Side by side
See the difference.
Scores, capabilities and costs for the models you’re considering.
Scroll to compare models →
| 3 models | googleembeddinggemma-2Remove × | intfloatmultilingual-e5-large-instructRemove × | thenlpergte-largeRemove × |
|---|---|---|---|
| What’s different | EmbeddingGemma 2 is a 740M parameter multimodal embedding model from Google DeepMind that maps text (incl. code), images, video and audio, alone or interleaved, into a single 768-dimensional vector space,… | 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 GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE… |
| Date | Oct 8, 2026Listed | Apr 17, 2025Listed | Nov 22, 2023Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | See provider quotes | See provider quotes | $0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ |
| Provider endpoints | 0 endpoints → | 0 endpoints → | 1 endpoint → |
Specifications | |||
| Context window | 8.2K | 512 | 512 |
| Max output | — | — | 460 |
| Parameters | — | — | — |
| Open weights | Not reported | Not reported | Supported |