all-mpnet-base-v2 is usually chosen when you want higher retrieval quality, while all-MiniLM-L12-v2 is usually chosen when you want lower latency and lower cost. Both are sentence embedding models, but they sit at different points on the quality–efficiency curve. all-MiniLM-L12-v2 is compact and fast, often good enough for many English semantic search tasks. all-mpnet-base-v2 is larger and typically produces embeddings that separate meaning more cleanly, which can improve recall for tricky queries and reduce “near misses” where the retrieved result is topically related but not actually the answer.
From an engineering viewpoint, the difference often shows up in edge cases. If your corpus includes many similar documents (e.g., multiple versions of API docs, many tickets with overlapping language, or FAQs that differ by one constraint), all-mpnet-base-v2 can be more robust in distinguishing them. The tradeoff is inference cost: embedding a million chunks will take longer, and query-time embedding will consume more CPU (or GPU) per request. That may not matter for a low-QPS internal tool, but it matters a lot for a public search endpoint with strict p95 latency requirements. Another practical difference is index size: mpnet-base models commonly output higher-dimensional embeddings than MiniLM models, which increases storage and memory footprint for large corpora.
A good way to decide is to A/B test on your own evaluation set using the same retrieval infrastructure. Store embeddings from both models in separate collections in a vector database such as Milvus or Zilliz Cloud, run the same query set, and compare metrics like recall@10 and nDCG@10 alongside latency and cost. Many teams discover that if they improve chunking and filtering, MiniLM becomes “good enough,” but if they need stronger semantic separation without a complex reranker, mpnet-base is worth the extra compute. The right choice is the one that meets your quality target inside your latency and cost envelope.
For more information, click here: https://zilliz.com/ai-models/all-mpnet-base-v2