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Text Embedding 005

google/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.

index.ts
import { embed } from 'ai';


const result = await embed({
  model: 'google/text-embedding-005',
  value: 'Sunny day at the beach',
})

What To Consider When Choosing a Provider

  • 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 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.

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.

FAQ

Text Embedding 005 scores 66.31% on average on MTEB at 768 dimensions. At 256 dimensions using dynamic sizing, it scores 64.37%. MTEB covers eight English NLP task categories including retrieval, reranking, clustering, classification, and semantic similarity.

The model uses Matryoshka Representation Learning to produce accurate embeddings at multiple dimension levels from a single inference pass. Users can request 256 or 768-dimension output. This is a training-level capability, not post-hoc truncation, which preserves more information per dimension at smaller sizes.

Use 256 dimensions when vector storage costs at scale are a constraint and the approximately 2-point MTEB score difference is acceptable for your use case. Use 768 dimensions for maximum retrieval accuracy.

No. This is an English-only model. For multilingual or non-English embedding tasks, use google/text-multilingual-embedding-002.

The model performs well on retrieval, reranking, clustering, classification, and semantic similarity across the eight MTEB task categories.

Check the pricing panel on this page for today's numbers. AI Gateway tracks rates across every provider that serves Text Embedding 005.

At 768 dimensions, the model competes well against MTEB entries of the same embedding size and models with significantly more parameters or higher dimensionality. This makes it an efficient choice for applications where inference cost and vector storage size matter.