Contextual AI Rerank v2 Instruct
Available in 1B, 2B, and 6B parameter sizes with unique recency-awareness capabilities for time-sensitive ranking. Only reranker family capable of ranking recent information higher with ~35% performance improvement on recency tasks. If you want to compare the best rerankers for your data, try Agentset.
Model Information
- Provider
- Contextual AI
- License
- cc-by-nc-4.0
- Price per 1M tokens
- $0.050
- Release Date
- 2025-09-12
- Model Name
- ctxl-rerank-v2-instruct-multilingual
- Total Evaluations
- 3300
Performance Record
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Performance Overview
ELO ratings by dataset
Contextual AI Rerank v2 Instruct's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
Contextual AI Rerank v2 Instruct - 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
- 3231ms
- P50 (Median)
- 3129ms
- P90
- 3651ms
PG
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 3566ms
- P50 (Median)
- 3475ms
- P90
- 4148ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.119
- nDCG@10
- 0.125
- Recall@5
- 0.123
- Recall@10
- 0.135
Latency Distribution
- Mean
- 3283ms
- P50 (Median)
- 3209ms
- P90
- 3891ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 3283ms
- P50 (Median)
- 3260ms
- P90
- 3885ms
arguana
Accuracy Metrics
- nDCG@5
- 0.525
- nDCG@10
- 0.560
- Recall@5
- 0.860
- Recall@10
- 0.960
Latency Distribution
- Mean
- 3627ms
- P50 (Median)
- 3601ms
- P90
- 4037ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 3010ms
- P50 (Median)
- 3042ms
- P90
- 3283ms
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 Contextual AI Rerank v2 Instruct with other top rerankers to understand the differences in performance, accuracy, and latency.