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Multimodal AI Workflows: When to Use Text, Image, and Audio Models

Most teams building AI products start the same way: pick a text model, point it at the problem, and iterate from there. That works well enough at first. Text models are flexible, well-documented, and capable of handling a surprising range of tasks. But at some point, usually when the product gets more complex or the use cases get more specific, that approach starts to crack. The shift we're seeing now isn't really about any single model getting better. It's about teams learning to combine modalities deliberately. Text, image, and audio models each have a natural domain where they genuinely outperform the alternatives. The question isn't which model is best in general. It's which modality fits the specific task at hand, and how to wire them together without making the whole pipeline fragile.