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Introducing Clairic, a new System One class of extraction models
Cloudflare's launch post for Clairic, a preview extraction model on Workers AI that returns typed values such as spans, numbers, dates, estimates and forecasts with probabilities. It sets out extraction as a second System One class beside decision models and gives Cloudflare's own forecast and claims-pipeline results.
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Introducing Clef: our open-source decision models, and new RL fine-tuning platform
Cloudflare's launch post for Clef and Clef-flash, two decision models on Workers AI that accept the Jev request format and open weights under Apache 2.0. It covers the architecture, Cloudflare's own benchmark tables against Jev, Kev and Laya, and a new reinforcement learning service.
Michelle Chen (@michellechen)140k viewsOpenIntroducing Clef: our open-source decision models, and new RL fine-tuning platform on blog.cloudflare.comArticleSupport inbox triage
Decision Model Leaderboard
Cloudflare's live leaderboard for decision models on the Jev Decision Index suite, with Clef, Clef-flash, Jev, Kev-9B and Laya plotted by score and latency. Cloudflare built and ran it, so treat the ranking as vendor-run.
Cloudflare (@ritakozlov)41k viewsOpenDecision Model Leaderboard on clef-evals.workers-ai-mle.workers.devArticle
[AINews] Jev: a System One Model that only decides/classifies/routes/scores
AI News daily roundup leading with the Jev launch, summarising RLCD and TypeSafe's 20-200x speed and 40-400x cost claims. Its recap of reactions notes posters using Jev as a structured classifier, judge and routing policy where text generation is unnecessary.
Jev means structured output is interesting again
Argues Jev's speed comes mainly from constrained single-token generation and aggressive prefill, techniques reproducible with existing LLMs, rather than a new architecture. Still treats very fast structured output as a new primitive, while doubting Jev matches frontier models on raw intelligence.
ArticleZaious/jev-capability-atlas
An independent, evidence-based map of where Jev's calibrated-decision claim holds up and where it breaks down, built from real API-call receipts rather than a leaderboard.
Articlewillkelly/jev-evaluation
An adversarial evaluation of Jev with nine experiments and 28 predictions fixed before any data was collected. One run covered 123,805 requests over 138 minutes for $12.69, with five failures, plus a 13-rule prompting guide.
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