Voyage AI Rerank 2.5 Lite
Latency-optimized version maintaining instruction-following and 32K context capabilities with streamlined inference. Designed for high-volume production deployments prioritizing cost efficiency over maximum accuracy. If you want to compare the best rerankers for your data, try Agentset.
Model Information
- Provider
- Voyage AI
- License
- Proprietary
- Price per 1M tokens
- $0.020
- Release Date
- 2025-08-11
- Model Name
- voyage-rerank-2.5-lite
- Total Evaluations
- 3300
Performance Record
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Performance Overview
ELO ratings by dataset
Voyage AI Rerank 2.5 Lite's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
Voyage AI Rerank 2.5 Lite - ELO by Dataset
Detailed Metrics
Dataset breakdown
Performance metrics across different benchmark datasets, including accuracy and latency percentiles.
arguana
Accuracy Metrics
- nDCG@5
- 0.436
- nDCG@10
- 0.496
- Recall@5
- 0.800
- Recall@10
- 0.980
Latency Distribution
- Mean
- 636ms
- P50 (Median)
- 613ms
- P90
- 819ms
business reports
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 599ms
- P50 (Median)
- 611ms
- P90
- 727ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.111
- nDCG@10
- 0.122
- Recall@5
- 0.103
- Recall@10
- 0.135
Latency Distribution
- Mean
- 639ms
- P50 (Median)
- 613ms
- P90
- 819ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 563ms
- P50 (Median)
- 610ms
- P90
- 619ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 587ms
- P50 (Median)
- 612ms
- P90
- 656ms
PG
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 670ms
- P50 (Median)
- 615ms
- P90
- 818ms
Build RAG in Minutes, Not Months
Agentset gives you a complete RAG API with top-ranked rerankers and embedding models built in. Upload your data, call the API, and get accurate results from day one.
import { Agentset } from "agentset";
const agentset = new Agentset();
const ns = agentset.namespace("ns_1234");
const results = await ns.search(
"What is multi-head attention?"
);
for (const result of results) {
console.log(result.text);
}Compare Models
See how it stacks up
Compare Voyage AI Rerank 2.5 Lite with other top rerankers to understand the differences in performance, accuracy, and latency.