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| 3 models | BAAIbge-m3Remove × | googleembeddinggemma-2Remove × | voyageaiVoyageAI by MongoDB: voyage-4-liteRemove × |
|---|---|---|---|
| What’s different | BGE-M3 is a versatile text embedding model that supports multi-functionality, multi-linguality, and multi-granularity, allowing it to perform dense retrieval, multi-vector retrieval, and sparse retrieval in… | 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,… | voyage-4-lite is a lightweight, general-purpose embedding model optimized for low latency and cost. Enabled by Matryoshka learning and quantization-aware training, voyage-4-lite supports embeddings in 2048,… |
| Date | May 27, 2024Listed | Oct 8, 2026Listed | Jul 27, 2026Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | $0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | See provider quotes | $0.02 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ |
| Provider endpoints | 1 endpoint → | 0 endpoints → | 1 endpoint → |
Specifications | |||
| Context window | 8.2K | 8.2K | 32K |
| Max output | 7.4K | — | 28.8K |
| Parameters | — | — | — |
| Open weights | Supported | Not reported | Not reported |