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.
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
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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
Detailed Metrics
Dataset breakdown
Performance metrics across different benchmark datasets, including accuracy and latency percentiles.
arguana
Accuracy Metrics
- nDCG@5
- 0.267
- nDCG@10
- 0.355
- Recall@5
- 0.520
- Recall@10
- 0.800
Latency Distribution
- Mean
- 570ms
- P50 (Median)
- 373ms
- P90
- 617ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 339ms
- P50 (Median)
- 285ms
- P90
- 304ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 286ms
- P50 (Median)
- 279ms
- P90
- 290ms
PG
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 458ms
- P50 (Median)
- 360ms
- P90
- 615ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.124
- nDCG@10
- 0.128
- Recall@5
- 0.123
- Recall@10
- 0.130
Latency Distribution
- Mean
- 364ms
- P50 (Median)
- 315ms
- P90
- 401ms
business reports
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 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.
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 3.5 with other top rerankers to understand the differences in performance, accuracy, and latency.