Gemini Embedding 001
Gemini Embedding 001 is Google's generally available text embedding model, with a strong position on the Massive Text Embedding Benchmark (MTEB) Multilingual leaderboard across retrieval, classification, and other tasks, with Matryoshka-based dimension flexibility and support for over 100 languages.
import { embed } from 'ai';
const result = await embed({ model: 'google/gemini-embedding-001', value: 'Sunny day at the beach',})What To Consider When Choosing a Provider
- Configuration: When planning vector storage, decide on your target output dimension (3072, 1536, or 768) before ingesting embeddings, as changing dimensions later requires re-embedding your full 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 Gemini Embedding 001
Best For
- Multilingual semantic search: Building retrieval systems that must return results across 100+ languages from a single embedding space, without maintaining separate per-language indexes
- Retrieval-augmented generation (RAG) pipelines: Embedding document corpora for dense retrieval that feeds into a generative model, where high-quality passage retrieval directly determines answer accuracy
- Cross-domain classification: Strong performance across science, legal, finance, and coding domains, making it suitable for classification tasks that span multiple subject areas
- Storage-cost-optimized vector databases: Using MRL to reduce dimensions to 768 or 1,536 for large corpora where full-precision embeddings would be cost-prohibitive, with a controlled quality trade-off
Consider Alternatives When
- Multimodal embedding needs: Your application requires embedding images, video, audio, or documents in addition to text (Gemini Embedding 2 provides natively multimodal embeddings in a single shared space)
- Inputs exceed 2,048 tokens: Your documents cannot be chunked effectively and requests above the limit will require truncation or segmentation
- Streaming or generative output: You need real-time streaming or generated text rather than vector representations
Conclusion
Gemini Embedding 001 offers a production-ready embedding foundation with consistent strong multilingual performance and dimension flexibility that makes it adaptable across deployment constraints, from small vector stores to large-scale retrieval corpora. Teams building multilingual RAG systems, cross-domain search, or classification pipelines have a commercially stable model with strong benchmark backing.
Frequently Asked Questions
How does gemini-embedding-001 rank on MTEB Multilingual?
It ranks highly on the MTEB Multilingual leaderboard, a position it has maintained since its experimental launch.
What is Matryoshka Representation Learning and how does it affect output dimensions?
MRL is a training technique that nests information across dimension scales, allowing the model to produce embeddings that remain meaningful when truncated to smaller sizes. Google recommends 3,072, 1,536, or 768 dimensions; the default is 3,072 for highest quality.
How many languages does gemini-embedding-001 support?
The model supports over 100 languages, consistent with its strong multilingual benchmark results.
What is the maximum input token length per request?
A maximum of 2,048 input tokens per embedding request.
What is the pricing?
This page lists the current rates. Multiple providers can serve Gemini Embedding 001, so AI Gateway surfaces live pricing rather than a single fixed figure.
How is gemini-embedding-001 priced on AI Gateway?
This page lists the current rates. Multiple providers can serve Gemini Embedding 001, so AI Gateway surfaces live pricing rather than a single fixed figure.
