Side by side
See the difference.
Scores, capabilities and costs for the models you’re considering.
Scroll to compare models →
| 3 models | googleembeddinggemma-2Remove × | intfloatIntfloat: E5-Base-v2Remove × | googleGoogle: Gemini Embedding 2Remove × |
|---|---|---|---|
| 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… | Gemini Embedding 2 is Google's first multimodal embedding model. We currently support mapping text and images into a unified vector space for semantic search and retrieval-augmented generation (RAG). It… |
| Date | Oct 8, 2026Listed | Nov 22, 2023Listed | May 20, 2026Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | See provider quotes | $0.005 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | $0.20 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ |
| Provider endpoints | 0 endpoints → | 1 endpoint → | 1 endpoint → |
Specifications | |||
| Context window | 8.2K | 512 | 8.2K |
| Max output | — | 460 | 7.4K |
| Parameters | — | — | — |
| Image input | Not reported | Not reported | Supported |
| Audio input | Not reported | Not reported | Supported |
| Video input | Not reported | Not reported | Supported |
| Open weights | Not reported | Supported | Not reported |