Jina Embeddings v5 Text Small vs Voyage 4

Detailed comparison between Jina Embeddings v5 Text Small and Voyage 4. 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 Jina Embeddings v5 Text Small and Voyage 4 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

Jina Embeddings v5 Text Small

1566

Voyage 4

1586

Win Rate

Head-to-head performance

Jina Embeddings v5 Text Small

54.7%

Voyage 4

57.0%

Accuracy (nDCG@10)

Ranking quality metric

Jina Embeddings v5 Text Small

0.608

Voyage 4

0.624

Average Latency

Response time

Jina Embeddings v5 Text Small

289ms

Voyage 4

339ms

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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

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

Build RAG in Minutes, Not Months

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

MetricJina Embeddings v5 Text SmallVoyage 4Description
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
291ms
311ms
Average response time
P50
241ms
309ms
50th percentile (median)
P90
290ms
321ms
90th percentile

business reports

MetricJina Embeddings v5 Text SmallVoyage 4Description
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
283ms
309ms
Average response time
P50
247ms
310ms
50th percentile (median)
P90
322ms
325ms
90th percentile

DBPedia

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

FiQa

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

SciFact

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

MSMARCO

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

ARCD

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

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