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BAAI: bge-large-en-v1.5

baai/bge-large-en-v1.5

Created Nov 18, 2025512 context
$0.01/M input tokens$0/M output tokens

The bge-large-en-v1.5 embedding model maps English sentences, paragraphs, and documents into a 1024-dimensional dense vector space, delivering high-fidelity semantic embeddings optimized for semantic search, document retrieval, and downstream NLP tasks in English.

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Providers for bge-large-en-v1.5

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-large-en-v1.5

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Apps using bge-large-en-v1.5

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Recent activity on bge-large-en-v1.5

Total usage per day on OpenRouter

Prompt
192K
Completion
0
Reasoning
0

Prompt tokens measure input size. Reasoning tokens show internal thinking before a response. Completion tokens reflect total output length.

Uptime stats for bge-large-en-v1.5

Uptime stats for bge-large-en-v1.5 across all providers

Sample code and API for bge-large-en-v1.5

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.