BGE Reranker Large
BGE Reranker Large has model metadata, but missing tracked provider pricing keeps it from being a default production pick.
Use it for
- Teams evaluating general LLM work
- Workloads that can use a 512 context window
Do not use it for
- Cost-sensitive launches that need sourced token pricing
- Vision or document-understanding workloads
- Strict JSON or tool-calling flows
No tracked provider token pricing is available yet.
About
BGE Reranker Large is a cross-encoder reranking model that scores query-passage pairs for relevance by jointly encoding them through full attention. Based on XLM-RoBERTa, it supports Chinese and English and achieves a C-MTEB reranking average of 66.09. It is designed as a second-stage ranker in retrieval pipelines: first retrieve candidates with a BGE embedding model, then re-rank with this model for higher precision.
BGE Reranker Large is an open-source model in the BGE family. The structured metadata tracks a 512-token context window. No headline benchmark score is tracked for BGE Reranker Large yet.
Top use-case fit
No primary decision-task fit is mapped for this model yet.
Provider price ladder
No tracked provider token pricing is available for this model yet.
Capabilities
No model capability flags are currently sourced.
Benchmark peer barsfor Coding
No task-mapped benchmark peers are available for this model yet.
Migration checks
No linked migration route is available for this model yet.
No tracked provider token pricing is available yet.