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OpenAI text-embedding-3-small vs Qwen3 Embedding 0.6B

Detailed comparison between OpenAI text-embedding-3-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 OpenAI text-embedding-3-smallExplore Qwen3 Embedding 0.6B

Model Comparison

OpenAI text-embedding-3-small takes the lead.

Both OpenAI text-embedding-3-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 OpenAI text-embedding-3-small:

  • OpenAI text-embedding-3-small has 60 higher ELO rating
  • OpenAI text-embedding-3-small delivers better accuracy (nDCG@10: 0.689 vs 0.656)
  • OpenAI text-embedding-3-small has a 8.3% higher win rate

Overview

Key metrics

ELO Rating

Overall ranking quality

OpenAI text-embedding-3-small

1480

Qwen3 Embedding 0.6B

1420

Win Rate

Head-to-head performance

OpenAI text-embedding-3-small

43.9%

Qwen3 Embedding 0.6B

35.7%

Accuracy (nDCG@10)

Ranking quality metric

OpenAI text-embedding-3-small

0.689

Qwen3 Embedding 0.6B

0.656

Average Latency

Response time

OpenAI text-embedding-3-small

15ms

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

MetricOpenAI text-embedding-3-smallQwen3 Embedding 0.6BDescription
Overall Performance
ELO Rating
1480
1420
Overall ranking quality based on pairwise comparisons
Win Rate
43.9%
35.7%
Percentage of comparisons won against other models
Pricing & Availability
Price per 1M tokens
$0.020
$0.010
Cost per million tokens processed
Dimensions
1536
1024
Vector embedding dimensions (lower is more efficient)
Release Date
2024-01-25
2025-06-06
Model release date
Accuracy Metrics
Avg nDCG@10
0.689
0.656
Normalized discounted cumulative gain at position 10
Performance Metrics
Avg Latency
15ms
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

MetricOpenAI text-embedding-3-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
16ms
21ms
Average response time
P50
16ms
21ms
50th percentile (median)
P90
16ms
21ms
90th percentile

DBPedia

MetricOpenAI text-embedding-3-smallQwen3 Embedding 0.6BDescription
Accuracy Metrics
nDCG@5
0.858
0.716
Ranking quality at top 5 results
nDCG@10
0.807
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
9ms
13ms
Average response time
P50
9ms
13ms
50th percentile (median)
P90
9ms
13ms
90th percentile

FiQa

MetricOpenAI text-embedding-3-smallQwen3 Embedding 0.6BDescription
Accuracy Metrics
nDCG@5
0.801
0.755
Ranking quality at top 5 results
nDCG@10
0.814
0.755
Ranking quality at top 10 results
Recall@5
0.624
0.591
% of relevant docs in top 5
Recall@10
0.682
0.683
% of relevant docs in top 10
Latency Metrics
Mean
16ms
19ms
Average response time
P50
16ms
19ms
50th percentile (median)
P90
16ms
19ms
90th percentile

SciFact

MetricOpenAI text-embedding-3-smallQwen3 Embedding 0.6BDescription
Accuracy Metrics
nDCG@5
0.663
0.658
Ranking quality at top 5 results
nDCG@10
0.684
0.666
Ranking quality at top 10 results
Recall@5
0.774
0.718
% of relevant docs in top 5
Recall@10
0.840
0.779
% of relevant docs in top 10
Latency Metrics
Mean
17ms
62ms
Average response time
P50
17ms
62ms
50th percentile (median)
P90
17ms
62ms
90th percentile

MSMARCO

MetricOpenAI text-embedding-3-smallQwen3 Embedding 0.6BDescription
Accuracy Metrics
nDCG@5
0.959
0.943
Ranking quality at top 5 results
nDCG@10
0.946
0.933
Ranking quality at top 10 results
Recall@5
0.122
0.122
% of relevant docs in top 5
Recall@10
0.212
0.215
% of relevant docs in top 10
Latency Metrics
Mean
20ms
15ms
Average response time
P50
20ms
15ms
50th percentile (median)
P90
20ms
15ms
90th percentile

ARCD

MetricOpenAI text-embedding-3-smallQwen3 Embedding 0.6BDescription
Accuracy Metrics
nDCG@5
0.786
0.757
Ranking quality at top 5 results
nDCG@10
0.793
0.763
Ranking quality at top 10 results
Recall@5
0.900
0.880
% of relevant docs in top 5
Recall@10
0.920
0.900
% of relevant docs in top 10
Latency Metrics
Mean
15ms
18ms
Average response time
P50
15ms
18ms
50th percentile (median)
P90
15ms
18ms
90th percentile

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