Embedding model dimensions are appropriate for the use case

Embedding model choice directly determines storage cost, query latency, and API cost for every document in your system. The legacy text-embedding-ada-002 model is strictly dominated by text-embedding-3-small, which achieves higher retrieval quality at lower cost — there is no justification for using ada-002 in new systems. Using text-embedding-3-large (3,072 dimensions) when text-embedding-3-small (1,536 dimensions) produces equivalent retrieval quality for your dataset wastes storage and doubles query payload size. ISO 25010 performance-efficiency requires that resource choices be calibrated to actual requirements.

Switch from text-embedding-ada-002 to text-embedding-3-small for all new embedding workloads. Use the dimensions parameter to reduce dimensionality further if your dataset is small and full fidelity is not needed.

// Before — legacy model, obsolete for new systems
const embedding = await openai.embeddings.create({
  model: "text-embedding-ada-002",
  input: text,
});

// After — modern model, better quality at lower cost
const embedding = await openai.embeddings.create({
  model: "text-embedding-3-small",
  input: text,
  // dimensions: 512, // optional: reduce for large-dataset storage savings
});

After switching models, update the vector table's dimension column to match the new model's output. Reindex existing embeddings — they are not compatible across models.