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    Sentence Transformers: all-mpnet-base-v2

    sentence-transformers/all-mpnet-base-v2

    Created Nov 17, 2025512 context
    $0.005/M input tokens$0/M output tokens

    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.

    Providers for all-mpnet-base-v2

    OpenRouter routes requests to the best providers that are able to handle your prompt size and parameters, with fallbacks to maximize uptime.

    Performance for all-mpnet-base-v2

    Compare different providers across OpenRouter

    Apps using all-mpnet-base-v2

    Top public apps this week using this model

    Recent activity on all-mpnet-base-v2

    Total usage per day on OpenRouter

    Uptime stats for all-mpnet-base-v2

    Uptime stats for all-mpnet-base-v2 across all providers

    Sample code and API for all-mpnet-base-v2

    OpenRouter normalizes requests and responses across providers for you.

    OpenRouter provides an OpenAI-compatible embeddings API that you can call directly, or using the OpenAI SDK.

    In the examples below, the OpenRouter-specific headers are optional. Setting them allows your app to appear on the OpenRouter leaderboards.

    Using third-party SDKs

    For information about using third-party SDKs and frameworks with OpenRouter, please see our frameworks documentation.

    See the Request docs for all possible fields, and Parameters for explanations of specific sampling parameters.