Voyage 4 vs Jina Embeddings v5 Text Small

Detailed comparison between Voyage 4 and Jina Embeddings v5 Text Small. See which embedding best meets your accuracy and performance needs. If you want to compare these models on your data, try Agentset.

Model Comparison

Voyage 4 takes the lead.

Both Voyage 4 and Jina Embeddings v5 Text Small are powerful embedding models designed to improve retrieval quality in RAG applications. However, their performance characteristics differ in important ways.

Why Voyage 4:

  • Voyage 4 has 20 higher ELO rating
  • Voyage 4 delivers better accuracy (nDCG@10: 0.624 vs 0.608)
  • Jina Embeddings v5 Text Small is 50ms faster on average

Overview

Key metrics

ELO Rating

Overall ranking quality

Voyage 4

1586

Jina Embeddings v5 Text Small

1566

Win Rate

Head-to-head performance

Voyage 4

57.0%

Jina Embeddings v5 Text Small

54.7%

Accuracy (nDCG@10)

Ranking quality metric

Voyage 4

0.624

Jina Embeddings v5 Text Small

0.608

Average Latency

Response time

Voyage 4

339ms

Jina Embeddings v5 Text Small

289ms

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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

MetricVoyage 4Jina Embeddings v5 Text SmallDescription
Overall Performance
ELO Rating
1586
1566
Overall ranking quality based on pairwise comparisons
Win Rate
57.0%
54.7%
Percentage of comparisons won against other models
Pricing & Availability
Price per 1M tokens
$0.060
$0.050
Cost per million tokens processed
Dimensions
1024
1024
Vector embedding dimensions (lower is more efficient)
Release Date
2026-01-15
2026-02-18
Model release date
Accuracy Metrics
Avg nDCG@10
0.624
0.608
Normalized discounted cumulative gain at position 10
Performance Metrics
Avg Latency
339ms
289ms
Average response time across all datasets

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Agentset gives you a complete RAG API with top-ranked embedding models and smart retrieval 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.

PG

MetricVoyage 4Jina Embeddings v5 Text SmallDescription
Accuracy Metrics
nDCG@5
0.000
0.000
Ranking quality at top 5 results
nDCG@10
0.000
0.000
Ranking quality at top 10 results
Recall@5
0.000
0.000
% of relevant docs in top 5
Recall@10
0.000
0.000
% of relevant docs in top 10
Latency Metrics
Mean
311ms
291ms
Average response time
P50
309ms
241ms
50th percentile (median)
P90
321ms
290ms
90th percentile

business reports

MetricVoyage 4Jina Embeddings v5 Text SmallDescription
Accuracy Metrics
nDCG@5
0.000
0.000
Ranking quality at top 5 results
nDCG@10
0.000
0.000
Ranking quality at top 10 results
Recall@5
0.000
0.000
% of relevant docs in top 5
Recall@10
0.000
0.000
% of relevant docs in top 10
Latency Metrics
Mean
309ms
283ms
Average response time
P50
310ms
247ms
50th percentile (median)
P90
325ms
322ms
90th percentile

DBPedia

MetricVoyage 4Jina Embeddings v5 Text SmallDescription
Accuracy Metrics
nDCG@5
0.815
0.823
Ranking quality at top 5 results
nDCG@10
0.811
0.805
Ranking quality at top 10 results
Recall@5
0.062
0.062
% of relevant docs in top 5
Recall@10
0.122
0.123
% of relevant docs in top 10
Latency Metrics
Mean
327ms
270ms
Average response time
P50
312ms
239ms
50th percentile (median)
P90
357ms
264ms
90th percentile

FiQa

MetricVoyage 4Jina Embeddings v5 Text SmallDescription
Accuracy Metrics
nDCG@5
0.873
0.838
Ranking quality at top 5 results
nDCG@10
0.859
0.831
Ranking quality at top 10 results
Recall@5
0.763
0.677
% of relevant docs in top 5
Recall@10
0.840
0.771
% of relevant docs in top 10
Latency Metrics
Mean
310ms
300ms
Average response time
P50
311ms
241ms
50th percentile (median)
P90
324ms
419ms
90th percentile

SciFact

MetricVoyage 4Jina Embeddings v5 Text SmallDescription
Accuracy Metrics
nDCG@5
0.737
0.703
Ranking quality at top 5 results
nDCG@10
0.758
0.734
Ranking quality at top 10 results
Recall@5
0.804
0.789
% of relevant docs in top 5
Recall@10
0.878
0.898
% of relevant docs in top 10
Latency Metrics
Mean
321ms
267ms
Average response time
P50
311ms
240ms
50th percentile (median)
P90
331ms
265ms
90th percentile

MSMARCO

MetricVoyage 4Jina Embeddings v5 Text SmallDescription
Accuracy Metrics
nDCG@5
0.941
0.960
Ranking quality at top 5 results
nDCG@10
0.931
0.954
Ranking quality at top 10 results
Recall@5
0.123
0.122
% of relevant docs in top 5
Recall@10
0.221
0.219
% of relevant docs in top 10
Latency Metrics
Mean
317ms
273ms
Average response time
P50
307ms
239ms
50th percentile (median)
P90
323ms
313ms
90th percentile

ARCD

MetricVoyage 4Jina Embeddings v5 Text SmallDescription
Accuracy Metrics
nDCG@5
0.916
0.842
Ranking quality at top 5 results
nDCG@10
0.916
0.842
Ranking quality at top 10 results
Recall@5
0.980
0.940
% of relevant docs in top 5
Recall@10
0.980
0.940
% of relevant docs in top 10
Latency Metrics
Mean
477ms
336ms
Average response time
P50
310ms
248ms
50th percentile (median)
P90
331ms
305ms
90th percentile

Explore More

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