Model reference · open weights

jina-reranker

jina-reranker is an open-weight embedding model from jinaai, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.

Licence fee required Embeddings jinaai 2 variants 806k downloads/mo
Request a licence + hosting quote All served models Not on the shared API today — deployed on request.

About

What jina-reranker is

jina-reranker-v3: Listwise Document Reranker for SOTA Multilingual Retrieval Blog | API | AWS | Azure | GCP | Arxiv | Successor: v3.5 [!IMPORTANT] Prefer jina-reranker-v3.5 for new projects — it is a drop-in upgrade with stronger domain / multilingual / structured rankings and faster listwise inference. Same API; switch the model id from jinaai/jina-reranker-v3 to jinaai/jina-reranker-v3.5. [!TIP] GGUF with quantizations and MLX versions are now available. jina-reranker-v3 is a 0.6B parameter multilingual document reranker with a novel last but not late interaction architecture. Unlike ColBERT's separate encoding with multi-vector matching, this model performs causal self-attention between query and documents within the same context window, extracting contextual embeddings from the last token of each document. Built on Qwen3-0.6B with 28 transformer layers and a lightweight MLP projector (1024→512→256), it processes up to 64 documents simultaneously within 131K token context. The model achieves state-of-the-art BEIR performance with 61.94 nDCG@10 while being 10× smaller than generative listwise rerankers. Usage Local Inference Use transformers for local inference: Installation: Load the model: Rank documents: API Reference: Returns: List of dicts with keys: - document: Original document text - relevancescore: Float score (higher = more relevant) - index: Position in input documents list - embedding: Document embedding (if returnembeddings=True) Example with options: API Use Jina AI's Reranker API for the fastest integration: Response format: Citation If you find jina-reranker-v3 useful in your research, please cite our technical report: License jina-reranker-v3 is listed on AWS & Azure. If you need to use it beyond those platforms or on-premises within your company, note that the model is licensed under CC BY-NC 4.0. For commercial usage inquiries, feel free to contact us.

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

Makerjinaai
TypeEmbedding models
Parameters (lead)597M
Context128k tokens
Variants2
Runs withtransformers
Based onQwen/Qwen3-0.6B
Released2025-09-18
Popularity806k downloads / month
Likes143
LicenceCommercial licence needed

How it works

How embedding models work

Your textsentence / documentEncodermaps meaningVectorlist of numbersAn embedding model turns text into a vector, so similar meanings sit close together — the basis of search and RAG.

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

VariantParamsPrecisionVRAMFits 16 GBWeights
jina-reranker-v3597MBF16~1.4 GBWeights ↗
jina-reranker-v3.5-GGUFGGUFWeights ↗

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys jina-reranker for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (jina-reranker below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"jina-reranker","input":"text to embed"}'

Details

Languages, data & research

Languages

multilingual

Tags

transformers safetensors qwen3 feature-extraction reranker text-ranking custom_code multilingual llama.cpp gguf llama-cpp conversational

Papers

Licence

Commercial licence needed

The weights are open but cc-by-nc-4.0 needs a commercial agreement for business use. AxForge can arrange that licence and host the model for you — you pay AxForge, we settle with the model’s maker. Ask us for a quote. Read the licence ↗

Sources

Weights & code

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