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VoyageAI by MongoDB: rerank-2.5-lite

Descrizione della fonte (inglese)

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...

Vedi la fonte su OpenRouter
Panoramica

Specifiche del modello

Contesto
32.000 tokens
Output massimo
28.800 tokens
Architettura
text->rerank
Tokenizer
Other
Limite di conoscenza
Non indicato
Moderato
No
OPENROUTER

Prezzi completi

Tariffe sincronizzate da OpenRouter, per milione di token.

Input
$0.02
per milione di token di input
API

Avvio rapido

Imposta OPENROUTER_API_KEY localmente. Python richiede requests; JavaScript viene eseguito in Node.js. Conserva la chiave sul server.

Documentazione API

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}'
Capacità e modalità

Input → Output

Input
text
Output
rerank
API

Parametri API supportati

Provider disponibili

1 Provider

Verificato: 7 settembre 2026

Provider live su OpenRouter

Disponibilità, latenza, throughput e routing cambiano continuamente. Consulta la fonte per i dati correnti.

OpenRouter

VoyageAI by MongoDB

Non indicato
Contesto
32K
Output massimo
29K
voyageai

4 modelli

Tutti i modelli
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...

Contesto
32K
Input
text
Output
embeddings
Input: $0.12per milione di token
Vedi i dettagli del modello →
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%...

Contesto
32K
Input
text
Output
rerank
Input: $0.05 per milione di token di input
Vedi i dettagli del modello →
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...

Contesto
32K
Input
text · image
Output
embeddings
Input: $0.12per milione di token
Vedi i dettagli del modello →
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,...

Contesto
32K
Input
text
Output
embeddings
Input: $0.02per milione di token
Vedi i dettagli del modello →