The bge-m3 embedding model encodes sentences, paragraphs, and long documents into a 1024-dimensional dense vector space, delivering high-quality semantic embeddings optimized for multilingual retrieval, semantic search, and large-context applications.
BAAI: bge-m3 – Recent Activity and Usage Stats | OpenRouter
Recent activity on bge-m3
Total usage per day on OpenRouter
Prompt
248M
Completion
0
Prompt tokens measure input size. Reasoning tokens show internal thinking before a response. Completion tokens reflect total output length.