Jina Embeddings v5 Omni Nano
Paid production API
Type / status: embedding / documented_api
Use cases: RAG, Semantic search, Multimodal retrieval
Embedding dimensions (not output tokens): 768
Supported languages: multilingual
Deployment options: elastic, api, aws, huggingface, macos, airgapped
Documented datacenters: US
Model release date: 2026-05-07
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-embeddings-v5-omni-nano
Input / output: text, image, video, audio, pdf → embedding
Model developer: Jina AI · Service provider: Jina AI
Model family: — · Currency: USD
- Input: USD 0.02 / 1,000,000 tokens
Jina Embeddings v5 Omni Nano is a lightweight multilingual multimodal embedding model with 1.004B parameters built on the jina-embeddings-v5-text-nano text backbone, extended with a fine-tuned SigLIP2 Base vision encoder (from Qwen3.5-0.8B) for image and video and a Whisper-large-v3 audio encoder (from Qwen2.5-Omni-7B), connected via trained cross-modal projectors. It supports 8K context length and produces 768-dimensional embeddings with Matryoshka representation learning (down to 32 dimensions). It uses last-token pooling and supports retrieval, text-matching, clustering, and classification tasks across text, image, video, audio, and PDF inputs. 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