bge-reranker-large

Large BGE reranker for rescoring retrieved passages after vector search in search and RAG pipelines.

BAAI · BGE

Editorial review

Reviewed byOpenSourcesAI EditorialLast updatedAugust 2026SourcesHugging Face model card (BAAI/bge-reranker-large)

Model checkpoints, context windows, provider support, local runtime compatibility, and license terms can change quickly. Verify the exact model card before production or commercial use.

Best for

Teams that already retrieve a candidate set and want a stronger final ranking step before passing context to an LLM.

Who should use it

Teams that already retrieve a candidate set and want a stronger final ranking step before passing context to an LLM.
Builders who want local or self-hosted testing options.
RAG builders comparing retrieval quality, latency, and multilingual coverage.

Common workflows

RAG reranking workflows
reranking workflows
rag workflows
retrieval workflows
semantic search workflows

Deployment and hardware notes

Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.

License and usage notes

MIT. Open weights. Verify the exact model card and license terms for the checkpoint or hosted provider you use.

Strengths

Open weights model option for BGE workflows.
Teams that already retrieve a candidate set and want a stronger final ranking step before passing context to an LLM.
Use the exact checkpoint and quantization that matches your hardware and latency target.

Limitations

Verify license, deployment requirements, runtime support, and fit on your own workload before production use.
Local hardware needs vary by size, quantization, and runtime. Check the exact model card and serving stack.
Context window and limits: 512 tokens.
Verify the exact model card, provider docs, license, and serving support before production use.