Side by side
See the difference.
Scores, capabilities and costs for the models you’re considering.
Scroll to compare models →
| 3 models | googleembeddinggemma-300mRemove × | intfloatIntfloat: Multilingual-E5-LargeRemove × | nvidiaNemotron-3-Embed-8BRemove × |
|---|---|---|---|
| What’s different | 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 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… | Nemotron-3-Embed-8B is a multilingual text embedding model from NVIDIA, based on Ministral-3-8B, that maps text into 4096-dimensional dense vectors for retrieval and semantic similarity. Spanning 34… |
| Date | Sep 16, 2025Listed | Jun 4, 2024Listed | Jul 16, 2026Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | See provider quotes | $0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | See provider quotes |
| Provider endpoints | 0 endpoints → | 1 endpoint → | 0 endpoints → |
Specifications | |||
| Context window | 2K | 512 | 32.8K |
| Max output | — | 460 | — |
| Parameters | 300M | — | 8B |
| Open weights | Not reported | Supported | Not reported |