CLIP vs ViT vs a multimodal LLM: picking a vision model without guessing

Vision model choice gets confusing because CLIP, plain ViT classifiers and multimodal LLMs all 'understand images' but solve different problems well.

Pick by task

• Need image-to-image or text-to-image search: CLIP-family models, built for exactly this shared embedding space.
• Need a fast, narrow classifier (defect vs no defect, cat vs dog): a plain fine-tuned ViT, smaller and cheaper than either alternative.
• Need to describe, reason about, or answer open questions about an image: a multimodal LLM — heavier, but far more flexible.

The trade-off in one line

CLIP and ViT are fast and cheap but narrow; multimodal LLMs are flexible and improving fast but slower and costlier per call. Start narrow if your task is genuinely narrow — you can always add a multimodal LLM step later for the cases the narrow model can't handle.

See multimodal LLMs for student projects for what the flexible end of this spectrum looks like in practice.

— Pranjul Rathour, GenAI Engineer from Kanpur, India. Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at any campus: pranjulrathour41@gmail.com.