Qwen3 Embedding 0.6B vs OpenAI text-embedding-3-large
Detailed comparison between Qwen3 Embedding 0.6B and OpenAI text-embedding-3-large. See which embedding best meets your accuracy and performance needs.
Model Comparison
OpenAI text-embedding-3-large takes the lead.
Both Qwen3 Embedding 0.6B and OpenAI text-embedding-3-large 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-large:
- OpenAI text-embedding-3-large has 60 higher ELO rating
- OpenAI text-embedding-3-large delivers better accuracy (nDCG@10: 0.811 vs 0.751)
- OpenAI text-embedding-3-large is 37140ms faster on average
- OpenAI text-embedding-3-large has a 18.5% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
Qwen3 Embedding 0.6B
OpenAI text-embedding-3-large
Win Rate
Head-to-head performance
Qwen3 Embedding 0.6B
OpenAI text-embedding-3-large
Accuracy (nDCG@10)
Ranking quality metric
Qwen3 Embedding 0.6B
OpenAI text-embedding-3-large
Average Latency
Response time
Qwen3 Embedding 0.6B
OpenAI text-embedding-3-large
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 | Qwen3 Embedding 0.6B | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1478 | 1539 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 37.3% | 55.7% | Percentage of comparisons won against other models |
| Pricing & Availability | |||
| Price per 1M tokens | $0.010 | $0.130 | Cost per million tokens processed |
| Release Date | 2025-06-06 | 2024-01-25 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.751 | 0.811 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 70062ms | 32922ms | 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 | Qwen3 Embedding 0.6B | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 77697ms | 57366ms | Average response time |
| P50 | 76143ms | 56219ms | 50th percentile (median) |
| P90 | 89352ms | 65971ms | 90th percentile |
business reports
| Metric | Qwen3 Embedding 0.6B | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 15599ms | 8013ms | Average response time |
| P50 | 15287ms | 7853ms | 50th percentile (median) |
| P90 | 17939ms | 9215ms | 90th percentile |
DBPedia
| Metric | Qwen3 Embedding 0.6B | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.549 | 0.648 | Ranking quality at top 5 results |
| nDCG@10 | 0.556 | 0.641 | Ranking quality at top 10 results |
| Recall@5 | 0.216 | 0.255 | % of relevant docs in top 5 |
| Recall@10 | 0.350 | 0.377 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 67654ms | 42058ms | Average response time |
| P50 | 66301ms | 41217ms | 50th percentile (median) |
| P90 | 77802ms | 48367ms | 90th percentile |
FiQa
| Metric | Qwen3 Embedding 0.6B | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.620 | 0.730 | Ranking quality at top 5 results |
| nDCG@10 | 0.647 | 0.752 | Ranking quality at top 10 results |
| Recall@5 | 0.590 | 0.700 | % of relevant docs in top 5 |
| Recall@10 | 0.680 | 0.781 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 212205ms | 48635ms | Average response time |
| P50 | 207961ms | 47662ms | 50th percentile (median) |
| P90 | 244036ms | 55930ms | 90th percentile |
SciFact
| Metric | Qwen3 Embedding 0.6B | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.666 | 0.726 | Ranking quality at top 5 results |
| nDCG@10 | 0.686 | 0.761 | Ranking quality at top 10 results |
| Recall@5 | 0.723 | 0.768 | % of relevant docs in top 5 |
| Recall@10 | 0.783 | 0.863 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 102019ms | 55796ms | Average response time |
| P50 | 99979ms | 54680ms | 50th percentile (median) |
| P90 | 117322ms | 64165ms | 90th percentile |
MSMARCO
| Metric | Qwen3 Embedding 0.6B | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.997 | 1.000 | Ranking quality at top 5 results |
| nDCG@10 | 0.992 | 1.000 | Ranking quality at top 10 results |
| Recall@5 | 0.122 | 0.123 | % of relevant docs in top 5 |
| Recall@10 | 0.215 | 0.224 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 65717ms | 41860ms | Average response time |
| P50 | 64403ms | 41023ms | 50th percentile (median) |
| P90 | 75575ms | 48139ms | 90th percentile |
NorQuAD
| Metric | Qwen3 Embedding 0.6B | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 12763ms | 5613ms | Average response time |
| P50 | 12508ms | 5501ms | 50th percentile (median) |
| P90 | 14677ms | 6455ms | 90th percentile |
ARCD
| Metric | Qwen3 Embedding 0.6B | OpenAI text-embedding-3-large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.865 | 0.899 | Ranking quality at top 5 results |
| nDCG@10 | 0.872 | 0.899 | Ranking quality at top 10 results |
| Recall@5 | 0.880 | 0.940 | % of relevant docs in top 5 |
| Recall@10 | 0.900 | 0.940 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 6841ms | 4036ms | Average response time |
| P50 | 6704ms | 3955ms | 50th percentile (median) |
| P90 | 7867ms | 4641ms | 90th percentile |
Explore More
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