text-embedding-ada-002 generally delivers solid, reliable embeddings at low cost, but newer OpenAI embedding models (for example, text-embedding-3-small and text-embedding-3-large) often achieve better retrieval quality and semantic precision while remaining affordable. In practical terms, “better” usually shows up as improved ranking for hard queries (ambiguous wording, domain-specific phrasing, longer passages) and fewer “near miss” results where the text looks similar but isn’t actually relevant. If your product’s success depends on search relevance, the newer models are frequently worth evaluating, even if text-embedding-ada-002 is already working.
From an engineering standpoint, the biggest operational difference is usually the embedding behavior rather than the surrounding pipeline. Your core workflow—chunk text, embed, store vectors, run similarity search—stays the same. What changes is how well the embeddings separate meaning in vector space. With text-embedding-ada-002 (1536 dimensions), you can get strong results with good chunking and metadata filters, but newer models can be more forgiving when chunking isn’t perfect or when user queries are messy. In practice, many teams run an A/B test: embed the same evaluation set with both models, then measure top-k hit rate (did the correct document appear in top 5?), ranking metrics like MRR, and qualitative “does this feel right?” checks on real queries.
When you store embeddings in a vector database such as Milvus or Zilliz Cloud, the comparison becomes easy to operationalize. You can keep two collections—one for text-embedding-ada-002 and one for the newer model—and run the same query traffic against both to compare latency, memory footprint, and relevance. If you see significant relevance gains without unacceptable cost increases, that’s a strong signal to migrate. For more information, click here： https://zilliz.com/ai-models/text-embedding-ada-002