The all-mpnet-base-v2 embedding model encodes sentences and short paragraphs into a 768-dimensional dense vector space, providing high-fidelity semantic embeddings well suited for tasks like information retrieval, clustering, similarity scoring, and text ranking.
$0.0025
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 all-mpnet-base-v2 embedding model encodes sentences and short paragraphs into a 768-dimensional dense vector space, providing high-fidelity semantic embeddings well suited for tasks like information retrieval, clustering, similarity scoring, and text ranking.
On NagaAI, all-mpnet-base-v2 costs $0.0025 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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