Gemini Embedding 001

Gemini Embedding 001 belongs to Google's Gemini family of models and is offered specifically as an embedding model rather than a general text generator, meaning it is designed to transform input text into dense vector representations suitable for retrieval, clustering, and semantic similarity workloads. Its place within the Gemini lineup positions it as the dedicated embedding counterpart that developers can pair with larger Gemini generative models in retrieval-augmented pipelines, and a community discussion frames it as the natural replacement when Google deprecated the prior text-embedding-004 endpoint, signaling an intent to consolidate embedding tasks under the Gemini brand.

In practical terms, the model is documented as supporting a working context of roughly two thousand tokens, which makes it well suited for embedding individual passages, document chunks, or short queries rather than very long documents. Because the supplied evidence is limited to third-party gateway listings and user-forum reports, deeper architectural details such as embedding dimensionality, training corpus composition, or benchmark scores on suites like MTEB cannot be cited with confidence; the strongest verifiable characterizations are its embedding mode, its Gemini-family identity, and its role as a modern Google embedding option following the text-embedding-004 era.