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JevBench by Benchmark Heaven · v1.4.2.2 · individual system

GLiNER2 large (Fastino)

Zero-shot classifier · by Fastino

JevBench v1.4.2.2 score

15.1

Rank #69 of 91 ranked systems.

48.2 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
31.1
calibration
24.8
speed
61.7
cost
73.3

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: GLiNER2 large — Zero-shot classifier · Score 15.1 (#69)
  • 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, GLiNER2 large vs Jev 1.13.0. Intelligence: 31.1 vs 53.1; Calibration: 24.8 vs 76.3; Speed: 61.7 vs 83.3; Cost: 73.3 vs 52.0.50100Intelligence31.1 · 53.1Calibration24.8 · 76.3Speed61.7 · 83.3Cost73.3 · 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, GLiNER2 large vs Jev 1.13.0. Easy: 99% vs 100%; Standard: 63% vs 99%; Judge: 61% vs 95%; Hard: 36% vs 74%; Sealed: 29% vs 37%.50100Easy99% · 100%Standard63% · 99%Judge61% · 95%Hard36% · 74%Sealed29% · 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), GLiNER2 large vs Jev 1.13.0. Ambiguous / abstain: 41% vs 43%; Judge: 45% vs 54%; Long policy: 21% vs 44%; Multi-hop: 25% vs 64%; Probability: 21% vs 63%; Temporal / numeric: 22% vs 28%; Trade-off: 26% vs 55%; Routing: 70% vs 100%; Trap / adversarial: 57% vs 83%; Paraphrase: 29% vs 64%; Safety judge: 44% vs 38%.50100Ambiguous /abstain41% · 43%Judge45% · 54%Long policy21% · 44%Multi-hop25% · 64%Probability21% · 63%Temporal /numeric22% · 28%Trade-off26% · 55%Routing70% · 100%Trap /adversarial57% · 83%Paraphrase29% · 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, GLiNER2 large vs Jev 1.13.0. Ambiguous / abstain: 46% vs 30%; Judge: 37% vs 34%; Long policy: 15% vs 28%; Multi-hop: 24% vs 45%; Paraphrase: 29% vs 64%; Probability: 18% vs 50%; Safety judge: 44% vs 38%; Temporal / numeric: 21% vs 29%; Trade-off: 31% vs 38%; Trap / adversarial: 42% vs 42%.50100Ambiguous /abstain46% · 30%Judge37% · 34%Long policy15% · 28%Multi-hop24% · 45%Paraphrase29% · 64%Probability18% · 50%Safety judge44% · 38%Temporal /numeric21% · 29%Trade-off31% · 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
Cost evidence
estimated; the board’s row disclosure contains the published basis.
Endpoint condition
our CPU (4 threads, Ryzen 5 3600)
Note on this row
The large checkpoint of Fastino's earlier GLiNER2 family, same documented mapping as the GLiNER2 row: the question goes in front of the text and the probabilities are the model's own single-label softmax over the labels, read out in full. A general schema classifier, not a Jev rebuild.
Published source
https://huggingface.co/fastino/gliner2-large-v1

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.