Cohere Rerank 4 Pro
Advanced cross-encoder reranking models (Fast & Pro) optimized for enterprise search and RAG, featuring a 32K context window, strong performance on long and complex documents, and multilingual retrieval across 100+ languages. Built for high-stakes domains like finance, healthcare, manufacturing, and e-commerce, with self-learning to adapt to domain-specific data over time. If you want to compare the best rerankers for your data, try Agentset.
Model Information
- Provider
- Cohere
- License
- Proprietary
- Price per 1M tokens
- $0.050
- Release Date
- 2025-12-11
- Model Name
- rerank-v4.0-pro
- Total Evaluations
- 3300
Performance Record
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.
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Performance Overview
ELO ratings by dataset
Cohere Rerank 4 Pro's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
Cohere Rerank 4 Pro - ELO by Dataset
Detailed Metrics
Dataset breakdown
Performance metrics across different benchmark datasets, including accuracy and latency percentiles.
arguana
Accuracy Metrics
- nDCG@5
- 0.353
- nDCG@10
- 0.439
- Recall@5
- 0.660
- Recall@10
- 0.920
Latency Distribution
- Mean
- 785ms
- P50 (Median)
- 768ms
- P90
- 933ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.126
- nDCG@10
- 0.129
- Recall@5
- 0.130
- Recall@10
- 0.135
Latency Distribution
- Mean
- 610ms
- P50 (Median)
- 585ms
- P90
- 817ms
business reports
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 529ms
- P50 (Median)
- 498ms
- P90
- 675ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 458ms
- P50 (Median)
- 408ms
- P90
- 615ms
PG
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 760ms
- P50 (Median)
- 720ms
- P90
- 896ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 541ms
- P50 (Median)
- 489ms
- P90
- 729ms
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 Cohere Rerank 4 Pro with other top rerankers to understand the differences in performance, accuracy, and latency.