gte-rerank-v2 · 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.
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.
On AIHubMix, Gte Rerank V2 costs $0.11 per million input tokens and $0.11 per million output tokens.
Gte Rerank V2 accepts text and image input.
Gte Rerank V2 is available through the AIHubMix unified API. The API is OpenAI-compatible: point your OpenAI SDK at https://aihubmix.com/v1, use your AIHubMix API key, and set the model name to gte-rerank-v2 — no other code changes needed.
Gte Rerank V2 is developed by Qwen. AIHubMix aggregates it alongside models from other providers behind one API and one bill.
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Use Gte Rerank V2 via the AIHubMix unified API — one interface for every major LLM.