Text Embedding 005
Text Embedding 005 is an English-language text embedding model with a 66.31% average Massive Text Embedding Benchmark (MTEB) score at 768 dimensions, supporting dynamic embedding sizes down to 256 dimensions to reduce storage and compute costs with minor performance tradeoffs.
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const result = await embed({ model: 'google/text-embedding-005', value: 'Sunny day at the beach',})Providers
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| Provider |
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 Google Vertex AI | $0.03/M | — | — | 08/01/2024 |
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About Text Embedding 005
Text Embedding 005 is Google's English-language text embedding model built on the Gecko architecture, which uses knowledge distillation from large language models (LLMs) to achieve competitive downstream task performance at a compact embedding size. At its full 768-dimension output, the model scores 66.31% on the MTEB benchmark, a standard evaluation suite covering eight categories including retrieval, reranking, clustering, classification, and semantic similarity.
This means Text Embedding 005 delivers competitive retrieval and similarity quality without requiring high-dimensional vector indices.
Dynamic embedding sizes are supported through Matryoshka Representation Learning (MRL), which trains the model to produce accurate representations at multiple dimension levels from a single pass. At 256 dimensions, it scores 64.37% on MTEB, a two-point reduction that may be an acceptable tradeoff when vector storage costs at scale are significant. This flexibility is built into the model architecture, not post-hoc dimension reduction.
What To Consider When Choosing a Provider
- Configuration: When choosing between 256- and 768-dimension outputs, Text Embedding 005 shows a performance difference of approximately 2 percentage points on MTEB (64.37% vs 66.31%). For cost-sensitive applications with high vector storage volume, 256 dimensions can meaningfully reduce infrastructure costs with a modest quality tradeoff.
- 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 005
Best For
- English semantic search: Document retrieval over English corpora, with a 66.31% MTEB score at the full 768-dimension output
- English clustering and classification: Grouping and labeling tasks over English text corpora at 768 dimensions
- 768-dimension vector similarity: Applications where 768-dimension indices are the standard infrastructure choice
- Cost-sensitive deployments: Use 256-dimension output to reduce storage and compute
- Google cloud integrations: Applications on BigQuery or Vertex AI Search that are already in the Gecko model family
Consider Alternatives When
- Multilingual content: Your corpus includes non-English text or mixed languages (consider
google/text-multilingual-embedding-002) - Higher dimensions needed: You need more than 768 dimensions for maximum theoretical discriminative power
Conclusion
Text Embedding 005 delivers strong English retrieval and semantic similarity quality on MTEB at a compact 768-dimension size, with the flexibility to operate at 256 dimensions for storage-sensitive deployments. For English-language semantic search, clustering, and similarity applications, it provides strong performance per dimension relative to larger models.