Google’s Gemini Embedding text model, gemini-embedding-001, is now generally available to developers via the Gemini API and Google AI Studio, bringing powerful multilingual and flexible text representation capabilities to the broader AI ecosystem.
Multilingual Support and Dimensional Flexibility
- Supports 100+ languages: Gemini Embedding is optimized for global applications and works across more than a hundred languages, making it an ideal solution for projects with diverse linguistic requirements.Matryoshka Representation Learning: The architecture leverages Matryoshka Representation Learning, allowing developers to scale embedding vectors efficiently—choose from the default 3072 dimensions or downscale to 1536 or 768, depending on your application’s trade-off between accuracy and performance. This adaptable structure lets you optimize for speed, cost, and storage with minimal quality loss as you reduce vector size.
Technical Specifications and Model Performance
- Input Capacity: Processes up to 2048 tokens per input, with suggestions that future updates may further expand this limit.Benchmark Leader: Since its early rollout, gemini-embedding-001 has achieved top scores on the Massive Text Embedding Benchmark (MTEB) Multilingual leaderboard, surpassing both previous Google models and external offerings across domains like science, legal, and coding.Unified Architecture: Consolidates capabilities that previously required multiple specialized models, simplifying workflows for search, retrieval, clustering, and classification tasks.
Key Features
- Default embeddings with 3072 dimensions (truncation supported for 1536 or 768)Vector normalization for compatibility with cosine similarity and vector search frameworksMinimal performance drop with reduced dimensionalityEnhanced compatibility with popular vector databases (e.g., Pinecone, ChromaDB, Qdrant, Weaviate) and Google databases (AlloyDB, Cloud SQL)
| Metric/Task | Gemini-embedding-001 | Legacy Google models | Cohere v3.0 | OpenAI-3-large |
|---|---|---|---|---|
| MTEB (Multilingual) Mean (Task) | 68.37 | 62.13 | 61.12 | 58.93 |
| MTEB (Multilingual) Mean (TaskType) | 59.59 | 54.32 | 53.23 | 51.41 |
| Bitext Mining | 79.28 | 70.73 | 70.50 | 62.17 |
| Classification | 71.82 | 64.64 | 62.95 | 60.27 |
| Clustering | 54.59 | 48.47 | 46.89 | 46.89 |
| Instant Retrieval | 5.18 | 4.08 | -1.89 | -2.68 |
| Multilabel Classification | 29.16 | 22.8 | 22.74 | 22.03 |
| Pair Classification | 83.63 | 81.14 | 79.88 | 79.17 |
| Reranking | 65.58 | 61.22 | 64.07 | 63.89 |
| Retrieval | 67.71 | 59.68 | 59.16 | 59.27 |
| STS (Semantic Textual Similarity) | 79.4 | 76.11 | 74.8 | 71.68 |
| MTEB (Eng, v2) | 73.3 | 69.53 | 66.01 | 66.43 |
| MTEB (Code, v1) | 76 | 65.4 | 51.94 | 58.95 |
| XOR-Retrieve | 90.42 | 65.67 | — | 68.76 |
| XTREME-UP | 64.33 | 34.97 | — | 18.80 |
Practical Applications
- Semantic Search & Retrieval: Improved document and passage matching across languagesClassification & Clustering: Robust text categorization and document groupingRetrieval-Augmented Generation (RAG): Enhanced retrieval accuracy for LLM-backed applicationsCross-Language & Multilingual Apps: Effortless management of internationalized content
Integration & Ecosystem
- API Access: Use gemini-embedding-001 in the Gemini API, Google AI Studio, and Vertex AI.Seamless Integration: Compatible with leading vector database solutions and cloud-based AI platforms, enabling easy deployment into modern data pipelines and applications.
Pricing and Migration
| Tier | Pricing | Notes |
|---|---|---|
| Free | Limited usage | Great for prototyping and experimentation |
| Paid | $0.15 per 1M tokens | Scales for production needs |
- Deprecation Schedule:
gemini-embedding-exp-03-07: Deprecated August 14, 2025Earlier models (embedding-001, text-embedding-004): Deprecation through early 2026Looking Forward
- Batch Processing: Google has announced upcoming support for batch APIs to enable asynchronous and cost-effective embedding generation at scale.Multimodal Embeddings: Future updates may allow unified embeddings for not just text but also code and images, advancing the breadth of Gemini’s applications.
Conclusion
The general availability of gemini-embedding-001 marks a major advancement in Google’s AI toolkit, providing developers with a powerful, flexible, and multilingual text embedding solution that adapts to a wide range of application needs. With its scalable dimensionality, top-tier multilingual performance, and seamless integration into popular AI and vector search ecosystems, this model equips teams to build smarter, faster, and more globally relevant applications. As Google continues to innovate with features like batch processing and multimodal support, gemini-embedding-001 lays a strong foundation for the future of semantic understanding in AI.
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