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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. Your use subject to Google's Terms & Privacy Policies.

Use with AI GatewayView docs
index.ts
import { embed } from 'ai';


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