Thenlper: GTE-Large

The gte-large embedding model converts English sentences, paragraphs and moderate-length documents into a 1024-dimensional dense vector space, delivering high-quality semantic embeddings optimized for information retrieval, semantic textual similarity, reranking and clustering tasks. Trained via multi-stage contrastive learning on a large domain-diverse relevance corpus, it offers excellent performance across general-purpose embedding use-cases.

• Context window: 8,192 tokens
• Input: text
• Output: embeddings
• Pricing: $0.01/M input tokens

View on OpenRouter. Model data sourced from OpenRouter.