Cloudflare拥有发布 Clef-omni，是其Clef决定模型家族中一个新的开放量级成员。 Clef-omni是Cloudflare决定模型，在1个呼叫中评分文本，图像，音频和视频。 它于2026年10月9日抵达，距离最初的克莱夫和克莱夫-flash 发布相隔8天。 同一更新将克莱夫-闪电定价削减到TypeSafe的Jev以下，并使托管克莱夫的速度约2x. 对于开发者来说，这可以删除多式agent管道通常需要的转录和字幕步骤。
TL；数据
- 大小：共计30B,3B活性参数(MOE);64 000个工人AI上的背景
- 运行：工人AI 作为@cf/cloudflare/clef-omni；自我托管测试在一个单H200上，大约64GB GPU内存在bfloat16
- 性能：在路线和意向任务方面接近克莱夫，在大多数推理和工作流程评估方面紧随其后
- 最佳：97.7 宏F1在CLINC150+OOS上，4 Clef和Jev型号的顶部
- 最差：POP909-CL的精确度为2.6;GPQA钻石对Jev的精确度为47.4;
- 底线：
- 最佳：一个电话决定四种方式。
- 最糟糕的是，精度比以文字为主的Clef更弱。
什么是决定模式？
一个决定模型以概率而不是生成文本返回输入的答案。 你派了一个州和一个计划的问题。 模型返回每个允许选项的概率。 Clef支持3个问题类型：nul(真假)，选择和得分。 该格式沿用了来自TypeSafe AI的Jev和System One API.
克莱夫 -奥姆尼是怎么工作的？
Clef-omni从Qwen3-Omni-30B-A3B-Instruct接受后训练。 Cloudflare保持理解主干线并放下文本转语音输出路径。 该型号运行一个单一的预装通过满载荷。 视频帧和音频同步成1个序列进行联合处理。
一个小联合计划头然后评分选择。 每个有效选项首先从输入中收集证据。 字段向量然后跨整个上下文交叉计算信心分数。 Cloudflare冻结了Qwen3主干线，并训练了LoRA适配器。 训练与布赖尔分数校准相结合的标签-流线交叉。
支持对Workers AI的投入，据开发商Docs:
- 图像：最多4个，PNG、JPEG或WebP，每个64至1 024个令牌
- 音频：最多4个剪辑，WAV或MP3，每片300秒，每分钟约780个信使
- 视频：最多2个片段，MP4或WebM，每片60秒，每秒2帧取样
- 问题：每个请求1至64个
Cloudflare以中位数约130ms的速度报告文本决定，以约150ms的速度报告图像。 21秒的录影带，在1.5秒左右有声分。
Clef -omni在基准测试上表现如何？.
Cloudflare将克莱夫-omni与克莱夫，克莱夫-flash和Jev在其决定指数运行上进行比较。 Clef-omni领先意图分类：94.8 macro-F1在Banking77上，97.7在CLINC150+OOS上。 它也是超过 Amazon ESCI在57.8.
它跟随别的地方。 家用电器个案精确度为69.3，克莱夫-弗莱什为97.73. 在TypeSafe的工作流程评估中，克莱夫-奥姆尼在所有5个报告指标中得分都低于克莱夫。
Clef-omni vs Clef vs Clef-flash vs Jev (英语).
基准测试分数来自Cloudflare的公告，未经独立核实。
克莱夫有多快？
Cloudflare移动了托管的克莱夫到SGLang，并调制了其服务堆栈。 发布没有新的重量。 中度耐性从262ms下降到152ms，以约800个令牌，1.7x增益。 约3400个令牌从616毫秒下降到305毫秒，收获2.0x. 大约16,000个信物从2,721毫秒下降到1,635毫秒。 克莱夫在SGLang 0.5.22中支持获得。
关键外卖
- Clef -omni在1个决定电话中给文本，图像，音频和视频打分。
- 这是Qwen3 - Omni,Apache - 2.0上的30B - A3B MOE，每百万令牌0.15美元。
- Clef-flash现在费用为0.038美元，在Jev的0.042美元下，有24k个主机。
- 主机克莱夫在移动到SGLang后在中位数上速度为1.7x至2.0x.
- Clef -omni赢得意向任务但跟踪Clef的推理和工作流程评估。
请检查access-date=中的日期值 (帮助) Cloudflare公告，型号重量和工人AI Docs. 所有功劳都归功于这个项目的研究人员。 并加入我们的通讯。 等等你在电报里吗现在你也可以加入我们的电报。
Asif Razzaq是Marktechpost AI Media Inc.的首席执行官。 作为一个有远见的企业家和工程师，阿西夫致力于利用人工智能的潜力促进社会福祉。 他最近的努力是人工智能媒体平台的发布，该平台以深入报道机器学习和深层学习新闻而著称，这些新闻在技术上既可靠又为广大观众所容易理解。 该平台每月有超过200万次观看，表明其受观众欢迎。
本文由 AI图鉴自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
Cloudflare has released Clef-omni, a new open-weight member of its Clef decision model family. Clef-omni is a Cloudflare decision model that scores text, images, audio and video in 1 call. It arrived on October 9, 2026, 8 days after the original Clef and Clef-flash launch. The same update cut Clef-flash pricing below TypeSafe’s Jev and made hosted Clef roughly 2x faster. For developers, this removes the transcription and captioning steps that multimodal agent pipelines usually need.
TL;DR
- Size: 30B total, 3B active parameters (MoE); 64,000-token context on Workers AI
- Runs on: Workers AI as @cf/cloudflare/clef-omni; self-hosting tested on a single H200, about 64 GB GPU memory in bfloat16
- Performance: Close to Clef on routing and intent tasks, behind it on most reasoning and workflow evals
- Best: 97.7 macro-F1 on CLINC150+OOS, top of the 4 Clef and Jev models
- Worst: 2.6 accuracy on POP909-CL, and 47.4 on GPQA Diamond versus Jev’s 78.3
- Bottom line:
- Best: one call decides across 4 modalities.
- Worst: weaker accuracy than text-focused Clef on hard reasoning.
What is a decision model?
A decision model returns typed answers with probabilities instead of generated text. You send a state and a schema of questions. The model returns a probability for every allowed option. Clef supports 3 question types: noul (true or false), choice and score. The format follows the Jev and SystemOne API from TypeSafe AI.
How does Clef-omni work?
Clef-omni is post-trained from Qwen3-Omni-30B-A3B-Instruct. Cloudflare keeps the comprehension backbone and drops the text-to-speech output path. The model runs a single prefill pass over the full payload. Video frames and audio are synced into 1 sequence for joint processing.
A small joint schema head then scores the options. Each valid option first gathers evidence from the input. Field vectors then cross-attend across the full context to compute confidence scores. Cloudflare froze the Qwen3 backbone and trained LoRA adapters. Training combined label-smoothed cross-entropy with Brier score calibration.
Supported inputs on Workers AI, per the developer docs:
- Images: up to 4, PNG, JPEG or WebP, 64 to 1,024 tokens each
- Audio: up to 4 clips, WAV or MP3, 300 seconds each, about 780 tokens per minute
- Video: up to 2 clips, MP4 or WebM, 60 seconds each, sampled at 2 frames per second
- Questions: 1 to 64 per request
Cloudflare reports text decisions in about 130 ms at the median and images in about 150 ms. A 21-second video with sound scores in about 1.5 seconds.
How does Clef-omni perform on benchmarks?
Cloudflare compared Clef-omni against Clef, Clef-flash and Jev on its Decision Index run. Clef-omni leads on intent classification: 94.8 macro-F1 on BANKING77 and 97.7 on CLINC150+OOS. It also tops Amazon ESCI at 57.8.
It trails elsewhere. Home appliances case-exact accuracy is 69.3, versus 97.73 for Clef-flash. When2Call accuracy is 63.3, versus 80.97 for Jev. On TypeSafe’s workflow evals, Clef-omni scores below Clef in all 5 reported metrics.
Clef-omni vs Clef vs Clef-flash vs Jev
Benchmark scores are from Cloudflare’s announcement and were not independently verified.
How much faster is Clef?
Cloudflare moved hosted Clef to SGLang and tuned its serving stack. No new weights were released. Median latency fell from 262 ms to 152 ms at about 800 tokens, a 1.7x gain. At about 3,400 tokens it fell from 616 ms to 305 ms, a 2.0x gain. At about 16,000 tokens it dropped from 2,721 ms to 1,635 ms. Clef support lands in SGLang 0.5.22.
Key Takeaways
- Clef-omni scores text, images, audio and video in 1 decision call.
- It is a 30B-A3B MoE on Qwen3-Omni, Apache-2.0, at $0.15 per million tokens.
- Clef-flash now costs $0.038, under Jev’s $0.042, with 24k hosted context.
- Hosted Clef is 1.7x to 2.0x faster at the median after moving to SGLang.
- Clef-omni wins intent tasks but trails Clef on reasoning and workflow evals.
Check out the Cloudflare announcement, model weights and Workers AI docs. All credit goes to the researcher of this project. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well.
Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
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