text-embedding-3-small
text-embedding-3-small delivers higher MTEB scores than ada-002 at lower cost, with a 1536-dimension default that drops into existing pipelines and a flexible dimensions parameter for further storage savings.
import { embed } from 'ai';
const result = await embed({ model: 'openai/text-embedding-3-small', value: 'Sunny day at the beach',})Providers
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| Provider |
|---|
 Azure | $0.02/M | — | — | 01/25/2024 | ||||||||
 OpenAI | $0.02/M | — | — | 01/25/2024 |
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About text-embedding-3-small
OpenAI announced text-embedding-3-small on January 25, 2024 alongside its larger sibling. At reduced per-token cost compared to ada-002, it scores 62.3% on MTEB (Massive Text Embedding Benchmark), 1.3 points higher than the model it replaces.
Multilingual retrieval improves substantially too. text-embedding-3-small reaches 44.0% on MIRACL versus ada-002's 31.4%, a 12.6-point gain that matters for any pipeline handling non-English or mixed-language content.
Like text-embedding-3-large, text-embedding-3-small supports the dimensions parameter via Matryoshka training. The default 1,536-dimension output matches ada-002 for drop-in compatibility, but you can reduce it when memory or storage costs are a concern. Semantic structure is front-loaded into the earlier dimensions, so shorter vectors still carry meaningful signal.
text-embedding-3-small fits well in the query-time embedding path of Retrieval-Augmented Generation (RAG) architectures. Every user query must be embedded before retrieval, and at scale that per-query cost and latency compounds. Low cost and fast inference make it a natural fit for that leg of the pipeline.
What To Consider When Choosing a Provider
- Configuration: Because the default output is 1536 dimensions, identical to ada-002, you can swap models without touching your vector database schema or index configuration.
- Zero Data Retention: AI Gateway supports Zero Data Retention for this model via direct gateway requests (BYOK is not included). To configure this, check the documentation.
- Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.
When to Use text-embedding-3-small
Best For
- Ada-002 drop-in replacement: No schema changes and immediate cost savings with the same 1536-dimension default
- Real-time RAG pipelines: Query-time embedding where latency and cost per request matter
- High-volume indexing: Document pipelines where the reduced cost yields significant infrastructure savings
- Multilingual retrieval: Applications that benefit from the 12.6-point MIRACL improvement over ada-002
- Budget-conscious projects: Embedding quality is important but not the absolute ceiling
Consider Alternatives When
- Retrieval accuracy bottleneck: The 2.3-point MTEB gap versus text-embedding-3-large is material for your use case
- Heavily multilingual corpus: The larger model's 54.9% MIRACL score versus 44.0% would produce meaningfully better results
- Maximum dimensionality: Specialized downstream models need the full 3072-dimension vectors from text-embedding-3-large
Conclusion
text-embedding-3-small is the practical default embedding model for most applications on AI Gateway. It costs less than ada-002, performs better, and drops in without migration pain. Start here unless you have a specific reason to pay for the large variant's extra accuracy.