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Open-weight decision model from Nace.AI on Qwen3.6-35B-A3B (MoE, about 3B active per token) with a Kev pointer head that answers the same POST /v1/systemone Choice, Noul, and Score shape with states up to 131,072 tokens, self-hosted through Nace's llama.cpp or Ollama forks or through Nace's hosted API; the weights use the custom Nace.AI Open RAIL-M license, and its published scores are self-reported.
Apache-2.0 open-weight Qwen3-4B fine-tune for sales call transcripts that answers one yes/no, Choice, or 0-10 Score question as a single option token with a probability per option, and also writes JSON insights with verbatim quotes; runs self-hosted (vLLM or GGUF), so no call data leaves your machine; a separate Jev-style alternative, not hosted Jev, trained on English B2B software calls and weaker on companies unlike its training data.
Independent calibration measurement of Jev on two labelled datasets, Banking77 and Web of Science, with a Jev to frontier cascade priced per row from measured tokens, now also packaged as an installable tool (pip install janus-decide) that measures a threshold on your own data and ships none by default; the protocol was frozen before any result and the raw JSONL and figures are committed, and no routing parameter transferred between the two datasets, as the optimal threshold, the sign of the accuracy gap between the two models, and whether routing paid for itself all changed; the Web of Science labels come from publication metadata rather than per-document annotation, so part of the error measured there is label ambiguity.
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