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