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VoyageAI by MongoDB: rerank-2.5-lite

Source description (English)

rerank-2.5-lite is a reranker optimized for both latency and quality, delivering a 7.16% improvement in retrieval accuracy over Cohere Rerank v3.5 across 93 datasets. It also outperformed Cohere Rerank v3.5...

View source on OpenRouter
Overview

Model specifications

Context
32,000 tokens
Maximum output
28,800 tokens
Architecture
text->rerank
Tokenizer
Other
Knowledge cutoff
Not provided
Moderated
No
OPENROUTER

Complete pricing

Synchronized OpenRouter rates. Token prices are shown per one million tokens.

Input
$0.02
per million input tokens
API

Quick start

Set OPENROUTER_API_KEY locally. Python requires requests; JavaScript runs in Node.js. Keep the key on the server.

API documentation

curl --fail-with-body https://openrouter.ai/api/v1/rerank \
  -H "Authorization: Bearer $OPENROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"voyageai/rerank-2.5-lite","query":"What is the capital of France?","documents":["Paris is the capital of France.","Berlin is the capital of Germany."],"top_n":1}'
Capabilities

Input → Output

Input
text
Output
rerank
API

Supported API parameters

Available providers

1 Provider

Checked: September 7, 2026

Live providers on OpenRouter

Provider availability, latency, throughput and routing can change continuously. Open the source page for current operational data.

OpenRouter

VoyageAI by MongoDB

Not provided
Context
32K
Maximum output
29K
voyageai

4 models

All models
voyageai logovoyageai

VoyageAI by MongoDB: voyage-code-4

voyage-code-4 is a code embedding model from Voyage AI, a MongoDB company. It is designed for coding agents and code retrieval, with Matryoshka embeddings at 2048, 1024, 512, and 256...

Context
32K
Input
text
Output
embeddings
Input: $0.12per 1M tokens
View model details →
voyageai logovoyageai

VoyageAI by MongoDB: rerank-2.5

rerank-2.5 is a cutting-edge reranker optimized for quality, delivering a 7.94% improvement in retrieval accuracy over Cohere Rerank v3.5 across 93 datasets. It also outperformed Cohere Rerank v3.5 by 12.70%...

Context
32K
Input
text
Output
rerank
Input: $0.05 per million input tokens
View model details →
voyageai logovoyageai

VoyageAI by MongoDB: voyage-multimodal-3.5

voyage-multimodal-3.5 is a state-of-the-art multimodal embedding model capable of vectorizing not only text, images, and video individually, but also content that interleaves all three modalities. It delivers excellent performance for...

Context
32K
Input
text · image
Output
embeddings
Input: $0.12per 1M tokens
View model details →
voyageai logovoyageai

VoyageAI by MongoDB: voyage-4-lite

voyage-4-lite is a lightweight, general-purpose embedding model optimized for low latency and cost. Enabled by Matryoshka learning and quantization-aware training, voyage-4-lite supports embeddings in 2048, 1024, 512, and 256 dimensions,...

Context
32K
Input
text
Output
embeddings
Input: $0.02per 1M tokens
View model details →