Side by side
See the difference.
Scores, capabilities and costs for the models you’re considering.
Scroll to compare models →
| 3 models | perplexityPerplexity: Embed V1 0.6BRemove × | intfloatIntfloat: Multilingual-E5-LargeRemove × | BAAIbge-m3-multi-8kRemove × |
|---|---|---|---|
| What’s different | pplx-embed-v1-0.6B is one of Perplexity's state-of-the-art text embedding models built for real-world, web-scale retrieval. pplx-embed-v1 is optimized for standard dense text retrieval with the 0.6B… | 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… | BGE-M3 is a multilingual text embedding model developed by BAAI, distinguished by its Multi-Linguality (supporting 100+ languages), Multi-Functionality (unified dense, multi-vector, and sparse retrieval),… |
| Date | Mar 16, 2026Listed | Jun 4, 2024Listed | Sep 3, 2026Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | $0.004 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | $0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | See provider quotes |
| Provider endpoints | 1 endpoint → | 1 endpoint → | 0 endpoints → |
Specifications | |||
| Context window | 32K | 512 | 8.2K |
| Max output | 28.8K | 460 | — |
| Parameters | 0.6B | — | — |
| Open weights | Not reported | Supported | Not reported |