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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.

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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.