Introducing Clef-omni with full multimodality, plus a faster Clef and a cheaper Clef-flash

Introduces Clef-omni, a multimodal decision model capable of processing audio, video, image, and text input, built on a Qwen3-Omni-30B-A3B-Instruct MoE foundation. It skips output token generation, focusing on scoring modalities directly via a two-stage attention routing mechanism and a built-in lexical grammar for schema-constrained scoring. Clef-flash pricing has been reduced to $0.038 per M input tokens with a context window of 24k, while Clef remains at $0.24 per M input tokens with a 64k context window. Clef model performance has been improved by 1.7x-2.0x median speedup through serving infrastructure optimizations, including the adoption of SGLang for serving. Benchmarks demonstrate competitive performance for Clef-omni, Clef, and Clef-flash across various tasks and modalities.