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Jina Embeddings v5 Text Small vs Qwen3 Embedding 0.6B

Detailed comparison between Jina Embeddings v5 Text Small and Qwen3 Embedding 0.6B. 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 0.6B

Model Comparison

Jina Embeddings v5 Text Small takes the lead.

Both Jina Embeddings v5 Text Small and Qwen3 Embedding 0.6B 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 146 higher ELO rating
  • Qwen3 Embedding 0.6B delivers better accuracy (nDCG@10: 0.656 vs 0.608)
  • Qwen3 Embedding 0.6B is 264ms faster on average
  • Jina Embeddings v5 Text Small has a 19.1% higher win rate

Overview

Key metrics

ELO Rating

Overall ranking quality

Jina Embeddings v5 Text Small

1566

Qwen3 Embedding 0.6B

1420

Win Rate

Head-to-head performance

Jina Embeddings v5 Text Small

54.7%

Qwen3 Embedding 0.6B

35.7%

Accuracy (nDCG@10)

Ranking quality metric

Jina Embeddings v5 Text Small

0.608

Qwen3 Embedding 0.6B

0.656

Average Latency

Response time

Jina Embeddings v5 Text Small

289ms

Qwen3 Embedding 0.6B

25ms

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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 0.6BDescription
Overall Performance
ELO Rating
1566
1420
Overall ranking quality based on pairwise comparisons
Win Rate
54.7%
35.7%
Percentage of comparisons won against other models
Pricing & Availability
Price per 1M tokens
$0.050
$0.010
Cost per million tokens processed
Dimensions
1024
1024
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
0.656
Normalized discounted cumulative gain at position 10
Performance Metrics
Avg Latency
289ms
25ms
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 0.6BDescription
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
21ms
Average response time
P50
247ms
21ms
50th percentile (median)
P90
322ms
21ms
90th percentile

DBPedia

MetricJina Embeddings v5 Text SmallQwen3 Embedding 0.6BDescription
Accuracy Metrics
nDCG@5
0.823
0.716
Ranking quality at top 5 results
nDCG@10
0.805
0.730
Ranking quality at top 10 results
Recall@5
0.062
0.053
% of relevant docs in top 5
Recall@10
0.123
0.105
% of relevant docs in top 10
Latency Metrics
Mean
270ms
13ms
Average response time
P50
239ms
13ms
50th percentile (median)
P90
264ms
13ms
90th percentile

FiQa

MetricJina Embeddings v5 Text SmallQwen3 Embedding 0.6BDescription
Accuracy Metrics
nDCG@5
0.838
0.755
Ranking quality at top 5 results
nDCG@10
0.831
0.755
Ranking quality at top 10 results
Recall@5
0.677
0.591
% of relevant docs in top 5
Recall@10
0.771
0.683
% of relevant docs in top 10
Latency Metrics
Mean
300ms
19ms
Average response time
P50
241ms
19ms
50th percentile (median)
P90
419ms
19ms
90th percentile

SciFact

MetricJina Embeddings v5 Text SmallQwen3 Embedding 0.6BDescription
Accuracy Metrics
nDCG@5
0.703
0.658
Ranking quality at top 5 results
nDCG@10
0.734
0.666
Ranking quality at top 10 results
Recall@5
0.789
0.718
% of relevant docs in top 5
Recall@10
0.898
0.779
% of relevant docs in top 10
Latency Metrics
Mean
267ms
62ms
Average response time
P50
240ms
62ms
50th percentile (median)
P90
265ms
62ms
90th percentile

MSMARCO

MetricJina Embeddings v5 Text SmallQwen3 Embedding 0.6BDescription
Accuracy Metrics
nDCG@5
0.960
0.943
Ranking quality at top 5 results
nDCG@10
0.954
0.933
Ranking quality at top 10 results
Recall@5
0.122
0.122
% of relevant docs in top 5
Recall@10
0.219
0.215
% of relevant docs in top 10
Latency Metrics
Mean
273ms
15ms
Average response time
P50
239ms
15ms
50th percentile (median)
P90
313ms
15ms
90th percentile

ARCD

MetricJina Embeddings v5 Text SmallQwen3 Embedding 0.6BDescription
Accuracy Metrics
nDCG@5
0.842
0.757
Ranking quality at top 5 results
nDCG@10
0.842
0.763
Ranking quality at top 10 results
Recall@5
0.940
0.880
% of relevant docs in top 5
Recall@10
0.940
0.900
% of relevant docs in top 10
Latency Metrics
Mean
336ms
18ms
Average response time
P50
248ms
18ms
50th percentile (median)
P90
305ms
18ms
90th percentile

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