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.
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU Kanpur) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. Email pranjulrathour41@gmail.com.
