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| 3 models | intfloatIntfloat: E5-Large-v2Remove × | googleembeddinggemma-300mRemove × | intfloatIntfloat: E5-Base-v2Remove × |
|---|---|---|---|
| What’s different | The e5-large-v2 embedding model maps English sentences, paragraphs, and documents into a 1024-dimensional dense vector space, delivering high-accuracy semantic embeddings optimized for retrieval, semantic… | EmbeddingGemma is a 300M parameter multilingual open embedding model from Google DeepMind, designed for efficient deployment even on low-resource devices, producing high-quality text vector representations… | 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… |
| Date | Nov 22, 2023Listed | Sep 16, 2025Listed | Nov 22, 2023Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | $0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | See provider quotes | $0.005 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ |
| Provider endpoints | 1 endpoint → | 0 endpoints → | 1 endpoint → |
Specifications | |||
| Context window | 512 | 2K | 512 |
| Max output | 460 | — | 460 |
| Parameters | — | 300M | — |
| Open weights | Supported | Not reported | Supported |