# 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](https://app.agentset.ai/).

## Leaderboard Rank

#2 of 12

### ELO Rating

1629

#2

### Win Rate

57.7%

#2

### Accuracy (nDCG@10)

0.095

#5

### Latency

614ms

#7

### 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

- **Wins**: 1903 (57.7%)   
- **Losses**: 1270 (38.5%)  
- **Ties**: 127 (3.8%)

## 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.

### Performance Overview

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

- arguana: ELO 1783  WR 364 W - 184 L - 2 T  
- FiQa: ELO 1702  WR 326 W - 207 L - 17 T  
- business reports: ELO 1658  WR 341 W - 197 L - 12 T  
- MSMARCO: ELO 1586  WR 286 W - 209 L - 55 T  
- PG: ELO 1553  WR 313 W - 237 L - 0 T  
- DBPedia: ELO 1491  WR 273 W - 236 L - 41 T

## Dataset Breakdown

### arguana

- **ELO**: 1783  
- **Win Rate**: 66.2%  
- **Accuracy Metrics**:  
  - nDCG@50: 0.353  
  - nDCG@100: 0.439  
  - Recall@50: 0.660  
  - Recall@100: 0.920  
- **Latency Distribution**:  
  - Mean: 785ms  
  - P50 (Median): 768ms  
  - P90: 933ms

### FiQa

- **ELO**: 1702  
- **Win Rate**: 59.3%  
- **Accuracy Metrics**:  
  - nDCG@50: 0.126  
  - nDCG@100: 0.129  
  - Recall@50: 0.130  
  - Recall@100: 0.135  
- **Latency Distribution**:  
  - Mean: 610ms  
  - P50 (Median): 585ms  
  - P90: 817ms

### business reports

- **ELO**: 1658  
- **Win Rate**: 62.0%  
- **Accuracy Metrics**:  
  - nDCG@50: 0.000  
  - nDCG@100: 0.000  
  - Recall@50: 0.000  
  - Recall@100: 0.000  
- **Latency Distribution**:  
  - Mean: 529ms  
  - P50 (Median): 498ms  
  - P90: 675ms

### MSMARCO

- **ELO**: 1586  
- **Win Rate**: 60.0%  
- **Accuracy Metrics**:  
  - nDCG@50: 0.000  
  - nDCG@100: 0.000  
  - Recall@50: 0.000  
  - Recall@100: 0.000  
- **Latency Distribution**:  
  - Mean: 458ms  
  - P50 (Median): 408ms  
  - P90: 615ms

### PG

- **ELO**: 1553  
- **Win Rate**: 56.9%  
- **Accuracy Metrics**:  
  - nDCG@50: 0.000  
  - nDCG@100: 0.000  
  - Recall@50: 0.000  
  - Recall@100: 0.000  
- **Latency Distribution**:  
  - Mean: 760ms  
  - P50 (Median): 720ms  
  - P90: 896ms

### DBPedia

- **ELO**: 1491  
- **Win Rate**: 38.6%  
- **Accuracy Metrics**:  
  - nDCG@50: 0.000  
  - nDCG@100: 0.000  
  - Recall@50: 0.000  
  - Recall@100: 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.

### Example Code

```typescript
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

Compare Cohere Rerank 4 Pro with other top rerankers to understand the differences in performance, accuracy, and latency.