Curiosity · AI Model
Jina Reranker v2
Jina Reranker v2 is a multilingual cross-encoder from Jina AI, published with open weights. It reorders candidate passages for higher top-k precision, supports 100+ languages, and adds code-aware reranking that handles programming-language queries more gracefully than generic rerankers.
Model specs
- Vendor
- Jina AI
- Family
- Jina Reranker
- Released
- 2024-06
- Context window
- 1,024 tokens
- Modalities
- text
- Input price
- $0.02/M tok
- Output price
- n/a
- Pricing as of
- 2026-04-20
Strengths
- Open weights — runs on a single GPU for self-hosted stacks
- Fast — designed to rerank 100+ candidates in milliseconds
- Code-aware training beats generic rerankers on code corpora
- Multilingual — 100+ languages
Limitations
- 1024-token chunks — longer passages need truncation
- English retrieval quality slightly trails Cohere Rerank 3
- Self-hosted deployment adds ops complexity
Use cases
- Self-hosted RAG reranking
- Multilingual e-commerce and knowledge search
- Code retrieval over developer documentation
- Hybrid retrieval paired with Jina Embeddings v3
Benchmarks
| Benchmark | Score | As of |
|---|---|---|
| BEIR NDCG@10 uplift vs BM25 | +10 | 2024 |
| CodeSearchNet NDCG@10 | ≈70 | 2024 |
Frequently asked questions
What is Jina Reranker v2?
Jina Reranker v2 is an open-weight multilingual cross-encoder reranking model from Jina AI, designed to reorder candidate passages in a retrieval pipeline for higher top-k precision.
How does it compare with Cohere Rerank 3?
Both are strong multilingual cross-encoders. Cohere Rerank 3 is a closed hosted API with excellent English quality; Jina Reranker v2 is open-weight, self-hostable, and adds code-aware reranking. Pick Jina when self-hosting or code retrieval matters; pick Cohere for a managed API.
Can I self-host Jina Reranker v2?
Yes — the weights are on Hugging Face and the model runs on a single GPU (or CPU for lower throughput) using transformers or the sentence-transformers CrossEncoder class.
What is code-aware reranking?
Code-aware rerankers are trained on programming-language queries and source code, so they handle camelCase, snake_case, and syntactic patterns better than text-only rerankers. This lifts NDCG on developer-doc search.
Sources
- Jina AI — Reranker v2 announcement — accessed 2026-04-20
- Hugging Face — jinaai/jina-reranker-v2-base-multilingual — accessed 2026-04-20