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| 3 models | intfloatIntfloat: E5-Base-v2Remove × | googleGoogle: Gemini Embedding 2Remove × | googleembeddinggemma-2Remove × |
|---|---|---|---|
| What’s different | The e5-base-v2 embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as… | Gemini Embedding 2 is Google's first multimodal embedding model. We currently support mapping text and images into a unified vector space for semantic search and retrieval-augmented generation (RAG). It… | EmbeddingGemma 2 is a 740M parameter multimodal embedding model from Google DeepMind that maps text (incl. code), images, video and audio, alone or interleaved, into a single 768-dimensional vector space,… |
| Date | Nov 22, 2023Listed | May 20, 2026Listed | Oct 8, 2026Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | $0.005 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | $0.20 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | See provider quotes |
| Provider endpoints | 1 endpoint → | 1 endpoint → | 0 endpoints → |
Specifications | |||
| Context window | 512 | 8.2K | 8.2K |
| Max output | 460 | 7.4K | — |
| Parameters | — | — | — |
| Image input | Not reported | Supported | Not reported |
| Audio input | Not reported | Supported | Not reported |
| Video input | Not reported | Supported | Not reported |
| Open weights | Supported | Not reported | Not reported |