by Qwen
gte-rerank-v2 is a multilingual unified text ranking model developed by Tongyi Lab, covering multiple major languages worldwide and providing high-quality text ranking services. It is typically used in scenarios such as semantic retrieval and RAG, and can simply and effectively improve text retrieval performance. Given a query and a set of candidate texts (documents), the model ranks the candidates from highest to lowest based on their semantic relevance to the query.
| Input | $0.11 / 1M tokens |
| Output | $0.11 / 1M tokens |
| Modalities | text, image |
Use gte-rerank-v2 via the AIHubMix unified API — one interface for every major LLM.