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JevBench by Benchmark Heaven · released v1.4.1

Jev vs reflex 4B: published benchmark comparison

A side-by-side view of Jev 1.13.0 and reflex 4B (kshetrajna12) from the same hash-checked v1.4.1 aggregate. The overall score is a composite; compare the separate measures against your use case.

Four-radar comparison: Jev and reflex 4B

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 1.13.0 — Jev (TypeSafe, closed) · Score 63.3 (#1)
  • B: reflex 4B — Jev rebuild · Score 54.0 (#5)

The four score axes

Radar: the four score axes, two systemsThe four score axes, Jev 1.13.0 vs reflex 4B. Intelligence: 53.1 vs 47.5; Calibration: 76.3 vs 70.4; Speed: 83.3 vs 68.0; Cost: 52.0 vs 59.7.50100Intelligence53.1 · 47.5Calibration76.3 · 70.4Speed83.3 · 68.0Cost52.0 · 59.7
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 1.13.0 vs reflex 4B. Easy: 100% vs 100%; Standard: 99% vs 95%; Judge: 95% vs 97%; Hard: 74% vs 63%; Sealed: 37% vs 28%.50100Easy100% · 100%Standard99% · 95%Judge95% · 97%Hard74% · 63%Sealed37% · 28%
Share correct per tier; Sealed = the 308 private decisions, aggregate only.

Hard tier by family (v1.2 topics)

Radar: hard tier by family (v1.2 topics), two systemsHard tier by family (v1.2 topics), Jev 1.13.0 vs reflex 4B. Adversarial: 100% vs 83%; Ambiguous: 79% vs 50%; Judge: 79% vs 73%; Long policy: 61% vs 53%; Multi-hop: 86% vs 63%; Probability: 80% vs 55%; Routing: 100% vs 100%; Temporal / numeric: 27% vs 33%; Trade-off: 92% vs 75%; Trap: 100% vs 100%.50100Adversarial100% · 83%Ambiguous79% · 50%Judge79% · 73%Long policy61% · 53%Multi-hop86% · 63%Probability80% · 55%Routing100% · 100%Temporal /numeric27% · 33%Trade-off92% · 75%Trap100% · 100%
Share correct within each family of the 220 v1.2 hard-tier decisions (public and held-out).

Sealed set by family

Radar: sealed set by family, two systemsSealed set by family, Jev 1.13.0 vs reflex 4B. Ambiguous / abstain: 30% vs 24%; Judge: 34% vs 34%; Long policy: 28% vs 20%; Multi-hop: 45% vs 34%; Paraphrase: 64% vs 21%; Probability: 50% vs 29%; Safety judge: 38% vs 44%; Temporal / numeric: 29% vs 23%; Trade-off: 38% vs 27%; Trap / adversarial: 42% vs 42%.50100Ambiguous /abstain30% · 24%Judge34% · 34%Long policy28% · 20%Multi-hop45% · 34%Paraphrase64% · 21%Probability50% · 29%Safety judge38% · 44%Temporal /numeric29% · 23%Trade-off38% · 27%Trap /adversarial42% · 42%
Share correct within each sealed family — system-level aggregates; the items stay private.

Published values

Jev 1.13.0 (TypeSafe AI) has the higher published JevBench Score. The displayed score uses one decimal place; the comparison above uses the same published release.

All values as a table
MeasureJev 1.13.0 (TypeSafe AI)reflex 4B (kshetrajna12)
Published rank#1#5
JevBench Score63.354.0
Sealed-set accuracy36.7%28.2%
Intelligence axis53.147.5
Calibration axis76.370.4
Speed axis83.368.0
Cost axis52.059.7
Cost evidencemeasuredestimated
Cost per 1,000 decisions$0.040 per 1,000 decisions$0.022 per 1,000 decisions

Intelligence, Calibration, Speed and Cost — equal-weight harmonic mean, with generalization and Jev-class gates. Cost evidence is labeled per row. Speed is a benchmark axis; deployment latency depends on the endpoint and conditions.

Compare more JevBench pairs: Jev vs JevK5 · Jev vs Hopper · Jev vs Winnow-12B Q8

See Jev alternatives · Choose by use case

Frequently asked questions

What does JevBench show for Jev and reflex 4B?
In v1.4.1, Jev is rank 1 with a JevBench Score of 63.3; reflex 4B (kshetrajna12) is rank 5 with a score of 54.0. The table shows their published axes and sealed-set accuracy separately.
Which system has higher sealed-set accuracy?
Jev 1.13.0 (TypeSafe AI) has the higher published sealed-set accuracy (36.7% versus 28.2%). This is separate from the composite JevBench Score.
Can I compare the cost values as actual bills?
Jev 1.13.0 (TypeSafe AI): measured at $0.040 per 1,000 decisions. reflex 4B (kshetrajna12): estimated at $0.022 per 1,000 decisions. Estimated and announced bases are not measured charges; inspect the board’s full row disclosure.