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| 3 models | intfloatIntfloat: E5-Base-v2Remove × | nvidiaNemotron-3-Embed-1B-BF16Remove × | voyageaiVoyageAI by MongoDB: voyage-multimodal-3.5Remove × |
|---|---|---|---|
| What’s different | 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… | voyage-multimodal-3.5 is a state-of-the-art multimodal embedding model capable of vectorizing not only text, images, and video individually, but also content that interleaves all three modalities. It… |
| Date | Nov 22, 2023Listed | Jul 15, 2026Listed | Jul 27, 2026Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | $0.005 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | See provider quotes | $0.12 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ |
| Provider endpoints | 1 endpoint → | 0 endpoints → | 1 endpoint → |
Benchmarks Each result retains its evaluation configuration | |||
| FreshStack · MTEB 2.14.3 ↗MTEB 2.14.3; test/default; dataset 7a20df1a | Not reported | 0.4173 ↗Revision f8801746; test/defaultContributor reported | Not reported |
Specifications | |||
| Context window | 512 | 32.8K | 32K |
| Max output | 460 | — | 28.8K |
| Parameters | — | 1B | — |
| Image input | Not reported | Not reported | Supported |
| Open weights | Supported | Not reported | Not reported |