Cosine similarity and inner product are the similarity metrics that work best with text-embedding-3-large embeddings. These metrics are well suited for dense, high-dimensional semantic vectors and are the most commonly used options in production systems. They focus on the relative direction of vectors rather than exact coordinate values, which aligns well with how semantic meaning is encoded.
Cosine similarity measures the angle between two vectors, effectively normalizing for vector length. This is useful because the magnitude of text embeddings usually does not carry semantic meaning on its own. Inner product is also widely used and often behaves similarly, especially if embeddings are normalized consistently at ingestion time. In practical terms, both metrics tend to produce stable rankings for semantic search, recommendation, and clustering tasks. Developers usually start with cosine similarity because it is intuitive and widely documented, then experiment with inner product only if needed.
Vector databases such as Milvus and Zilliz Cloud support both metrics natively and allow you to configure them per collection. The most important rule is consistency: the same metric should be used during indexing and querying. Changing metrics without reindexing can lead to unpredictable results. In practice, teams rarely need exotic distance functions; cosine similarity combined with good chunking and metadata filtering delivers reliable results with text-embedding-3-large at scale.
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