Jina Reranker v1 Tiny EN

rerankapache-2.0

Jina Reranker v1 Tiny English represents a breakthrough in efficient search refinement, designed specifically for organizations requiring high-performance reranking in resource-constrained environments. This model addresses the critical challenge of maintaining search quality while significantly reducing computational overhead and deployment costs. With just 33M parameters - a fraction of typical reranker sizes - it delivers remarkably competitive performance through innovative knowledge distillation techniques. The model's most surprising feature is its ability to process documents nearly five times faster than base models while maintaining over 92% of their accuracy, making enterprise-grade search refinement accessible to applications where computational resources are at a premium.

Context
8K
Input price
$0.05/1M tokens
Output price
—

Capabilities

Infrastructure
Fine-tuning

Details

Model IDjina-reranker-v1-tiny-en
Provider Jina AI
Creator jina
Licenseapache-2.0
Statusactive
Typererank
Input modalitiestext
Output modalitiestext
Open weightYes
Parameters—
Architecture—
Context window8K tokens
Max input—
Max output—
Reasoning tokens—
Knowledge cutoff—
Release date
Sourceofficial

Endpoints

rerankPOST
https://api.jina.ai/rerank

Pricing

Input
$0.05
Output
—
Cache write
—
Cache read
—
Batch in
—
Batch out
—

Family Comparison: jina-reranker

ModelContextMax outPricing
Jina Reranker v1 Base EN8K—$0.05—
Jina Reranker v1 Tiny EN8K—$0.05—
Jina Reranker v1 Turbo EN8K—$0.05—
Jina Reranker v2 Base Multilingual1K—$0.05—
Jina Reranker v3.5131K—$0.05—
Jina Reranker v3134K—$0.05—

API

GET/v1/models/jina/jina-reranker-v1-tiny-en

Jina Reranker v1 Tiny EN

jina-reranker-v1-tiny-en

Jina Reranker v1 Tiny English represents a breakthrough in efficient search refinement, designed specifically for organizations requiring high-performance reranking in resource-constrained environments. This model addresses the critical challenge of maintaining search quality while significantly reducing computational overhead and deployment costs. With just 33M parameters - a fraction of typical reranker sizes - it delivers remarkably competitive performance through innovative knowledge distillation techniques. The model's most surprising feature is its ability to process documents nearly five times faster than base models while maintaining over 92% of their accuracy, making enterprise-grade search refinement accessible to applications where computational resources are at a premium.

Changes · 3 entries
jina-reranker-v1-tiny-enupdateca7a400Nov 2, 2026, 03:02 AM
nameJina Reranker v1 Tiny English→Jina Reranker v1 Tiny EN
description33M-parameter English reranker via knowledge distillation...→Jina Reranker v1 Tiny English represents a breakthrough i...
jina-reranker-v1-tiny-enupdate80c574aJun 3, 2026, 07:05 AM
nameJina Reranker v1 Tiny English→Jina Reranker v1 Tiny EN
description33M-parameter English reranker via knowledge distillation...→Jina Reranker v1 Tiny English represents a breakthrough i...
jina-reranker-v1-tiny-encreate3e56cf1Apr 26, 2026, 07:23 PM