Gemini Embedding model
A specialized engine for high-dimensional vector representation, providing efficient numerical mapping of text and images. The Gemini Embedding model is best for semantic search, document retrieval, and recommendation systems that require fast, scalable similarity calculations across large datasets.
Documentation
Visit the Embeddings page for full coverage of features and capabilities.
gemini-embedding-001
| Property | Description |
|---|---|
| Model code | Gemini API gemini-embedding-001 |
| Supported data types | Input Text Output Text embeddings |
| Token limits[1](https://ai.google.dev/gemini-api/docs/tokens)^ | Input token limit 2,048 Output dimension size Flexible, supports: 128 - 3072, Recommended: 768, 1536, 3072 |
| Versions | Read the model version patterns for more details. - Stable: gemini-embedding-001 |
| Latest update | June 2025 |
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