Jina Code Embeddings 1.5b
Paid production API
Type / status: embedding / documented_api
Use cases: Code search, RAG
Embedding dimensions (not output tokens): 1536
Supported languages: multilingual
Deployment options: api, huggingface, aws, azure, gcp, airgapped
Documented datacenters: US
Model release date: 2025-09-01
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-code-embeddings-1.5b
Input / output: text → embedding
Model developer: Jina AI · Service provider: Jina AI
Model family: — · Currency: USD
- Input: USD 0.05 / 1,000,000 tokens
jina-code-embeddings-1.5b is a 1.54 billion parameter model representing a significant advancement in code retrieval capabilities. Built on Qwen2.5-Coder-1.5B backbone with last-token pooling, it moves beyond traditional training on limited aligned data to leverage vast unaligned code and documentation corpora. The model implements comprehensive task-specific instructions across five categories: NL2Code, TechQA, Code2Code, Code2NL, and Code2Completion, each with distinct prefixes for queries and documents. Supports Matryoshka representation learning for flexible embedding truncation. Despite larger size, maintains practical deployment characteristics while achieving benchmark performance competitive with substantially larger alternatives. 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: 32,768 tokens
Official source · Last verified: 2026-10-15T09:33:13.684489+00:00