Your question is Embedding Model Trade-offs. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
What are the primary trade-offs when choosing between different embedding models for vector search?
Discuss how you would compare embedding quality, dimensionality, latency, cost, context length, multilingual support, domain fit, and operational constraints. Explain how you would evaluate candidate models offline and validate the choice in production, including retrieval failures, corpus changes, and migration concerns.
Your answer should distinguish indexing-time costs from query-time costs and address when a smaller or domain-specific model is preferable to a larger general-purpose model.