Jina Colbert v2
Paid production API
Type / status: embedding / documented_api
Use cases: RAG, Semantic search
Embedding dimensions (not output tokens): 128
Supported languages: multilingual
Deployment options: api, license, huggingface, aws, azure, gcp, airgapped
Documented datacenters: US
Model release date: 2024-08-31
API endpoint: https://api.jina.ai/v1/embeddings
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-colbert-v2
Input / output: text → multi-vector
Model developer: Jina AI · Service provider: Jina AI
Model family: — · Currency: USD
- Input: USD 0.05 / 1,000,000 tokens
Jina-ColBERT-v2 is a groundbreaking multilingual information retrieval model that solves the critical challenge of efficient, high-quality search across multiple languages. As the first multilingual ColBERT-like model to generate compact embeddings, it addresses the growing need for scalable, cost-effective multilingual search solutions in global applications. Organizations dealing with multilingual content, from e-commerce platforms to content management systems, can leverage this model to provide accurate search results across 89 languages while significantly reducing storage and computational costs through its innovative dimension reduction capabilities. 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. Embedding dimensions are vector size, not generated tokens.
Documented API context: 8,192 tokens
Official source · Last verified: 2026-10-15T09:33:13.684489+00:00