Sentence Transformers: all-mpnet-base-v2

sentence-transformers/all-mpnet-base-v2

Released Nov 17, 2025512 context

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

The all-mpnet-base-v2 embedding model encodes sentences and short paragraphs into a 768-dimensional dense vector space, providing high-fidelity semantic embeddings well suited for tasks like information retrieval, clustering, similarity scoring, and text ranking.

Sentence Transformers: all-mpnet-base-v2

sentence-transformers/all-mpnet-base-v2

Released Nov 17, 2025512 context

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

Providers for all-mpnet-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 all-mpnet-base-v2

Compare different providers across OpenRouter

Effective Pricing for all-mpnet-base-v2

Actual cost per million tokens across providers over the past hour

Apps using all-mpnet-base-v2

Top public apps this month

Recent activity on all-mpnet-base-v2

Total usage per day on OpenRouter

Prompt

2.03M

Completion

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

Uptime stats for all-mpnet-base-v2

Uptime stats for all-mpnet-base-v2 across all providers

Sample code and API for all-mpnet-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.

all-mpnet-base-v2 - API Pricing & Providers | OpenRouter