Side by side
See the difference.
Scores, capabilities and costs for the models you’re considering.
Scroll to compare models →
| 3 models | intfloatIntfloat: Multilingual-E5-LargeRemove × | intfloatIntfloat: E5-Base-v2Remove × | nvidiaNemotron-3-Embed-1B-BF16Remove × |
|---|---|---|---|
| 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… | Nemotron-3-Embed-1B-BF16 is a compact multilingual text embedding model from NVIDIA, pruned and distilled from Ministral-3 to ~1B parameters, that maps text into 2048-dimensional dense vectors for retrieval… |
| Date | Jun 4, 2024Listed | Nov 22, 2023Listed | Jul 15, 2026Listed |
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 ↗ | See provider quotes |
| Provider endpoints | 1 endpoint → | 1 endpoint → | 0 endpoints → |
Benchmarks Each result retains its evaluation configuration | |||
| FreshStack · MTEB 2.14.3 ↗MTEB 2.14.3; test/default; dataset 7a20df1a | Not reported | Not reported | 0.4173 ↗Revision f8801746; test/defaultContributor reported |
Specifications | |||
| Context window | 512 | 512 | 32.8K |
| Max output | 460 | 460 | — |
| Parameters | — | — | 1B |
| Open weights | Supported | Supported | Not reported |