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| 3 models | googleembeddinggemma-2Remove × | intfloatIntfloat: E5-Base-v2Remove × | voyageaiVoyageAI by MongoDB: voyage-4-liteRemove × |
|---|---|---|---|
| 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 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… | 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 | Oct 8, 2026Listed | Nov 22, 2023Listed | Jul 27, 2026Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | See provider quotes | $0.005 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | $0.02 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ |
| Provider endpoints | 0 endpoints → | 1 endpoint → | 1 endpoint → |
Specifications | |||
| Context window | 8.2K | 512 | 32K |
| Max output | — | 460 | 28.8K |
| Parameters | — | — | — |
| Open weights | Not reported | Supported | Not reported |