# Cohere Rerank 3.5

Advanced reranking model with improved reasoning and multilingual capabilities for enterprise data. Excels in complex enterprise scenarios including Finance, E-commerce, and Hospitality across 100+ languages. If you want to compare the best rerankers for your data, try [Agentset](https://app.agentset.ai/).

## Leaderboard Rank

#10 of 12

ELO Rating

1451

#10

Win Rate

40.9%

#10

Accuracy (nDCG@10)

0.080

#11

Latency

392ms

#4

### Model Information

- **Provider**: Cohere  
- **License**: Proprietary  
- **Price per 1M tokens**: $0.050  
- **Release Date**: 2024-12-02  
- **Model Name**: cohere-rerank-v3.5  
- **Total Evaluations**: 3300

### Performance Record

- **Wins**: 1350 (40.9%)  
- **Losses**: 1867 (56.6%)  
- **Ties**: 83 (2.5%)

## 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.

## Performance Overview

### ELO ratings by dataset

Cohere Rerank 3.5's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.

#### Cohere Rerank 3.5 - ELO by Dataset

- arguana: 1224  
- MSMARCO: 1374  
- DBPedia: 1524  
- PG: 1674  
- FiQa: 1692  
- business reports

## Detailed Metrics

### Dataset breakdown

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

### arguana

- **ELO**: 169  
- **Win Rate**: 47.4% 
- **Records**: 261W-289L-0T

#### Accuracy Metrics
- nDCG@50: 0.267  
- nDCG@100: 0.355  
- Recall@50: 0.520  
- Recall@100: 0.800

#### Latency Distribution
- Mean: 570ms  
- P50 (Median): 373ms  
- P90: 617ms

### MSMARCO

- **ELO**: 156  
- **Win Rate**: 39.5%  
- **Records**: 217W-295L-38T

#### Accuracy Metrics
- nDCG@50: 0.000  
- nDCG@100: 0.000  
- Recall@50: 0.000  
- Recall@100: 0.000

#### Latency Distribution
- Mean: 339ms  
- P50 (Median): 285ms  
- P90: 304ms

### DBPedia

- **ELO**: 147  
- **Win Rate**: 38.9%  
- **Records**: 214W-306L-30T

#### Accuracy Metrics
- nDCG@50: 0.000  
- nDCG@100: 0.000  
- Recall@50: 0.000  
- Recall@100: 0.000

#### Latency Distribution
- Mean: 286ms  
- P50 (Median): 279ms  
- P90: 290ms

### PG

- **ELO**: 145  
- **Win Rate**: 47.6%  
- **Records**: 262W-288L-0T

#### Accuracy Metrics
- nDCG@50: 0.000  
- nDCG@100: 0.000  
- Recall@50: 0.000  
- Recall@100: 0.000

#### Latency Distribution
- Mean: 458ms  
- P50 (Median): 360ms  
- P90: 615ms

### FiQa

- **ELO**: 128  
- **Win Rate**: 31.6%  
- **Records**: 174W-369L-7T

#### Accuracy Metrics
- nDCG@50: 0.124  
- nDCG@100: 0.128  
- Recall@50: 0.123  
- Recall@100: 0.130

#### Latency Distribution
- Mean: 364ms  
- P50 (Median): 315ms  
- P90: 401ms

### business reports

- **ELO**: 123  
- **Win Rate**: 40.4%  
- **Records**: 222W-320L-8T

#### Accuracy Metrics
- nDCG@50: 0.000  
- nDCG@100: 0.000  
- Recall@50: 0.000  
- Recall@100: 0.000

#### Latency Distribution
- Mean: 334ms  
- P50 (Median): 293ms  
- P90: 503ms

## 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.

### Code Example

```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);
}
```