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The JEV feature missing from most LLM speed-vs-accuracy comparisons

Most comparisons between decision systems focus on two questions: How accurate is it? How fast is it? Both matter. But while benchmarking JEV against LLMs, I found a third property that is easy to miss: Can you trust the structured answer as a decision object? Valid JSON is not necessarily a valid…

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  1. 2026-09-27 05:37 · r/AI_Agents
    The JEV feature missing from most LLM speed-vs-accuracy comparisons

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  1. Mica v0.1 4B: open Jev-style decision model (yes/no, choice, score) that runs on an 8 GB GPU — trained for under $30 of GPU time — r/LocalLLM
  2. Fastino Releases GLiNER2.5-Decide: A 340M Open-Weight Decision Model That Runs on CPU — MarkTechPost
  3. Turn GLM-5.3-Flash into a Jev-like System One model — Lobsters AI
  4. Jev vs. Kev: open-source Jev alternative tested side by side — r/LocalLLaMA
  5. How JEV works internally — r/learnmachinelearning
  6. New frontier AI models, TypeSafe’s Jev AI, & NASA’s IBM collab — Mixture of Experts (IBM)
  7. The cheap new AI model taking aim at OpenAI and Anthropic — Financial Times AI
  8. I built an open-weight alternative to Jev / TypeSafe - introducing OpenJudgement-4B (early preview) — r/LocalLLaMA

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