Jina Reranker M0
Paid production API
Type / status: rerank / documented_api
Use cases: RAG, Document reranking
Supported languages: multilingual
Deployment options: api, license, huggingface, aws, azure, gcp, airgapped
Documented datacenters: US
Model release date: 2025-04-08
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-m0
Input / output: text, image → 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-m0 is a groundbreaking multimodal multilingual reranker model designed to rank visual documents across multiple languages. What makes this model exceptional is its ability to process queries alongside visually rich document images—including pages with text, figures, tables, and various layouts—across 29 languages. The model outputs a ranked list of documents ordered by their relevance to the input query. Unlike previous rerankers that struggled with the "modality gap" problem (where images clustered near other images while text clustered near text), jina-reranker-m0 unifies textual and visual modalities in a single decoder-only model, creating a seamless multimodal search experience that can rank both images and text documents together effectively. 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: 10,240 tokens
Official source · Last verified: 2026-10-15T09:33:13.684489+00:00