The gte-large embedding model converts English sentences, paragraphs and moderate-length documents into a 1024-dimensional dense vector space, delivering high-quality semantic embeddings optimized for information retrieval, semantic textual similarity, reranking and clustering tasks. Trained via multi-stage contrastive learning on a large domain-diverse relevance corpus, it offers excellent performance across general-purpose embedding use-cases.
$0.005
Share of requests served without a server error. Client errors (4xx) do not count.
No optional parameters beyond the standard request.
Works with the OpenAI SDK pointed at our base URL. Every parameter is in the API reference.
Tokens embedded per day on NagaAI.
The gte-large embedding model converts English sentences, paragraphs and moderate-length documents into a 1024-dimensional dense vector space, delivering high-quality semantic embeddings optimized for information retrieval, semantic textual similarity, reranking and clustering tasks.
On NagaAI, GTE-Large costs $0.005 per 1M tokens. You pay only for what you use, with no subscription.
It takes text as input and returns embeddings.
Create an API key, copy the model ID from the top of this page and send requests with the OpenAI SDK pointed at NagaAI. The API section on this page has a ready example.
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