Cloudflare shipped the second act of its open-weight decision model family on October 9: Clef-omni takes audio (wav/mp3) and video (mp4/webm) alongside text and image in a single API call, returning structured decision scores. The decision-model category has been largely text-only since TypeSafe's Jev debuted; Cloudflare is now pushing it toward multi-sensory input.
One prefill pass, four modalities
Clef-omni is built on the Qwen3-Omni-30B-A3B-Instruct MoE foundation. Cloudflare kept its multimodal comprehension backbone and discarded the text-to-speech output components — decision models are not LLMs, generate no output tokens, and run a quick prefill pass over the full payload: every candidate option gathers key evidence from the input (whether buried in text, visuals, or audio streams), then field vectors cross-attend across the context via two-stage attention routing to compute confidence scores. The official example is equipment acceptance: one photo of the unit, an audio recording of it running, and a video of the fan, paired with three questions — is the label visible, does it sound normal, is the fan running — all scored in one call. Training follows the Clef recipe: freeze the Qwen3 backbone, train LoRA adapters, label-smoothed cross-entropy plus Brier score calibration. Weights are open on HuggingFace.
Sub-150ms scoring and a price cut
The speed numbers are aggressive: text-only decisions return in about 130 ms median, images in about 150 ms, audio in a few hundred milliseconds, and a full 21-second video clip with sound is scored in about 1.5 seconds — all in one API call. On pricing, Clef-omni launched at 0.15 USD per million input tokens; in the same post Clef-flash dropped from 0.09 to 0.038 USD, which Cloudflare says is now cheaper than Jev. The trade-off for the cut is context: the hosted Clef-flash window shrank from 64k to 24k tokens — official data says only 0.24% of requests exceed 24k, and the self-host weights still support 256k. Clef itself moved to SGLang serving, with official figures of roughly 1.7-2x speedups, and the upstream PR lands in SGLang 0.5.22.
Wins on classification, yields on complex workflows
In the official benchmark table, Clef-omni ranks first in the family on BANKING77 (94.8 macro-F1), CLINC150+OOS (97.7), and Amazon ESCI (57.8) classification tasks. But Home appliances scores 69.3, far behind Clef-flash's 97.73; When2Call sits at 63.3 versus Jev's 80.97; and on TypeSafe's own evals, invoice-processing exact actions reach 60.2 versus Clef's 64.7. Multimodal breadth trades away some task depth — whether that trade is worth it depends on how much audio and video your workflow actually carries.
So what
Two things stand out. First, decision models are going multimodal along the same path LLMs did, and Cloudflare's cadence — "decided Friday evening, trained over the weekend, launched Thursday" to this week's multimodal-plus-price-cut-plus-speedup triple — shows how an edge network vendor uses open weights and aggressive pricing to cut into the inference market. Second, the Qwen open-weight base gained another overseas commercial case — in the same week, Microsoft's Microsoft-Decision-1 was built on Qwen3.5-9B (per Solidot). When a "judgment" costs 0.038 USD per million tokens, the question is no longer "should we plug in AI" but "how many judgments in our process are still made by humans."
Reference: Cloudflare's official announcement https://blog.cloudflare.com/clef-faster-cheaper-multimodal/