Mixedbread
Models

mxbai-rerank-large-v2

Parameters 1500M
Context Window 8K / 32K
Price / 1M tokens $0.15
Languages Multilingual

Model Description

is the flagship model of Mixedbread's second-generation rerank family, a set of state-of-the-art reranking models that are fully open-source under the Apache 2.0 license. This powerful 1.5B-parameter model delivers best-in-class accuracy and robust multilingual capabilities while maintaining impressive processing speed.

The v2 models represent a significant advancement over the first generation, leveraging a sophisticated three-step reinforcement learning process:

  1. GRPO (Guided Reinforcement Prompt Optimization) - Teaching the model to output clear relevance scores
  2. Contrastive Learning - Developing fine-grained understanding of query-document relationships
  3. Preference Learning - Tuning the model to prioritize the most relevant documents

On benchmarks, mxbai-rerank-large-v2 achieves exceptional results with an NDCG@10 score of 57.49 on BEIR average, 29.79 on Mr.TyDi multilingual datasets Despite its larger parameter count, it processes queries with impressive efficiency (0.89s per query on NFC dataset with A100 GPU), up to 8x faster than comparable models.

When used in combination with a keyword-based search engine such as Elasticsearch, OpenSearch, or Solr, the rerank model can be added to the end of an existing search workflow. This allows users to incorporate semantic relevance into their keyword-based search system without changing the existing infrastructure - an easy, low-complexity method of improving search results with just one line of code.

Compare with other models

ModelContext WindowPrice / 1M tokens
mxbai rerank large v28K / 32K$0.15
mxbai rerank base v28K / 32K$0.15
mxbai rerank large v1512 $0.15
mxbai rerank base v1512 $0.15
mxbai rerank xsmall v1512 $0.15

Example

Reranking Models
The Mixedbread rerank family is a collection of state-of-the-art, open-source reranking models designed to significantly enhance search accuracy across various domains. These models can be seamlessly integrated into existing search systems, offering best-in-class performance and easy implementation for improved user satisfaction in search results.
mxbai-rerank-base-v2
mxbai-rerank-base-v2 is a state-of-the-art open-source reranking model from the Mixedbread rerank family, offering an excellent balance between size and performance. This reinforcement-learning enhanced model excels at boosting search results across 100+ languages, handling long contexts, and supporting various use cases from code search to function-call reranking.

Last updated: June 26, 2025