Sentence Transformers: all-MiniLM-L6-v2

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.

• Context window: 8,192 tokens
• Input: text
• Output: embeddings
• Pricing: $0.005/M input tokens

View on OpenRouter. Model data sourced from OpenRouter.