text-embedding-ada-002
text-embedding-ada-002 is OpenAI's second-generation embedding model that unified multiple prior embedding endpoints into a single model, producing 1536-dimensional vectors suitable for search, clustering, classification, and recommendations.
import { embed } from 'ai';
const result = await embed({ model: 'openai/text-embedding-ada-002', value: 'Sunny day at the beach',})Providers
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About text-embedding-ada-002
text-embedding-ada-002 was released on December 15, 2022 as a major consolidation of OpenAI's embedding model lineup. Previously, OpenAI offered multiple first-generation embedding endpoints across different model sizes (Davinci, Curie, Babbage, Ada) and task types (similarity, search, classification). text-embedding-ada-002 unified all of these into a single model that produces 1536-dimensional vectors usable across search, clustering, classification, and recommendation tasks.
At launch, text-embedding-ada-002 was a substantial improvement over the first-generation models, outperforming the previous best (Davinci) on most benchmarks while being significantly cheaper and producing more compact vectors. It became the standard embedding model used by the majority of OpenAI API consumers.
The model has since been superseded by the text-embedding-3 family released in January 2024. text-embedding-3-small delivers higher MTEB scores at reduced cost with the same default 1536 dimensions, making it a direct upgrade path. text-embedding-3-large pushes accuracy further with 3072 dimensions and substantial multilingual improvements. For existing deployments that haven't yet migrated, text-embedding-ada-002 remains available and functional.
What To Consider When Choosing a Provider
- Configuration: text-embedding-ada-002 has been succeeded by the text-embedding-3 family, which offers both better performance and lower cost. For new projects, text-embedding-3-small is a drop-in replacement with higher quality at reduced cost.
- Configuration: If your vector database is already indexed with ada-002 embeddings, note that you cannot mix embeddings from different models in the same index. Migration requires re-embedding your corpus.
- 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-ada-002
Best For
- Existing deployments: Applications with vector indexes already built on ada-002 embeddings that can't yet re-index
- Legacy pipeline maintenance: Systems that depend on ada-002's specific embedding characteristics
- Backward compatibility: Workflows pinned to ada-002 for consistency with historical data
- Migration baselines: Benchmarking against ada-002 when evaluating newer embedding models
Consider Alternatives When
- New projects: Text-embedding-3-small delivers higher quality at reduced cost with the same 1536-dimension default
- Maximum quality: Text-embedding-3-large for the highest accuracy on retrieval-critical workloads
- Multilingual retrieval: Both text-embedding-3 variants significantly outperform ada-002 on MIRACL
- Cost optimization: Text-embedding-3-small offers immediate savings with no quality regression
Conclusion
text-embedding-ada-002 was the standard OpenAI embedding model for over a year, consolidating a fragmented lineup into a single practical endpoint. While newer models now offer better performance and lower cost, it remains available through AI Gateway for existing deployments that haven't yet migrated.
Frequently Asked Questions
Should I use text-embedding-ada-002 for a new project?
No. text-embedding-3-small is a direct upgrade: higher quality, lower cost, and the same default 1536-dimension output. Use the newer model for new projects.
Can I mix ada-002 and text-embedding-3 vectors in the same index?
No. Embeddings from different models are not compatible. Migration requires re-embedding your entire corpus with the new model.
What is the output dimension of text-embedding-ada-002?
1536 dimensions, fixed. Unlike the text-embedding-3 family, ada-002 does not support the
dimensionsparameter for flexible vector sizes.How does text-embedding-ada-002 compare to text-embedding-3-small on benchmarks?
text-embedding-3-small scores 1.3 points higher on MTEB (62.3% vs 61.0%) and 12.6 points higher on MIRACL (44.0% vs 31.4%), at lower cost.
How does AI Gateway handle authentication for text-embedding-ada-002?
AI Gateway accepts a single API key or OIDC token for all requests. You don't embed OpenAI credentials in your application; AI Gateway routes and authenticates on your behalf.
What are typical latency characteristics?
This page shows live throughput and time-to-first-token metrics measured across real AI Gateway embedding traffic.