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    intfloat: e5-base-v2

    intfloat/e5-base-v2

    Created Nov 18, 2025512 context
    $0.005/M input tokens$0/M output tokens

    The e5-base-v2 embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as semantic search, similarity scoring, retrieval and clustering.

    Providers for e5-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 e5-base-v2

    Compare different providers across OpenRouter

    Apps using e5-base-v2

    Top public apps this week using this model

    Recent activity on e5-base-v2

    Total usage per day on OpenRouter

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    Uptime stats for e5-base-v2

    Uptime stats for e5-base-v2 across all providers

    Sample code and API for e5-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.