What's the most efficient memory-enabled model for searching meeting archives and answering follow-up questions?

If you mean efficient for large meeting archives + follow-up Q&A with memory, the best choice is usually not a single “most powerful” model, but a small/fast model with retrieval (RAG) + short-term conversation memory.

Best practical recommendation

• OpenAI GPT-4o mini: strong balance of speed, cost, and quality for archive search and Q&A.
• If you need higher accuracy on tricky questions, use GPT-4.1 or GPT-4o for the answer step, but keep GPT-4o mini for retrieval/routing.

Important note

For “memory-enabled” meeting archive search, the real memory should live in:

1. Vector database / search index for archived meetings
2. Conversation memory store for user follow-ups
3. The model only reasons over retrieved snippets

That architecture is usually more efficient than relying on a model’s built-in context window.

Good stack

• Embedding model: text-embedding-3-small for cost efficiency, text-embedding-3-large for better recall
• Answering model: gpt-4o-mini
• Optional reranker: to improve search precision

If you want the shortest answer

GPT-4o mini + RAG is the most efficient setup for meeting archive search and follow-up questions.

If you tell me your constraints—budget, latency, archive size, and whether you need exact quotes or just summaries—I can recommend a specific model+architecture combo.