Embedding Dimension Calculator

Embedding dimensions define the size of the vector produced for each piece of text. A model with 1536 dimensions outputs a 1536-element array of floating-point numbers for every text input. Higher dimensions can capture more semantic nuance but consume more storage and memory.

Most embedding models output float32 (32-bit floating-point) numbers. Each float32 value takes exactly 4 bytes. So a 1536-dimension vector requires 1536 × 4 = 6,144 bytes (≈6 KB) per document. When storing millions of vectors, this adds up quickly.

Not necessarily. text-embedding-3-large (3072 dims) generally outperforms text-embedding-ada-002 (1536 dims) on benchmarks, but the improvement depends on your task. For many retrieval tasks, smaller models like voyage-3 (1024 dims) are competitive while being 4× cheaper to store.

Approximate Nearest Neighbor (ANN) search trades a small amount of accuracy for massive speed gains — typically 99%+ recall at 10-100× the speed. Exact search guarantees finding the true nearest neighbors but doesn't scale beyond ~1M vectors. All production vector databases use ANN.

OpenAI text-embedding-3 models excel at English and code tasks and integrate easily with the OpenAI ecosystem. Cohere embed-v3 models have strong multilingual support (100+ languages) and work well for enterprise search. For multilingal use cases, Cohere is often the better choice.