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Pick up to four models and compare the trade-offs that matter: context, price, API access, weights, inputs, outputs, and use cases.
| Attribute | Gemini Embedding 2 Google |
|---|---|
| Summary | A Gemini embedding model for converting content into vectors for search, recommendations, and RAG. |
| Best for | Semantic search, multimodal retrieval, recommendations, clustering |
| Watch out for | Embeddings require a vector database and domain-specific evaluation. |
| Family | Gemini Embedding |
| Type | Embedding |
| Status | Stable |
| Released | Not listed |
| Context window | Not listed |
| Max output | Not listed |
| Inputs | Text and supported multimodal content |
| Outputs | Embedding vector |
| Reasoning | Not applicable |
| Tool calling | No |
| Structured output | Embedding vector |
| API available | Yes |
| Open weights | No |
| Self-hostable | No |
| License | Proprietary |
| Price | See provider pricing |