Voyage AI Rerank 2.5

First reranker with instruction-following capabilities enabling dynamic query steering and domain-specific optimization. Supports 32K token context (8x Cohere's) through advanced distillation techniques. If you want to compare the best rerankers for your data, try Agentset.

Leaderboard Rank

ELO Rating

1544

Win Rate

58.0%

Accuracy (nDCG@10)

0.110

Latency

613ms

Model Information

• Provider: Voyage AI
• License: Proprietary
• Price per 1M tokens: $0.050
• Release Date: 2025-08-11
• Model Name: voyage-rerank-2.5
• Total Evaluations: 3300

Performance Record

• Wins: 1915 (58.0%)
• Losses: 1270 (38.5%)
• Ties: 115 (3.5%)

Rerankers Are Just One Piece of RAG

Agentset gives you a managed RAG pipeline with the top-ranked models and best practices baked in. No infrastructure to maintain, no reranking to configure.

Trusted by teams building production RAG applications

• 5M+ Documents
• 1,500+ Teams
• 99.9% Uptime

Performance Overview

Voyage AI Rerank 2.5's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.

Voyage AI Rerank 2.5 - ELO by Dataset

Detailed Metrics

Dataset breakdown

Performance metrics across different benchmark datasets, including accuracy and latency percentiles.

DBPedia

• ELO: 1680
• Win Rate: 58.7% (323W-188L-39T)

Accuracy Metrics

• nDCG@50: 0.000
• nDCG@100: 0.000
• Recall@50: 0.000
• Recall@100: 0.000

Latency Distribution

• Mean: 583ms
• P50 (Median): 613ms
• P90: 632ms

business reports

• ELO: 1638
• Win Rate: 61.5% (338W-202L-10T)

Accuracy Metrics

• nDCG@50: 0.000
• nDCG@100: 0.000
• Recall@50: 0.000
• Recall@100: 0.000

Latency Distribution

• Mean: 612ms
• P50 (Median): 521ms
• P90: 734ms

FiQa

• ELO: 1627
• Win Rate: 67.1% (369W-167L-14T)

Accuracy Metrics

• nDCG@50: 0.108
• nDCG@100: 0.119
• Recall@50: 0.098
• Recall@100: 0.128

Latency Distribution

• Mean: 627ms
• P50 (Median): 611ms
• P90: 814ms

arguana

• ELO: 1508
• Win Rate: 59.1% (325W-221L-4T)

Accuracy Metrics

• nDCG@50: 0.536
• nDCG@100: 0.543
• Recall@50: 0.960
• Recall@100: 0.980

Latency Distribution

• Mean: 675ms
• P50 (Median): 612ms
• P90: 820ms

MSMARCO

• ELO: 1451
• Win Rate: 46.4% (255W-247L-48T)

Accuracy Metrics

• nDCG@50: 0.000
• nDCG@100: 0.000
• Recall@50: 0.000
• Recall@100: 0.000

Latency Distribution

• Mean: 571ms
• P50 (Median): 611ms
• P90: 647ms

PG

• ELO: 1360
• Win Rate: 55.5% (305W-245L-0T)

Accuracy Metrics

• nDCG@50: 0.000
• nDCG@100: 0.000
• Recall@50: 0.000
• Recall@100: 0.000

Latency Distribution

• Mean: 612ms
• P50 (Median): 612ms
• P90: 791ms

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.

Example Code

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 with other top rerankers to understand the differences in performance, accuracy, and latency.