Which embedding model works best for RAG when using GPT-5.4 mini/nano or GPT-4.1 mini/nano as the LLM?

Hi everyone,

I’m building a Retrieval-Augmented Generation (RAG) system and I’m trying to choose the right embedding model.

My generation models will likely be one of these:

• GPT-5.4 mini / nano

• GPT-4.1 mini / nano

I understand that the embedding model and the LLM are separate components in a RAG pipeline, but I would like to know what embedding models provide the best retrieval quality when paired with these models.

Some questions:

1. Embedding model selection

   • Which embedding models are currently recommended for high-quality RAG?

   • Is using OpenAI embedding models the best choice when the generation model is also OpenAI?

   • Are there open-source embedding models that can outperform hosted embedding models for specific use cases?

2. Embedding dimension and performance

   • How much does embedding dimension affect retrieval quality?

   • Is a larger embedding model always better, or can smaller embeddings provide similar results with lower cost and latency?

3. Compatibility with GPT models

   • Does the choice of embedding model significantly impact the performance of GPT-5.4 mini/nano or GPT-4.1 mini/nano?

   • Are there recommended combinations of embedding model + GPT model for RAG applications?

4. Practical RAG considerations

   • For a production RAG system with many documents and frequent queries, what should be prioritized:

     • Better semantic retrieval quality?

     • Lower embedding cost?

     • Faster retrieval latency?

     • Smaller vector storage size?

Example use case:

• Multi-tenant SaaS knowledge base

• Documents: FAQs, product information, support documents, policies

• Vector database: Pinecone / Chroma / FAISS

• LLM: GPT-5.4 mini/nano or GPT-4.1 mini/nano

I would appreciate recommendations based on real-world RAG deployments and benchmarks.

Thanks!