Cohere Rerank 4 Fast
Fast cross-encoder reranker for enterprise search and RAG, built for low-latency production workloads. Supports up to 32K context and strong multilingual retrieval across 100+ languages, with optional self-learning to adapt to your domain 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-fast
- Total Evaluations
- 3300
Performance Record
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Performance Overview
ELO ratings by dataset
Cohere Rerank 4 Fast's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
Cohere Rerank 4 Fast - ELO by Dataset
Detailed Metrics
Dataset breakdown
Performance metrics across different benchmark datasets, including accuracy and latency percentiles.
business reports
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 428ms
- P50 (Median)
- 408ms
- P90
- 550ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 297ms
- P50 (Median)
- 297ms
- P90
- 309ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 403ms
- P50 (Median)
- 382ms
- P90
- 486ms
PG
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 492ms
- P50 (Median)
- 439ms
- P90
- 650ms
arguana
Accuracy Metrics
- nDCG@5
- 0.351
- nDCG@10
- 0.425
- Recall@5
- 0.660
- Recall@10
- 0.880
Latency Distribution
- Mean
- 574ms
- P50 (Median)
- 562ms
- P90
- 728ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.135
- nDCG@10
- 0.138
- Recall@5
- 0.125
- Recall@10
- 0.130
Latency Distribution
- Mean
- 485ms
- P50 (Median)
- 459ms
- P90
- 624ms
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 Fast with other top rerankers to understand the differences in performance, accuracy, and latency.