.13.0. Easy: 100% vs 100%; Standard: 97% vs 99%; Judge: 92% vs 95%; Hard: 75% vs 74%; Sealed: 32% vs 37%.50100Easy100% · 100%Standard97% · 99%Judge92% · 95%Hard75% · 74%Sealed32% · 37%
Share correct per tier; Sealed = the 308 private decisions, aggregate only.

Current question set by family (hard + sealed)

Radar: current question set by family (hard + sealed), two systemsCurrent question set by family (hard + sealed), Jev-Omni vs Jev 1.13.0. Ambiguous / abstain: 51% vs 43%; Judge: 64% vs 54%; Long policy: 37% vs 44%; Multi-hop: 56% vs 64%; Probability: 48% vs 63%; Temporal / numeric: 26% vs 28%; Trade-off: 47% vs 55%; Routing: 100% vs 100%; Trap / adversarial: 83% vs 83%; Paraphrase: 57% vs 64%; Safety judge: 44% vs 38%.50100Ambiguous /abstain51% · 43%Judge64% · 54%Long policy37% · 44%Multi-hop56% · 64%Probability48% · 63%Temporal /numeric26% · 28%Trade-off47% · 55%Routing100% · 100%Trap /adversarial83% · 83%Paraphrase57% · 64%Safety judge44% · 38%
Share correct per family across the 220 hard-tier decisions (public and held out) and the 308 sealed decisions of v1.4, pooled; Routing is hard-tier only, Paraphrase and Safety judge sealed only.

Sealed set by family

Radar: sealed set by family, two systemsSealed set by family, Jev-Omni vs Jev 1.13.0. Ambiguous / abstain: 35% vs 30%; Judge: 44% vs 34%; Long policy: 18% vs 28%; Multi-hop: 32% vs 45%; Paraphrase: 57% vs 64%; Probability: 36% vs 50%; Safety judge: 44% vs 38%; Temporal / numeric: 21% vs 29%; Trade-off: 27% vs 38%; Trap / adversarial: 42% vs 42%.50100Ambiguous /abstain35% · 30%Judge44% · 34%Long policy18% · 28%Multi-hop32% · 45%Paraphrase57% · 64%Probability36% · 50%Safety judge44% · 38%Temporal /numeric21% · 29%Trade-off27% · 38%Trap /adversarial42% · 42%
Share correct within each sealed family — system-level aggregates; the items stay private.

Availability and evidence

Openness
Code and weights marked open in the published row
License note
Apache-2.0, following Gemma 4; dataset rights stated separately by the author
Cost evidence
estimated; the board’s row disclosure contains the published basis.
Endpoint condition
our RunPod GPU (L40 48 GB, Czechia), reached over the internet from Germany
Note on this row
Akhilaaa3/Jev-Omni revision c050d51354147985d13286cf4acf90f562f2c631, the author's own load_model.py (merged text decision model + 256-way head) and his own predict(), transformers 5.17.0 / torch 2.8.0 from the pod image; built on the CPU and moved to CUDA with every nn.Linear weight cast to bfloat16 first - the same cast his reference loader jev_omni.py applies - because our 46 GB GPU cannot hold his fp32 copy; on our RunPod L40 in Czechia Offline local open-weight inference on the frozen 308-item v1.4 set in a network-disabled, read-only container; no operator endpoint received sealed text.
Published source
https://huggingface.co/akhilaaa3/Jev-Omni

From the public v1.4.2.2 aggregate. Scores and ranks can change when a new release is published.

Read the full board and published method. The overall score is a composite, not raw accuracy.