ModelRefs / Text Embedding 3 Small - AI model implementation reference

Text Embedding 3 Small - AI model implementation reference

Text Embedding 3 Small is OpenAI's cost-efficient hosted text embedding model for retrieval, semantic search, and RAG, positioned below Text Embedding 3 Large on capacity while remaining substantially cheaper. It supports shortened dimensions and should be benchmarked against the large model on your own corpus before a routing decision.

Overview

Text Embedding 3 Small is attributed to OpenAI in ModelRefs' canonical registry. Tracked modalities: Text input, Vector embedding output. Primary use cases considered on ModelRefs: Cost-sensitive retrieval and semantic search at scale; High-volume RAG indexing where per-vector cost dominates.

This ModelRefs profile is decision-support material, not a final or universal ranking. Confirm current behavior, access, pricing, limits, licensing, and lifecycle in OpenAI's own documentation, and evaluate Text Embedding 3 Small on representative workloads before implementation.

Benchmark & Evaluation

ModelRefs currently has partial, narrow benchmark coverage for Text Embedding 3 Small. Treat the available benchmark evidence as one input to the decision, not a guarantee that Text Embedding 3 Small is the strongest option for your workload, and evaluate it on representative workloads before selecting it.

  • No benchmark score is imported into this editorial record. Canonical benchmark runs and scores are governed separately with their own provenance and render only through those records; coverage in ModelRefs is currently narrow (partial), so any scored comparison must show its coverage limits.
  • ModelRefs holds canonical run evidence on retrieval benchmarks (MTEB, MIRACL); coverage is narrow and results are corpus-dependent.

Implementation considerations

  • Benchmark small vs. large directly on your retrieval task; the cheaper model is often sufficient but this must be measured, not assumed.
  • Decide the embedding dimension explicitly and keep index and query dimensions consistent.
  • Hosted through the OpenAI embeddings API endpoint.
  • Verify current dimensions, rate limits, retention controls, and pricing for the exact account tier before rollout.

Risks and limitations

  • Embedding quality is task- and domain-specific: retrieval/RAG performance depends on chunking, distance metric, and re-ranking, and does not follow chat-model quality intuitions.
  • Hosted embedding behavior, dimensions, pricing, and lifecycle can change; verify the current model snapshot and deprecation status before production reliance.

Source coverage

This reference is Provisional. Model behavior, access, pricing, limits, licensing, and lifecycle can change; verify the linked provider documentation and run task-specific evaluations before implementation.

Connected ModelRefs evidence

Sources

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