Google: Gemini Embedding 001

Google: Gemini Embedding 001 is an embedding model from Google, listed on api14 under the model id google/gemini-embedding-001. It sits in the embeddings category, and Google released it on 31 October 2025. Its purpose is to turn text into vector representations that you can store, compare and search over, rather than to generate chat responses.

The model takes text as its only input and returns embeddings as its only output. It supports a context length of 20,000 tokens, with a maximum output of 18,000. Google describes it as providing a unified experience across domains, including science, legal, finance and coding, and notes that it has consistently held a top spot on the Massive Text Embedding Benchmark. There is no chat style generation here, no image or audio input, and no tool calling, so treat it purely as a representation model.

Those capabilities point to a familiar set of use cases. Semantic search and retrieval augmented generation are the obvious ones, where you embed documents and queries and rank by similarity. Because Google calls out science, legal, finance and coding as domains it handles well, it is also a reasonable fit for building search or clustering over technical papers, contracts, filings and source code. The 20,000 token context means you can embed fairly long passages in a single request instead of splitting everything into small chunks, which helps when a document section only makes sense as a whole. Classification, deduplication, topic clustering and recommendation style similarity matching all follow from the same vector output.

You call it through api14's OpenAI-compatible API using the model id google/gemini-embedding-001, so existing OpenAI client libraries and request shapes work without a rewrite. The model reports no supported parameters, so do not expect to pass options such as temperature, top_p or similar tuning fields and have them take effect. Send your text input and read the embedding vector from the response.

On limits, the main ones are the text only input and the 20,000 token context, so audio, image and video inputs are out of scope. If your task is generating or editing text rather than representing it, a chat or completion model is the better choice, and if you need multimodal embeddings you will have to look elsewhere. For text similarity, search and clustering work within the context limit, this model is the direct fit.