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FrontiersMind published Lumma-Fev-v2 on October 29th, a family of "" that take a state and a typed question and return a probability for every option in one pass, with no text to parse.
The 27-billion-parameter version is built on Alibaba's Qwen3.8-27B under , with a 4B on Qwen3.5-4B.
On eight public the 27B averages 87.3% against 81.2% for the TypeSafe Jev model it compares with, while trailing it on BBH and JudgeBench, and posts the best of the models compared. Every number is the developer's own, and the Hindi, Tamil and Kannada results come from an internal set translated with Claude.
A model that returns a calibrated number in place of an essay is what an 's yes-or-no gates need.
FrontiersMind releases models that answer with probabilities instead of text
FrontiersMind published Lumma-Fev-v2 on October 29th, a family of "decision models" that take a state and a typed question and return a probability for every option in one pass, with no text to parse. The 27-billion-parameter version is built on Alibaba's Qwen3.8-27B under Apache 2.0, with a 4B on Qwen3.5-4B. On eight public benchmarks the 27B averages 87.3% against 81.2% for the TypeSafe Jev model it compares with, while trailing it on BBH and JudgeBench, and posts the best calibration of the models compared.
Every number is the developer's own, and the Hindi, Tamil and Kannada results come from an internal set translated with Claude. A model that returns a calibrated number in place of an essay is what an agent's yes-or-no gates need. SourcesA
Sources
- ALumma-Fev-v2 (FrontiersMind, Hugging Face)huggingface.co
This story ran in the Morning Brief of November 1st 2026.