Gemini Embedding 2
#1 in Embeddings & Vektor-DBsgoogle · v2 · siet Public Preview: 10. März 2026; GA: 22. April 2026 · 24× · tolest 30. Juni 2026
100
Momentum
Gemini Embedding 2 is Google's first natively multimodal embedding model, mapping text, images, video, audio, and PDFs into a single unified vector space. Built on the Gemini architecture, it generates 3,072-dimensional float vectors by default, using Matryoshka Representation Learning (MRL) for flexible output dimensions. It supports over 100 languages and is available as a cloud API via the Gemini API and Vertex AI. It launched as Public Preview on March 10, 2026, and reached General Availability (GA) on April 22, 2026.
Momentum-Verloop
04.04.03.07.
Features
| Deployment (Self-Hosted/Cloud) | Cloud API only (no self-hosting / on-premises). Available via Gemini API and Vertex AI. |
| Throughput/Latency | No officially published latency/throughput figures. Per user report: up to 70% latency reduction vs. multi-model pipelines (customer quote, not an official benchmark). |
| License | Proprietary – Gemini API Additional Terms of Service (Google). Free tier: data used for product improvement; Paid tier: no use for training. |
| Platform | Gemini API (ai.google.dev) & Vertex AI / Gemini Enterprise Agent Platform (Google Cloud). Ecosystem integrations: LangChain, LlamaIndex, Haystack, Weaviate, Qdrant, ChromaDB, Vector Search. |
| Price | Text: $0.20/1M tokens; Image: $0.45/1M tokens; Audio: $6.50/1M tokens; Video: $12.00/1M tokens. Free tier available (rate-limited). Batch API: 50% discount. |
| Protocol Compatibility | REST API (Gemini API embedding endpoint). OpenAI compatibility library supported (Batch API). SDKs: Google Gen AI SDK (Python, Node.js, Java, and others). |
| Release Date | Public preview: March 10, 2026 | GA: April 22, 2026 |
| Supported Models/Providers | Model ID: gemini-embedding-2 (GA) / gemini-embedding-2-preview. Input: text (up to 8,192 tokens), up to 6 images (PNG/JPEG), video up to 120s (MP4/MOV), audio up to 180s (native), PDFs up to 6 pages. Output: 3,072-dim vectors (MRL: scalable to 1,536, 768, 128). |