AutoTrust's student of TypeSafe Jev 1.13. A frozen Qwen3.8-27B plus a 108.9M-parameter decision block trained on Jev's own output distributions; one set of weights answers typed questions in one pass (System 1) or generates text with the untouched base (System 2).
Invergent's decision model for text and images. A fine-tune of Gemma-4-26B-A4B (about 4B parameters active per token) that answers Choice, Noul and Score questions, with an optional thinking mode for harder questions.
Decides
choice, score, noul
choice, score, noul, classify, route
Architecture
blocks-of-experts
rune
Fine-tuned from
qwen/qwen3.8-27b
google/gemma-4-26b-a4b-it
License
apache-2.0
apache-2.0
Availability
Open weights
Open weights + hosted API
Hosted by
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Invergent
Input price
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Decision accuracy
88.7%
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Calibration error
—
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Valid action rate
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Median latency
137 ms
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p95 latency
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Figures are from each model’s manifest; accuracy and latency are what the publishers report, on their own suites and hardware. Add a third model.
Questions
What is the difference between jev-27b and rune?
jev-27b is from AutoTrust AI Lab and rune from Surogate (Invergent). jev-27b has open weights you can download and run; rune has open weights and a hosted API. Both answer choice, score and noul questions. Only rune answers classify and route. rune is the smaller model, at 26B parameters to 27B.
Which is more accurate, jev-27b or rune?
Only jev-27b publishes an accuracy figure (88.7% on JevBench public set (231 items), family-macro score, maker's run); rune does not, so there is no comparison to make without your own test.
Which is cheaper, jev-27b or rune?
jev-27b: Free (open weights). rune: Hosted, price not published, or free to self-host. Open weights cost nothing per call beyond your own hardware.
Can I run jev-27b or rune locally?
Yes, both: systemone pull autotrust-ai/jev-27b and systemone pull surogate/rune download the weights.
Evaluation suite
JevBench public set (231 items), family-macro score, maker's run
