Sentence Transformers: all-MiniLM-L6-v2

sentence-transformers/all-minilm-l6-v2

Created Nov 17, 2025512 context

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

The all-MiniLM-L6-v2 embedding model maps sentences and short paragraphs into a 384-dimensional dense vector space, enabling high-quality semantic representations that are ideal for downstream tasks such as information retrieval, clustering, similarity scoring, and text ranking.

Sentence Transformers: all-MiniLM-L6-v2

sentence-transformers/all-minilm-l6-v2

Created Nov 17, 2025512 context

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

Providers for all-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 all-MiniLM-L6-v2

Compare different providers across OpenRouter

Apps using all-MiniLM-L6-v2

Top public apps this month

Recent activity on all-MiniLM-L6-v2

Total usage per day on OpenRouter

Prompt

49.3M

Completion

Reasoning

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

Uptime stats for all-MiniLM-L6-v2

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

Sample code and API for all-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.