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| 3 models | intfloatIntfloat: Multilingual-E5-LargeRemove × | intfloatIntfloat: E5-Base-v2Remove × | googleGoogle: Gemini Embedding 001Remove × |
|---|---|---|---|
| What’s different | The multilingual-e5-large embedding model encodes sentences, paragraphs, and documents across over 90 languages into a 1024-dimensional dense vector space, delivering robust semantic embeddings optimized… | 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-001 provides a unified cutting edge experience across domains, including science, legal, finance, and coding. This embedding model has consistently held a top spot on the Massive Text… |
| Date | Jun 4, 2024Listed | Nov 22, 2023Listed | Oct 31, 2025Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | $0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | $0.005 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | $0.15 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ |
| Provider endpoints | 1 endpoint → | 1 endpoint → | 1 endpoint → |
Specifications | |||
| Context window | 512 | 512 | 20K |
| Max output | 460 | 460 | 18K |
| Parameters | — | — | — |
| Open weights | Supported | Supported | Not reported |