Jina: Jina Embeddings V4

jina/jina-embeddings-v4

Jina Embeddings V4 is a 3.8 billion parameter multimodal embedding model that provides unified text and image representation capabilities. Built on the Qwen2.5-VL-3B-Instruct backbone, the model features an architecture that supports both single-vector and multi-vector embeddings in the late interaction style, addressing limitations found in traditional CLIP-style dual-encoder models. The model incorporates three specialized task-specific LoRA adapters (60M parameters each) that optimize performance across different retrieval scenarios including asymmetric query-document retrieval, semantic text similarity, and code search without modifying the frozen backbone weights. The model demonstrates strong performance in processing visually rich content such as tables, charts, diagrams, screenshots, and mixed-media formats through a unified processing pathway that reduces the modality gap present in conventional architectures. Supporting multilingual capabilities, the model can handle input texts up to 32,768 tokens with images resized to 20 megapixels, making it suitable for various document retrieval and cross-modal search applications across different languages and domains.

Model specifications

Input
text
Output
text
Context
400 tokens
Max output
10,000 tokens
Input price
$0.05 / 1M tokens
Output price
$0 / 1M tokens
Released
2026-02-14

Capabilities

  • Streaming
  • Playground

Provider pricing, discounts and data privacy

Compare effective provider prices, published discounts, regions, retention policies, training use, compliance, and privacy links by service tier.

Standard service tier

1 available provider · tier input average $0.05 / 1M tokens

Jina

Tier: Standard · Region: US

Pricing
Input
$0.05 / 1M tokens

No provider discount is currently published.

Data privacy and compliance
Region
US
Zero data retention
No
Data retention
Unknown retention
Used for training
Unknown
Data collection
Moderated
No
GDPR compliant
No
HIPAA compliant
No
SOC 2 certified
No
BYOK supported
No

Frequently asked questions

What is Jina: Jina Embeddings V4?
Jina Embeddings V4 is a 3.8 billion parameter multimodal embedding model that provides unified text and image representation capabilities. Built on the Qwen2.5-VL-3B-Instruct backbone, the model features an architecture that supports both single-vector and multi-vector embeddings in the late interaction style, addressing limitations found in traditional CLIP-style dual-encoder models. The model incorporates three specialized task-specific LoRA adapters (60M parameters each) that optimize performance across different retrieval scenarios including asymmetric query-document retrieval, semantic text similarity, and code search without modifying the frozen backbone weights. The model demonstrates strong performance in processing visually rich content such as tables, charts, diagrams, screenshots, and mixed-media formats through a unified processing pathway that reduces the modality gap present in conventional architectures. Supporting multilingual capabilities, the model can handle input texts up to 32,768 tokens with images resized to 20 megapixels, making it suitable for various document retrieval and cross-modal search applications across different languages and domains.
How much does Jina: Jina Embeddings V4 cost?
Input costs start at $0.05 / 1M tokens and output costs start at $0 / 1M tokens. Provider-level prices vary by service tier.
What is the context length of Jina: Jina Embeddings V4?
Jina: Jina Embeddings V4 supports a 400 token context window and up to 10,000 output tokens.
What capabilities does Jina: Jina Embeddings V4 support?
Jina: Jina Embeddings V4 supports Streaming, Playground.
Which providers offer Jina: Jina Embeddings V4?
Jina: Jina Embeddings V4 is available from Jina.
How do providers handle data privacy for Jina: Jina Embeddings V4?
1 of 1 providers report that customer data is not used for training, and 0 offer zero-data-retention routing. Retention, compliance, and privacy-policy links are listed per provider.

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