{"id":121621,"date":"2026-10-02T02:53:54","date_gmt":"2026-10-02T02:53:54","guid":{"rendered":"https:\/\/youzum.net\/cloudflare-releases-clef-and-clef-flash-open-weight-decision-models-that-return-typed-probabilities-instead-of-text\/"},"modified":"2026-10-02T02:53:54","modified_gmt":"2026-10-02T02:53:54","slug":"cloudflare-releases-clef-and-clef-flash-open-weight-decision-models-that-return-typed-probabilities-instead-of-text","status":"publish","type":"post","link":"https:\/\/youzum.net\/it\/cloudflare-releases-clef-and-clef-flash-open-weight-decision-models-that-return-typed-probabilities-instead-of-text\/","title":{"rendered":"Cloudflare Releases Clef and Clef-flash: Open-Weight Decision Models That Return Typed Probabilities Instead of Text"},"content":{"rendered":"<p class=\"wp-block-paragraph\">Cloudflare has released <a href=\"https:\/\/blog.cloudflare.com\/clef-decision-models\/\">Clef and Clef-flash<\/a>, the first models trained by its Workers AI team. They are decision models, not chatbots. Each reads an input state and a schema of typed questions. It returns a probability for every allowed answer, with no free-form text. Both are open-weight under Apache 2.0 and compatible with TypeSafe AI\u2019s Jev API.<\/p>\n<p class=\"wp-block-paragraph\"><strong>Is it deployable?<\/strong> Yes, Both models run today on <a href=\"https:\/\/developers.cloudflare.com\/workers-ai\/models\/clef\/\">Workers AI<\/a>, and the weights are on <a href=\"https:\/\/huggingface.co\/Cloudflare\/clef\">Hugging Face<\/a> for self-hosting.<\/p>\n<h2 class=\"wp-block-heading\"><strong>What a Decision Model Does<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">An LLM generates tokens one at a time, and its output still needs parsing. A decision model only answers a fixed set of questions about an input. <strong>Clef supports 3 question types:<\/strong><\/p>\n<ul class=\"wp-block-list\">\n<li><code>noul<\/code>: yes\/no, returns the probability of yes.<\/li>\n<li><code>choice<\/code>: picks 1 named option, with per-option probabilities and a confidence value.<\/li>\n<li><code>score<\/code>: rates against an ordered rubric, returning a probability-weighted score.<\/li>\n<\/ul>\n<p class=\"wp-block-paragraph\">On Workers AI, 1 request carries up to 64 questions and up to 4 images.<\/p>\n<p class=\"wp-block-paragraph\">TypeSafe AI launched <a href=\"https:\/\/typesafe.ai\/blog\/introducing-system-one-models-and-jev\">Jev<\/a>, its first \u2018System One\u2019 model, on September 15, 2026. Open alternatives like <a href=\"https:\/\/huggingface.co\/jaredpalmer\/kev-9b\">Kev-9B<\/a> and <a href=\"https:\/\/huggingface.co\/convaiinnovations\/laya\">Laya<\/a> followed. Clef uses the same System One API. Switching from Jev means changing the endpoint and model name.<\/p>\n<h2 class=\"wp-block-heading\"><strong>How Clef Works<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">Clef is post-trained from <a href=\"https:\/\/huggingface.co\/Qwen\/Qwen3.8-27B\">Qwen3.8-27B<\/a>, and <a href=\"https:\/\/huggingface.co\/Cloudflare\/clef-flash\">Clef-flash<\/a> from <a href=\"https:\/\/huggingface.co\/Qwen\/Qwen3.5-9B\">Qwen3.5-9B<\/a>. Both keep the backbone\u2019s vision encoder.<\/p>\n<p class=\"wp-block-paragraph\">Inference has 2 stages. The backbone first runs a single prefill-only pass over the state and questions. A small transformer, the joint schema head, then reads the final hidden states. It routes evidence to each question, lets fields cross-attend, and scores all options jointly. A per-question softmax turns logits into probabilities.<\/p>\n<p class=\"wp-block-paragraph\">Training froze both backbones and jointly optimized the routing head with rank-256 low-rank adapters. The loss pairs label-smoothed cross-entropy with a Brier loss for calibration. A secondary objective, Reinforcement Learning for Calibrated Decisions (RLCD), gives partial credit to adjacent ordinal choices.<\/p>\n<h2 class=\"wp-block-heading\"><strong>Interactive Explainer<\/strong><\/h2>\n<div>\n<\/div>\n<p class=\"wp-block-paragraph\">\n<h2 class=\"wp-block-heading\"><strong>Benchmarks: Where Clef Wins and Where It Does Not<\/strong><\/h2>\n<\/p><p class=\"wp-block-paragraph\">On Cloudflare\u2019s 10-benchmark shortlist from the <a href=\"https:\/\/clef-evals.workers-ai-mle.workers.dev\/\">Decision Index<\/a> 0.2.1 suite, a Clef model scored highest on 7.<\/p>\n<ul class=\"wp-block-list\">\n<li><strong>BANKING77 (macro-F1):<\/strong> Clef 94.20 vs Jev 79.74.<\/li>\n<li><strong>CLINC150+OOS (macro-F1):<\/strong> Clef 97.43 vs Jev 89.27.<\/li>\n<li><strong>Home appliances (case exact):<\/strong> Clef-flash 97.73 vs Jev 52.27.<\/li>\n<\/ul>\n<p class=\"wp-block-paragraph\">Jev keeps clear leads elsewhere. The <a href=\"https:\/\/huggingface.co\/Cloudflare\/clef\">full model card<\/a> shows Jev ahead on GPQA Diamond (78.3 vs 48.0). It also leads MMLU-Pro (82.7 vs 65.9) and BBH (92.9 vs 73.7).<\/p>\n<p class=\"wp-block-paragraph\">On TypeSafe\u2019s own <a href=\"https:\/\/evals.typesafe.ai\/\">workflow evals<\/a>, Clef beat Jev in 3 of 4 areas, by small margins. Invoice processing was 64.7 vs 61.8, customer service 76.3 vs 76.0, and security incidents 62.9 vs 61.7. Jev leads agent trace observability, 71.6 vs 68.5.<\/p>\n<p class=\"wp-block-paragraph\">In Cloudflare\u2019s threat intelligence workflow, Clef classified a domain in 2.2 seconds. gpt-oss-120b took 4.7 seconds.<\/p>\n<p class=\"wp-block-paragraph\"><em>All numbers are vendor-reported, with no independent replication yet.<\/em><\/p>\n<h2 class=\"wp-block-heading\"><strong>Feature Comparison<\/strong><\/h2>\n<figure class=\"wp-block-table\">\n<table class=\"has-fixed-layout\">\n<thead>\n<tr>\n<th>Feature<\/th>\n<th>Clef<\/th>\n<th>Clef-flash<\/th>\n<th>Jev<\/th>\n<th>Kev-9B<\/th>\n<th>Laya<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td><strong>Developer<\/strong><\/td>\n<td>Cloudflare<\/td>\n<td>Cloudflare<\/td>\n<td>TypeSafe AI<\/td>\n<td>Jared Palmer<\/td>\n<td>Convai Innovations<\/td>\n<\/tr>\n<tr>\n<td><strong>Size<\/strong><\/td>\n<td>27B<\/td>\n<td>9B<\/td>\n<td>Not disclosed<\/td>\n<td>9B + 45.4M LoRA<\/td>\n<td>421M<\/td>\n<\/tr>\n<tr>\n<td><strong>Backbone<\/strong><\/td>\n<td>Qwen3.8-27B<\/td>\n<td>Qwen3.5-9B<\/td>\n<td>Not disclosed<\/td>\n<td>Qwen3.5-9B-Base<\/td>\n<td>ModernBERT-large<\/td>\n<\/tr>\n<tr>\n<td><strong>Weights<\/strong><\/td>\n<td>Apache 2.0<\/td>\n<td>Apache 2.0<\/td>\n<td>Hosted API<\/td>\n<td>Apache 2.0<\/td>\n<td>Apache 2.0<\/td>\n<\/tr>\n<tr>\n<td><strong>Image input<\/strong><\/td>\n<td>Yes<\/td>\n<td>Yes<\/td>\n<td>No<\/td>\n<td>No<\/td>\n<td>No<\/td>\n<\/tr>\n<tr>\n<td><strong>Context<\/strong><\/td>\n<td>65,536<\/td>\n<td>65,536<\/td>\n<td>32K (per Cloudflare)<\/td>\n<td>65,536 (8,192 validated)<\/td>\n<td>512 (English)<\/td>\n<\/tr>\n<tr>\n<td><strong>Median latency*<\/strong><\/td>\n<td>209.3 ms<\/td>\n<td>38.8 ms<\/td>\n<td>524.1 ms<\/td>\n<td>51.4 ms<\/td>\n<td>5.8 ms<\/td>\n<\/tr>\n<tr>\n<td><strong>Hosted price (input)<\/strong><\/td>\n<td>$0.24\/M<\/td>\n<td>$0.09\/M<\/td>\n<td>$0.042\/M<\/td>\n<td>Self-host<\/td>\n<td>Self-host<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/figure>\n<p class=\"wp-block-paragraph\"><em>*Cloudflare\u2019s internal Decision Index run. All 5 implement the System One API. Sources: <a href=\"https:\/\/developers.cloudflare.com\/workers-ai\/models\/clef\/\">Clef docs<\/a>, <a href=\"https:\/\/developers.cloudflare.com\/workers-ai\/models\/clef-flash\/\">Clef-flash docs<\/a>, <a href=\"https:\/\/huggingface.co\/Cloudflare\/clef\">Clef card<\/a>, <a href=\"https:\/\/typesafe.ai\/blog\/introducing-system-one-models-and-jev\">Jev post<\/a>, <a href=\"https:\/\/huggingface.co\/jaredpalmer\/kev-9b\">Kev-9B card<\/a>, <a href=\"https:\/\/huggingface.co\/convaiinnovations\/laya\">Laya card<\/a>.<\/em><\/p>\n<h2 class=\"wp-block-heading\"><strong>Deployment and Fine-Tuning<\/strong><\/h2>\n<p class=\"wp-block-paragraph\">Both models are callable through the Workers AI binding (<code>env.AI.run()<\/code>), the REST API, or <a href=\"https:\/\/developers.cloudflare.com\/ai-gateway\/\">AI Gateway<\/a>. For self-hosting, the model cards list testing on a single H200 with BF16 weights.<\/p>\n<p class=\"wp-block-paragraph\">Cloudflare also announced a reinforcement learning service for tuning Clef on private data. It starts with Cloudflare\u2019s forward-deployed engineers, with a self-serve platform later. The pipeline combines AI Gateway, Workers AI, Containers and a new Trainer component. Teams can apply via the <a href=\"https:\/\/www.cloudflare.com\/resource\/clef-rl-interest\">design partner form<\/a>.<\/p>\n<h2 class=\"wp-block-heading\"><strong>Key Takeaways<\/strong><\/h2>\n<ul class=\"wp-block-list\">\n<li>Clef (27B) and Clef-flash (9B) are Apache 2.0, Jev-compatible decision models.<\/li>\n<li>Median latency: 209.3 ms for Clef, 38.8 ms for Clef-flash, 524.1 ms for Jev.<\/li>\n<li>Clef reads text, JSON, images and video within a 64K-token context window.<\/li>\n<li>Jev still leads on knowledge-heavy tests like GPQA Diamond, MMLU-Pro and BBH.<\/li>\n<li>An RL fine-tuning service starts with Cloudflare\u2019s forward-deployed engineers.<\/li>\n<\/ul>\n<p class=\"wp-block-paragraph\">\n<hr class=\"wp-block-separator has-alpha-channel-opacity\" \/>\n<\/p><p class=\"wp-block-paragraph\">\n<\/p><p class=\"wp-block-paragraph\">Check out the <strong><a href=\"https:\/\/huggingface.co\/Cloudflare\/clef\" target=\"_blank\" rel=\"noreferrer noopener\">Model weight<\/a>, <a href=\"https:\/\/clef-evals.workers-ai-mle.workers.dev\/\" target=\"_blank\" rel=\"noreferrer noopener\">Demo<\/a><\/strong> and <strong><a href=\"https:\/\/blog.cloudflare.com\/clef-decision-models\/\" target=\"_blank\" rel=\"noreferrer noopener\">Technical details<\/a><\/strong>. All credit goes to the researcher of this project. Also,\u00a0feel free to follow us on\u00a0<strong><a href=\"https:\/\/x.com\/intent\/follow?screen_name=marktechpost\" target=\"_blank\" rel=\"noopener\"><mark>Twitter<\/mark><\/a><\/strong>\u00a0and don\u2019t forget to join our\u00a0<strong><a href=\"https:\/\/www.reddit.com\/r\/machinelearningnews\/\" target=\"_blank\" rel=\"noopener\">150k+ML SubReddit<\/a><\/strong>\u00a0and Subscribe to\u00a0<strong><a href=\"https:\/\/magic.beehiiv.com\/v1\/f5e63dd4-5653-4f09-83e2-321a8b1ba526?email=%7B%7Bemail%7D%7D\" target=\"_blank\" rel=\"noopener\">our Newsletter<\/a><\/strong>. Wait! are you on telegram?\u00a0<strong><a href=\"https:\/\/t.me\/machinelearningresearchnews\" target=\"_blank\" rel=\"noopener\">now you can join us on telegram as well.<\/a><\/strong><\/p>\n<p class=\"wp-block-paragraph\">Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.?\u00a0<strong><a href=\"https:\/\/forms.gle\/MJjjVDPS7whH8Ngs6\" target=\"_blank\" rel=\"noopener\"><mark>Connect with us<\/mark><\/a><\/strong><\/p>\n<p>The post <a href=\"https:\/\/www.marktechpost.com\/2026\/10\/01\/cloudflare-releases-clef-and-clef-flash\/\">Cloudflare Releases Clef and Clef-flash: Open-Weight Decision Models That Return Typed Probabilities Instead of Text<\/a> appeared first on <a href=\"https:\/\/www.marktechpost.com\/\">MarkTechPost<\/a>.<\/p>","protected":false},"excerpt":{"rendered":"<p>Cloudflare has released Clef and Clef-flash, the first models trained by its Workers AI team. They are decision models, not chatbots. Each reads an input state and a schema of typed questions. It returns a probability for every allowed answer, with no free-form text. Both are open-weight under Apache 2.0 and compatible with TypeSafe AI\u2019s Jev API. Is it deployable? Yes, Both models run today on Workers AI, and the weights are on Hugging Face for self-hosting. What a Decision Model Does An LLM generates tokens one at a time, and its output still needs parsing. A decision model only answers a fixed set of questions about an input. Clef supports 3 question types: noul: yes\/no, returns the probability of yes. choice: picks 1 named option, with per-option probabilities and a confidence value. score: rates against an ordered rubric, returning a probability-weighted score. On Workers AI, 1 request carries up to 64 questions and up to 4 images. TypeSafe AI launched Jev, its first \u2018System One\u2019 model, on September 15, 2026. Open alternatives like Kev-9B and Laya followed. Clef uses the same System One API. Switching from Jev means changing the endpoint and model name. How Clef Works Clef is post-trained from Qwen3.8-27B, and Clef-flash from Qwen3.5-9B. Both keep the backbone\u2019s vision encoder. Inference has 2 stages. The backbone first runs a single prefill-only pass over the state and questions. A small transformer, the joint schema head, then reads the final hidden states. It routes evidence to each question, lets fields cross-attend, and scores all options jointly. A per-question softmax turns logits into probabilities. Training froze both backbones and jointly optimized the routing head with rank-256 low-rank adapters. The loss pairs label-smoothed cross-entropy with a Brier loss for calibration. A secondary objective, Reinforcement Learning for Calibrated Decisions (RLCD), gives partial credit to adjacent ordinal choices. Interactive Explainer Benchmarks: Where Clef Wins and Where It Does Not On Cloudflare\u2019s 10-benchmark shortlist from the Decision Index 0.2.1 suite, a Clef model scored highest on 7. BANKING77 (macro-F1): Clef 94.20 vs Jev 79.74. CLINC150+OOS (macro-F1): Clef 97.43 vs Jev 89.27. Home appliances (case exact): Clef-flash 97.73 vs Jev 52.27. Jev keeps clear leads elsewhere. The full model card shows Jev ahead on GPQA Diamond (78.3 vs 48.0). It also leads MMLU-Pro (82.7 vs 65.9) and BBH (92.9 vs 73.7). On TypeSafe\u2019s own workflow evals, Clef beat Jev in 3 of 4 areas, by small margins. Invoice processing was 64.7 vs 61.8, customer service 76.3 vs 76.0, and security incidents 62.9 vs 61.7. Jev leads agent trace observability, 71.6 vs 68.5. In Cloudflare\u2019s threat intelligence workflow, Clef classified a domain in 2.2 seconds. gpt-oss-120b took 4.7 seconds. All numbers are vendor-reported, with no independent replication yet. Feature Comparison Feature Clef Clef-flash Jev Kev-9B Laya Developer Cloudflare Cloudflare TypeSafe AI Jared Palmer Convai Innovations Size 27B 9B Not disclosed 9B + 45.4M LoRA 421M Backbone Qwen3.8-27B Qwen3.5-9B Not disclosed Qwen3.5-9B-Base ModernBERT-large Weights Apache 2.0 Apache 2.0 Hosted API Apache 2.0 Apache 2.0 Image input Yes Yes No No No Context 65,536 65,536 32K (per Cloudflare) 65,536 (8,192 validated) 512 (English) Median latency* 209.3 ms 38.8 ms 524.1 ms 51.4 ms 5.8 ms Hosted price (input) $0.24\/M $0.09\/M $0.042\/M Self-host Self-host *Cloudflare\u2019s internal Decision Index run. All 5 implement the System One API. Sources: Clef docs, Clef-flash docs, Clef card, Jev post, Kev-9B card, Laya card. Deployment and Fine-Tuning Both models are callable through the Workers AI binding (env.AI.run()), the REST API, or AI Gateway. For self-hosting, the model cards list testing on a single H200 with BF16 weights. Cloudflare also announced a reinforcement learning service for tuning Clef on private data. It starts with Cloudflare\u2019s forward-deployed engineers, with a self-serve platform later. The pipeline combines AI Gateway, Workers AI, Containers and a new Trainer component. Teams can apply via the design partner form. Key Takeaways Clef (27B) and Clef-flash (9B) are Apache 2.0, Jev-compatible decision models. Median latency: 209.3 ms for Clef, 38.8 ms for Clef-flash, 524.1 ms for Jev. Clef reads text, JSON, images and video within a 64K-token context window. Jev still leads on knowledge-heavy tests like GPQA Diamond, MMLU-Pro and BBH. An RL fine-tuning service starts with Cloudflare\u2019s forward-deployed engineers. Check out the Model weight, Demo and Technical details. All credit goes to the researcher of this project. Also,\u00a0feel free to follow us on\u00a0Twitter\u00a0and don\u2019t forget to join our\u00a0150k+ML SubReddit\u00a0and Subscribe to\u00a0our Newsletter. Wait! are you on telegram?\u00a0now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.?\u00a0Connect with us The post Cloudflare Releases Clef and Clef-flash: Open-Weight Decision Models That Return Typed Probabilities Instead of Text appeared first on MarkTechPost.<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_pvb_checkbox_block_on_post":false,"footnotes":""},"categories":[52,5,7,1],"tags":[],"class_list":["post-121621","post","type-post","status-publish","format-standard","hentry","category-ai-club","category-committee","category-news","category-uncategorized","pmpro-has-access"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.3 - 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