Benchmarks
More
OverviewBenchmaxxingCompareChartsCost vs CapabilityProviders per ModelProvider explorerGatewaysEU & SovereignJev-class modelsImage JevBenchAbout
JevBench by Benchmark Heaven · v1.4.2.2 · individual system

decision-machine-1 (milliseconds.ai)

Closed decision API · by milliseconds.ai (Baptiste Laget) · API endpoint saw sealed item text

JevBench v1.4.2.2 score

39.9

Rank #25 of 91 ranked systems.

23.4 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
41.3
calibration
68.3
speed
92.9
cost
53.7

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.

The four score axes

Radar: the four score axes, two systemsThe four score axes, decision-machine-1 vs Jev 1.13.0. Intelligence: 41.3 vs 53.1; Calibration: 68.3 vs 76.3; Speed: 92.9 vs 83.3; Cost: 53.7 vs 52.0.50100Intelligence41.3 · 53.1Calibration68.3 · 76.3Speed92.9 · 83.3Cost53.7 · 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, decision-machine-1 vs Jev 1.13.0. Easy: 100% vs 100%; Standard: 76% vs 99%; Judge: 90% vs 95%; Hard: 47% vs 74%; Sealed: 26% vs 37%.50100Easy100% · 100%Standard76% · 99%Judge90% · 95%Hard47% · 74%Sealed26% · 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), decision-machine-1 vs Jev 1.13.0. Ambiguous / abstain: 24% vs 43%; Judge: 51% vs 54%; Long policy: 24% vs 44%; Multi-hop: 32% vs 64%; Probability: 33% vs 63%; Temporal / numeric: 20% vs 28%; Trade-off: 32% vs 55%; Routing: 100% vs 100%; Trap / adversarial: 63% vs 83%; Paraphrase: 50% vs 64%; Safety judge: 19% vs 38%.50100Ambiguous /abstain24% · 43%Judge51% · 54%Long policy24% · 44%Multi-hop32% · 64%Probability33% · 63%Temporal /numeric20% · 28%Trade-off32% · 55%Routing100% · 100%Trap /adversarial63% · 83%Paraphrase50% · 64%Safety judge19% · 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, decision-machine-1 vs Jev 1.13.0. Ambiguous / abstain: 24% vs 30%; Judge: 41% vs 34%; Long policy: 15% vs 28%; Multi-hop: 18% vs 45%; Paraphrase: 50% vs 64%; Probability: 25% vs 50%; Safety judge: 19% vs 38%; Temporal / numeric: 18% vs 29%; Trade-off: 31% vs 38%; Trap / adversarial: 42% vs 42%.50100Ambiguous /abstain24% · 30%Judge41% · 34%Long policy15% · 28%Multi-hop18% · 45%Paraphrase50% · 64%Probability25% · 50%Safety judge19% · 38%Temporal /numeric18% · 29%Trade-off31% · 38%Trap /adversarial42% · 42%
Share correct within each sealed family — system-level aggregates; the items stay private.

Availability and evidence

Openness
Marked closed in the published row
License note
proprietary API, closed weights
Cost evidence
measured; the board’s row disclosure contains the published basis.
Endpoint condition
production API (milliseconds.ai, served from its nearest region), measured from Germany
Note on this row
A closed-weights decision model behind a production API that serves TypeSafe's wire format, so the unchanged typesafe adapter ran it. Run on a free test key (30 requests a minute, 2.2 s between requests); the provider states the inference infrastructure is the same as for paid keys. Cost is the public paid tariff, $0.04 per million input tokens (output free), times the input tokens the API reported.
Published source
https://www.milliseconds.ai

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.