Side by side
See the difference.
Scores, capabilities and costs for the models you’re considering.
Scroll to compare models →
| 3 models | intfloatIntfloat: E5-Large-v2Remove × | intfloatmultilingual-e5-large-instructRemove × | 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… | The Multilingual-E5 models, initialized from XLM-RoBERTa, support up to 512 tokens per input — any longer text will be silently truncated. To ensure optimal performance, always prefix inputs with “query:”… | 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 | Apr 17, 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 | 512 | 512 |
| Max output | 460 | — | 460 |
| Parameters | — | — | — |
| Open weights | Supported | Not reported | Supported |