Google’s Gemini Embed Text Model, GEMINI-EMBEDDING-001Developers are now often available through the Gemini API and Google AI Studio, bringing powerful multilingual and flexible text representation capabilities to the wider AI ecosystem.
Multilingual support and size flexibility
- Supports over 100 languages: GEMINI Embedding is optimized for global applications and runs on over a hundred languages, which is an ideal solution for projects with multiple language requirements.
- Matryoshka says learning: This architecture utilizes Matryoshka representation learning, enabling developers to Scale embedding vector Effectively – Choose from the default 3072 dimensions or downscale to 1536 or 768 based on the tradeoff between your application and performance. This adaptable structure allows you to optimize speed, cost and storage Minimum mass loss When you reduce the vector size.
Technical specifications and model performance
- Input capacity:deal with 2048 tokens for each inputit is recommended that future updates may further expand this limit.
- Benchmark person in charge: Since its early launch, Gemini-Embedding-001 has been implemented Highest score In the massive text embedding benchmark (MTEB) multilingual rankings, it surpasses previous Google models and external products across fields such as science, law and coding.
- Unified architecture: Merge the functionality that previously required multiple professional models, simplifying the workflow for search, retrieval, clustering and classification tasks.
Key Features
- Default embed 3072 dimensions (Support 1536 or 768 cutoff)
- Vector Normalization Compatibility with cosine similarity and vector search frameworks
- Minimum performance degradation Reduced size
- Enhanced compatibility with popular vector databases such as Pinecone, Chromadb, Qdrant, Weaviate and Google Databases (AlloyDB, Cloud SQL)
| Metric/task | GEMINI-EMBEDDING-001 | Traditional Google Models | cohere v3.0 | Openai-3 big |
|---|---|---|---|---|
| MTEB (Multi-language) Mean (Task) | 68.37 | 62.13 | 61.12 | 58.93 |
| MTEB (multi-language) average value (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 search | 5.18 | 4.08 | -1.89 | -2.68 |
| Multi-label classification | 29.16 | 22.8 | 22.74 | 22.03 |
| A pair of categories | 83.63 | 81.14 | 79.88 | 79.17 |
| Reread | 65.58 | 61.22 | 64.07 | 63.89 |
| Search | 67.71 | 59.68 | 59.16 | 59.27 |
| STS (Semantic Text 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 application
- Semantic search and search: Improved documentation and language matching
- Classification and clustering: Powerful text classification and document grouping
- Search Authorized Generation (RAG): Enhanced LLM-supported application retrieval accuracy
- Cross-language and multilingual applications: Easily manage international content
Integration and Ecosystem
- API access: Use Gemini-Embedding-001 in Gemini API, Google AI Studio, and Vertex AI.
- Seamless integration: Compatible with leading vector database solutions and cloud-based AI platforms for easy deployment into modern data pipelines and applications.
Pricing and migration
| layer | Pricing | notes |
|---|---|---|
| Free | Limited use | Great for prototypes and experiments |
| Remunerated | $0.15 per 1 million token | Production Demand Scale |
- Depreciation schedule:
gemini-embedding-exp-03-07: Deprecated August 14, 2025- Earlier models (Embedding-001, Text-Embedding-004): Deprecated in early 2026
- migrate:suggestion Migrate to Gemini-Embedding-001 Benefit from continuous improvement and support.
expect
- Batch processing: Google announces upcoming support for batch APIs Asynchronous and cost-effective embedding generation Large scale.
- Multi-mode embedding: Future updates may allow not only text but also unified embedding of code and images, thus advancing the breadth of Gemini applications.
in conclusion
The general availability of Gemini-Embedding-001 marks a major advancement in the Google AI toolkit, providing developers with powerful, flexible and multilingual text embedding solutions that adapt to a wide range of application needs. The model has scalable dimensions, top-notch multilingual performance, and seamless integration into the popular AI and vector search ecosystem, making teams smarter and faster to build smarter, faster and relevant applications around the world. As Google continues to innovate with features such as batch processing and multi-mode support, Gemini-Embedding-001 lays a solid foundation for the future of semantic understanding in AI.
| Check out the technical details. All credits for this study are to the researchers on the project. Ready to connect with 1 million+ AI development/engineers/researchers? See how NVIDIA, LG AI Research and Advanced AI companies leverage Marktechpost to reach target audiences [Learn More] |
Asif Razzaq is CEO of Marktechpost Media Inc. As a visionary entrepreneur and engineer, ASIF is committed to harnessing the potential of artificial intelligence to achieve social benefits. His recent effort is to launch Marktechpost, an artificial intelligence media platform that has an in-depth coverage of machine learning and deep learning news that can sound both technically, both through technical voices and be understood by a wide audience. The platform has over 2 million views per month, demonstrating its popularity among its audience.
