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Jina Embeddings v5 Text Small vs Qwen3 Embedding 4B

Detailed comparison between Jina Embeddings v5 Text Small and Qwen3 Embedding 4B. See which embedding best meets your accuracy and performance needs. If you want to compare these models on your data, try Agentset.

Explore Jina Embeddings v5 Text SmallExplore Qwen3 Embedding 4B

Model Comparison

Jina Embeddings v5 Text Small takes the lead.

Both Jina Embeddings v5 Text Small and Qwen3 Embedding 4B are powerful embedding models designed to improve retrieval quality in RAG applications. However, their performance characteristics differ in important ways.

Why Jina Embeddings v5 Text Small:

  • Jina Embeddings v5 Text Small has 84 higher ELO rating
  • Qwen3 Embedding 4B delivers better accuracy (nDCG@10: 0.705 vs 0.608)
  • Qwen3 Embedding 4B is 260ms faster on average
  • Jina Embeddings v5 Text Small has a 10.2% higher win rate

Overview

Key metrics

ELO Rating

Overall ranking quality

Jina Embeddings v5 Text Small

1566

Qwen3 Embedding 4B

1482

Win Rate

Head-to-head performance

Jina Embeddings v5 Text Small

54.7%

Qwen3 Embedding 4B

44.6%

Accuracy (nDCG@10)

Ranking quality metric

Jina Embeddings v5 Text Small

0.608

Qwen3 Embedding 4B

0.705

Average Latency

Response time

Jina Embeddings v5 Text Small

289ms

Qwen3 Embedding 4B

29ms

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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 SmallQwen3 Embedding 4BDescription
Overall Performance
ELO Rating
1566
1482
Overall ranking quality based on pairwise comparisons
Win Rate
54.7%
44.6%
Percentage of comparisons won against other models
Pricing & Availability
Price per 1M tokens
$0.050
$0.020
Cost per million tokens processed
Dimensions
1024
2560
Vector embedding dimensions (lower is more efficient)
Release Date
2026-02-18
2025-06-06
Model release date
Accuracy Metrics
Avg nDCG@10
0.608
0.705
Normalized discounted cumulative gain at position 10
Performance Metrics
Avg Latency
289ms
29ms
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.

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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.

business reports

MetricJina Embeddings v5 Text SmallQwen3 Embedding 4BDescription
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
29ms
Average response time
P50
247ms
29ms
50th percentile (median)
P90
322ms
29ms
90th percentile

DBPedia

MetricJina Embeddings v5 Text SmallQwen3 Embedding 4BDescription
Accuracy Metrics
nDCG@5
0.823
0.799
Ranking quality at top 5 results
nDCG@10
0.805
0.787
Ranking quality at top 10 results
Recall@5
0.062
0.061
% of relevant docs in top 5
Recall@10
0.123
0.119
% of relevant docs in top 10
Latency Metrics
Mean
270ms
26ms
Average response time
P50
239ms
26ms
50th percentile (median)
P90
264ms
26ms
90th percentile

FiQa

MetricJina Embeddings v5 Text SmallQwen3 Embedding 4BDescription
Accuracy Metrics
nDCG@5
0.838
0.838
Ranking quality at top 5 results
nDCG@10
0.831
0.836
Ranking quality at top 10 results
Recall@5
0.677
0.719
% of relevant docs in top 5
Recall@10
0.771
0.839
% of relevant docs in top 10
Latency Metrics
Mean
300ms
23ms
Average response time
P50
241ms
23ms
50th percentile (median)
P90
419ms
23ms
90th percentile

SciFact

MetricJina Embeddings v5 Text SmallQwen3 Embedding 4BDescription
Accuracy Metrics
nDCG@5
0.703
0.666
Ranking quality at top 5 results
nDCG@10
0.734
0.697
Ranking quality at top 10 results
Recall@5
0.789
0.782
% of relevant docs in top 5
Recall@10
0.898
0.891
% of relevant docs in top 10
Latency Metrics
Mean
267ms
38ms
Average response time
P50
240ms
38ms
50th percentile (median)
P90
265ms
38ms
90th percentile

MSMARCO

MetricJina Embeddings v5 Text SmallQwen3 Embedding 4BDescription
Accuracy Metrics
nDCG@5
0.960
0.974
Ranking quality at top 5 results
nDCG@10
0.954
0.954
Ranking quality at top 10 results
Recall@5
0.122
0.124
% of relevant docs in top 5
Recall@10
0.219
0.224
% of relevant docs in top 10
Latency Metrics
Mean
273ms
31ms
Average response time
P50
239ms
31ms
50th percentile (median)
P90
313ms
31ms
90th percentile

ARCD

MetricJina Embeddings v5 Text SmallQwen3 Embedding 4BDescription
Accuracy Metrics
nDCG@5
0.842
0.857
Ranking quality at top 5 results
nDCG@10
0.842
0.864
Ranking quality at top 10 results
Recall@5
0.940
0.940
% of relevant docs in top 5
Recall@10
0.940
0.960
% of relevant docs in top 10
Latency Metrics
Mean
336ms
25ms
Average response time
P50
248ms
25ms
50th percentile (median)
P90
305ms
25ms
90th percentile

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