LFM3 makes modality selection part of model selection

A shared training backbone does not imply identical input and output capabilities. That distinction matters when reading Liquid AI's LFM3 family announcement, published October 27, 2026.

The article describes separate variants for text, vision, audio, multimodal interaction, and structured decisions. The vision variant accepts images and video; the audio variant handles text and sound; Omni combines the modalities. Treating the family's combined capabilities as a specification for every checkpoint would obscure differences that directly affect integration.

The team also presents specialization recipes, including receipt extraction and spoken tool calls. Those examples establish concrete adaptation paths. They do not demonstrate that a chosen variant already handles another application's terminology, document layouts, or unusual requests.

I would select candidates by the inputs available and the outputs the application actually needs, then check each variant's dependencies before comparing task results. A broader modality set alone would not settle that choice. The unresolved research question is how specialization affects performance on rare cases that the adaptation examples do not cover.

Source: Leonie Monigatti and the Liquid AI team, Build specialized multimodal models with LFM3.