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| 3 models | intfloatIntfloat: E5-Large-v2Remove × | nvidiaNemotron-3-Embed-8BRemove × | voyageaiVoyageAI by MongoDB: voyage-code-4Remove × |
|---|---|---|---|
| What’s different | The e5-large-v2 embedding model maps English sentences, paragraphs, and documents into a 1024-dimensional dense vector space, delivering high-accuracy semantic embeddings optimized for retrieval, semantic… | 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… | voyage-code-4 is a code embedding model from Voyage AI, a MongoDB company. It is designed for coding agents and code retrieval, with Matryoshka embeddings at 2048, 1024, 512, and 256... |
| Date | Nov 22, 2023Listed | Jul 16, 2026Listed | Aug 13, 2026Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | $0.01 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 → |
Specifications | |||
| Context window | 512 | 32.8K | 32K |
| Max output | 460 | — | 28.8K |
| Parameters | — | 8B | — |
| Open weights | Supported | Not reported | Not reported |