Sentence Transformers: paraphrase-MiniLM-L6-v2

sentence-transformers/paraphrase-minilm-l6-v2

Created Nov 18, 2025512 context

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

The paraphrase-MiniLM-L6-v2 embedding model converts sentences and short paragraphs into a 384-dimensional dense vector space, producing high-quality semantic embeddings optimized for paraphrase detection, semantic similarity scoring, clustering, and lightweight retrieval tasks.

Sentence Transformers: paraphrase-MiniLM-L6-v2

sentence-transformers/paraphrase-minilm-l6-v2

Created Nov 18, 2025512 context

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

Providers for paraphrase-MiniLM-L6-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 paraphrase-MiniLM-L6-v2

Compare different providers across OpenRouter

Apps using paraphrase-MiniLM-L6-v2

Top public apps this month

Recent activity on paraphrase-MiniLM-L6-v2

Total usage per day on OpenRouter

Prompt

207K

Completion

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

Uptime stats for paraphrase-MiniLM-L6-v2

Uptime stats for paraphrase-MiniLM-L6-v2 across all providers

Sample code and API for paraphrase-MiniLM-L6-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.

paraphrase-MiniLM-L6-v2 - API, Providers, Stats | OpenRouter