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.
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 25 higher ELO rating
- OpenAI text-embedding-3-small delivers better accuracy (nDCG@10: 0.762 vs 0.751)
- OpenAI text-embedding-3-small is 40104ms faster on average
- OpenAI text-embedding-3-small has a 7.3% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
OpenAI text-embedding-3-small
Qwen3 Embedding 0.6B
Win Rate
Head-to-head performance
OpenAI text-embedding-3-small
Qwen3 Embedding 0.6B
Accuracy (nDCG@10)
Ranking quality metric
OpenAI text-embedding-3-small
Qwen3 Embedding 0.6B
Average Latency
Response time
OpenAI text-embedding-3-small
Qwen3 Embedding 0.6B
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
| Metric | OpenAI text-embedding-3-small | Qwen3 Embedding 0.6B | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1503 | 1478 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 44.6% | 37.3% | Percentage of comparisons won against other models |
| Pricing & Availability | |||
| Price per 1M tokens | $0.020 | $0.010 | Cost per million tokens processed |
| Release Date | 2024-01-25 | 2025-06-06 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.762 | 0.751 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 29958ms | 70062ms | Average response time across all datasets |
Dataset Performance
By field
Comprehensive comparison of accuracy metrics (nDCG, Recall) and latency percentiles for each benchmark dataset.
PG
| Metric | OpenAI text-embedding-3-small | Qwen3 Embedding 0.6B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 55877ms | 77697ms | Average response time |
| P50 | 54759ms | 76143ms | 50th percentile (median) |
| P90 | 64259ms | 89352ms | 90th percentile |
business reports
| Metric | OpenAI text-embedding-3-small | Qwen3 Embedding 0.6B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 5645ms | 15599ms | Average response time |
| P50 | 5532ms | 15287ms | 50th percentile (median) |
| P90 | 6492ms | 17939ms | 90th percentile |
DBPedia
| Metric | OpenAI text-embedding-3-small | Qwen3 Embedding 0.6B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.605 | 0.549 | Ranking quality at top 5 results |
| nDCG@10 | 0.604 | 0.556 | Ranking quality at top 10 results |
| Recall@5 | 0.230 | 0.216 | % of relevant docs in top 5 |
| Recall@10 | 0.365 | 0.350 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 35365ms | 67654ms | Average response time |
| P50 | 34658ms | 66301ms | 50th percentile (median) |
| P90 | 40670ms | 77802ms | 90th percentile |
FiQa
| Metric | OpenAI text-embedding-3-small | Qwen3 Embedding 0.6B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.635 | 0.620 | Ranking quality at top 5 results |
| nDCG@10 | 0.647 | 0.647 | Ranking quality at top 10 results |
| Recall@5 | 0.623 | 0.590 | % of relevant docs in top 5 |
| Recall@10 | 0.681 | 0.680 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 41338ms | 212205ms | Average response time |
| P50 | 40511ms | 207961ms | 50th percentile (median) |
| P90 | 47539ms | 244036ms | 90th percentile |
SciFact
| Metric | OpenAI text-embedding-3-small | Qwen3 Embedding 0.6B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.682 | 0.666 | Ranking quality at top 5 results |
| nDCG@10 | 0.707 | 0.686 | Ranking quality at top 10 results |
| Recall@5 | 0.778 | 0.723 | % of relevant docs in top 5 |
| Recall@10 | 0.843 | 0.783 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 55544ms | 102019ms | Average response time |
| P50 | 54433ms | 99979ms | 50th percentile (median) |
| P90 | 63876ms | 117322ms | 90th percentile |
MSMARCO
| Metric | OpenAI text-embedding-3-small | Qwen3 Embedding 0.6B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.997 | 0.997 | Ranking quality at top 5 results |
| nDCG@10 | 0.990 | 0.992 | Ranking quality at top 10 results |
| Recall@5 | 0.122 | 0.122 | % of relevant docs in top 5 |
| Recall@10 | 0.213 | 0.215 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 35961ms | 65717ms | Average response time |
| P50 | 35242ms | 64403ms | 50th percentile (median) |
| P90 | 41355ms | 75575ms | 90th percentile |
NorQuAD
| Metric | OpenAI text-embedding-3-small | Qwen3 Embedding 0.6B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 6467ms | 12763ms | Average response time |
| P50 | 6338ms | 12508ms | 50th percentile (median) |
| P90 | 7437ms | 14677ms | 90th percentile |
ARCD
| Metric | OpenAI text-embedding-3-small | Qwen3 Embedding 0.6B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.855 | 0.865 | Ranking quality at top 5 results |
| nDCG@10 | 0.862 | 0.872 | 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 | 3464ms | 6841ms | Average response time |
| P50 | 3395ms | 6704ms | 50th percentile (median) |
| P90 | 3984ms | 7867ms | 90th percentile |
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