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.

Leaderboard Rank

Rank: 7 of 12
ELO Rating: 1510
Win Rate: 49.8%
Accuracy (nDCG@10): 0.094
Latency: 447ms

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

  • Wins: 1643 (49.8%)
  • Losses: 1540 (46.7%)
  • Ties: 117 (3.5%)

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 4 Fast's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.

Cohere Rerank 4 Fast - ELO by Dataset

  • business reports: 1426
  • DBPedia: 1471
  • MSMARCO: 1515
  • PG: 1615
  • arguana: 1604
  • FiQa: 1429

Detailed Metrics

Dataset breakdown

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

business reports

  • ELO: 1603
  • Win Rate: 56.2%
  • Record: 309W-231L-10T

Accuracy Metrics

  • nDCG@50: 0.000
  • nDCG@100: 0.000
  • Recall@50: 0.000
  • Recall@100: 0.000

Latency Distribution

  • Mean: 428ms
  • P50 (Median): 408ms
  • P90: 550ms

DBPedia

  • ELO: 1580
  • Win Rate: 41.4%
  • Record: 228W-282L-40T

Accuracy Metrics

  • nDCG@50: 0.000
  • nDCG@100: 0.000
  • Recall@50: 0.000
  • Recall@100: 0.000

Latency Distribution

  • Mean: 297ms
  • P50 (Median): 297ms
  • P90: 309ms

MSMARCO

  • ELO: 1501
  • Win Rate: 45.1%
  • Record: 248W-251L-51T

Accuracy Metrics

  • nDCG@50: 0.000
  • nDCG@100: 0.000
  • Recall@50: 0.000
  • Recall@100: 0.000

Latency Distribution

  • Mean: 403ms
  • P50 (Median): 382ms
  • P90: 486ms

PG

  • ELO: 1474
  • Win Rate: 41.6%
  • Record: 229W-321L-0T

Accuracy Metrics

  • nDCG@50: 0.000
  • nDCG@100: 0.000
  • Recall@50: 0.000
  • Recall@100: 0.000

Latency Distribution

  • Mean: 492ms
  • P50 (Median): 439ms
  • P90: 650ms

arguana

  • ELO: 1472
  • Win Rate: 62.2%
  • Record: 342W-203L-5T

Accuracy Metrics

  • nDCG@50: 0.351
  • nDCG@100: 0.425
  • Recall@50: 0.660
  • Recall@100: 0.880

Latency Distribution

  • Mean: 574ms
  • P50 (Median): 562ms
  • P90: 728ms

FiQa

  • ELO: 1429
  • Win Rate: 42.2%
  • Record: 287W-252L-11T

Accuracy Metrics

  • nDCG@50: 0.135
  • nDCG@100: 0.138
  • Recall@50: 0.125
  • Recall@100: 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.

Code Example

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 Cohere Rerank 4 Fast stacks up against other top rerankers to understand the differences in performance, accuracy, and latency.