Jina Reranker v3
Paid production API
Type / status: rerank / documented_api
Use cases: RAG, Document reranking
Supported languages: multilingual
Deployment options: elastic, api, huggingface, aws, azure, gcp, airgapped
Documented datacenters: US
Model release date: 2025-10-01
API endpoint: https://api.jina.ai/v1/rerank
Open-weights license (hosted API terms are separate): CC-BY-NC-4.0
Official catalog input-token price. Embeddings produce vectors and rerankers produce rankings, not billable generated output tokens. Zero catalog completion/image/request values do not make the service free. Reader API billing is separate from the model-catalog price.
The Reader page advertises 10 million free tokens for a new API key. Actual allocation and expiry are subject to the official account; this is a trial, not permanent free access.
Subject to hosted service terms; buying API tokens does not license model weights. Each model has its own weight license. CC-BY-NC-4.0 and Qwen Research License must not be described as unrestricted commercial self-hosting. Official FAQ states API requests, inputs and outputs are not used to train models. Jina legal page directs current data processing to Elastic's Data Processing Addendum. Model catalog lists US datacenters; Reader/Search also document EU endpoints. Do not infer identical regions across products.
Official model ID: jina-ai/jina-reranker-v3
Input / output: text → ranking
Model developer: Jina AI · Service provider: Jina AI
Model family: — · Currency: USD
| Price dimension | Current rate |
|---|---|
| Input | USD 0.05 |
| Cached read | Unverified |
jina-reranker-v3 is a 0.6B parameter multilingual document reranker introducing 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, enabling rich cross-document interactions before 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. Endpoint and model identifier are declared in the current OpenAPI schema; authenticated inference was not executed. Endpoint support is documented; authenticated inference was not tested. The output is ranking scores, not generated text tokens.
Documented API context: 134,144 tokens
Official source · Last verified: 2026-10-15T09:33:13.684489+00:00