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    BAAI: bge-m3

    baai/bge-m3

    Created Nov 18, 20258,192 context
    $0.01/M input tokens$0/M output tokens

    The bge-m3 embedding model encodes sentences, paragraphs, and long documents into a 1024-dimensional dense vector space, delivering high-quality semantic embeddings optimized for multilingual retrieval, semantic search, and large-context applications.

    Providers for bge-m3

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

    Performance for bge-m3

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    Apps using bge-m3

    Top public apps this week using this model

    Recent activity on bge-m3

    Total usage per day on OpenRouter

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    Uptime stats for bge-m3

    Uptime stats for bge-m3 across all providers

    Sample code and API for bge-m3

    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.