LLM Reference

BGE Reranker Large

Released
2023-09-12
Last refreshed
2026-04-28
Status
Researched 134d ago
Open sourceCommercial use: permitted

BGE Reranker Large is a released general LLM work model with open-source; evaluate it while provider pricing coverage matures.

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
Specifications
Family
BGE
Released
2023-09-12
Context
512
Parameters
560M
Architecture
Encoder Only
Specialization
ranking
Openness
Open source
License
MITOSI-approvedCommercial use: permitted
Weights
Available
Code
Unknown
Training
Pretrained
Created by

Open-source AI fostering global collaboration

Beijing, China
Founded 2018
Website
Pricing

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.

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.