Sentence Transformers: multi-qa-mpnet-base-dot-v1

sentence-transformers/multi-qa-mpnet-base-dot-v1

Created Nov 18, 2025512 context

$0.005/M input tokens$0/M output tokens

The multi-qa-mpnet-base-dot-v1 embedding model transforms sentences and short paragraphs into a 768-dimensional dense vector space, generating high-quality semantic embeddings optimized for question-and-answer retrieval, semantic search, and similarity-scoring across diverse content.

Sentence Transformers: multi-qa-mpnet-base-dot-v1

sentence-transformers/multi-qa-mpnet-base-dot-v1

Created Nov 18, 2025512 context

$0.005/M input tokens$0/M output tokens

Providers for multi-qa-mpnet-base-dot-v1

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

Performance for multi-qa-mpnet-base-dot-v1

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Apps using multi-qa-mpnet-base-dot-v1

Top public apps this month

Recent activity on multi-qa-mpnet-base-dot-v1

Total usage per day on OpenRouter

Prompt

11K

Completion

Reasoning

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

Uptime stats for multi-qa-mpnet-base-dot-v1

Uptime stats for multi-qa-mpnet-base-dot-v1 across all providers

Sample code and API for multi-qa-mpnet-base-dot-v1

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.