Zerank 1 vs Cohere Rerank 4 Fast

Detailed comparison between Zerank 1 and Cohere Rerank 4 Fast. See which reranker best meets your accuracy and performance needs. If you want to compare these models on your data, try Agentset.

Model Comparison

Zerank 1 takes the lead.

Both Zerank 1 and Cohere Rerank 4 Fast are powerful reranking models designed to improve retrieval quality in RAG applications. However, their performance characteristics differ in important ways.

Why Zerank 1:

  • Zerank 1 has 63 higher ELO rating
  • Zerank 1 is 180ms faster on average
  • Zerank 1 has a 7.4% higher win rate

Overview

Key metrics

ELO Rating

Overall ranking quality

Zerank 1

1573

Cohere Rerank 4 Fast

1510

Win Rate

Head-to-head performance

Zerank 1

57.2%

Cohere Rerank 4 Fast

49.8%

Accuracy (nDCG@10)

Ranking quality metric

Zerank 1

0.082

Cohere Rerank 4 Fast

0.094

Average Latency

Response time

Zerank 1

266ms

Cohere Rerank 4 Fast

447ms

Rerankers Are Just One Piece of RAG

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5M+
Documents
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Teams
99.9%
Uptime

Visual Performance Analysis

Performance

ELO Rating Comparison

Win/Loss/Tie Breakdown

Accuracy Across Datasets (nDCG@10)

Latency Distribution (ms)

Breakdown

How the models stack up

MetricZerank 1Cohere Rerank 4 FastDescription
Overall Performance
ELO Rating
1573
1510
Overall ranking quality based on pairwise comparisons
Win Rate
57.2%
49.8%
Percentage of comparisons won against other models
Pricing & Availability
Price per 1M tokens
$0.025
$0.050
Cost per million tokens processed
Release Date
2025-07-10
2025-12-11
Model release date
Accuracy Metrics
Avg nDCG@10
0.082
0.094
Normalized discounted cumulative gain at position 10
Performance Metrics
Avg Latency
266ms
447ms
Average response time across all datasets

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);
}

Dataset Performance

By field

Comprehensive comparison of accuracy metrics (nDCG, Recall) and latency percentiles for each benchmark dataset.

MSMARCO

MetricZerank 1Cohere Rerank 4 FastDescription
Latency Metrics
Mean
232ms
403ms
Average response time
P50
226ms
382ms
50th percentile (median)
P90
245ms
486ms
90th percentile

arguana

MetricZerank 1Cohere Rerank 4 FastDescription
Accuracy Metrics
nDCG@5
0.308
0.351
Ranking quality at top 5 results
nDCG@10
0.369
0.425
Ranking quality at top 10 results
Recall@5
0.580
0.660
% of relevant docs in top 5
Recall@10
0.760
0.880
% of relevant docs in top 10
Latency Metrics
Mean
287ms
574ms
Average response time
P50
275ms
562ms
50th percentile (median)
P90
332ms
728ms
90th percentile

FiQa

MetricZerank 1Cohere Rerank 4 FastDescription
Accuracy Metrics
nDCG@5
0.115
0.135
Ranking quality at top 5 results
nDCG@10
0.121
0.138
Ranking quality at top 10 results
Recall@5
0.105
0.125
% of relevant docs in top 5
Recall@10
0.125
0.130
% of relevant docs in top 10
Latency Metrics
Mean
259ms
485ms
Average response time
P50
258ms
459ms
50th percentile (median)
P90
276ms
624ms
90th percentile

business reports

MetricZerank 1Cohere Rerank 4 FastDescription
Latency Metrics
Mean
289ms
428ms
Average response time
P50
272ms
408ms
50th percentile (median)
P90
363ms
550ms
90th percentile

PG

MetricZerank 1Cohere Rerank 4 FastDescription
Latency Metrics
Mean
293ms
492ms
Average response time
P50
279ms
439ms
50th percentile (median)
P90
348ms
650ms
90th percentile

DBPedia

MetricZerank 1Cohere Rerank 4 FastDescription
Latency Metrics
Mean
238ms
297ms
Average response time
P50
233ms
297ms
50th percentile (median)
P90
257ms
309ms
90th percentile

Explore More

Compare more rerankers

See how all reranking models stack up. Compare Cohere, Jina AI, Voyage, ZeRank, and more. View comprehensive benchmarks, compare performance metrics, and find the perfect reranker for your RAG application.