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.
Thenlper: GTE-Large – Recent Activity and Usage Stats | OpenRouter
Recent activity on GTE-Large
Total usage per day on OpenRouter
Prompt
7.95M
Completion
0
Prompt tokens measure input size. Reasoning tokens show internal thinking before a response. Completion tokens reflect total output length.