OpenAI text-embedding-3-large vs BAAI/bge-m3
Detailed comparison between OpenAI text-embedding-3-large and BAAI/bge-m3. 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 BAAI/bge-m3 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 48 higher ELO rating
- OpenAI text-embedding-3-large delivers better accuracy (nDCG@10: 0.811 vs 0.753)
- OpenAI text-embedding-3-large is 47952ms faster on average
- OpenAI text-embedding-3-large has a 14.8% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
OpenAI text-embedding-3-large
BAAI/bge-m3
Win Rate
Head-to-head performance
OpenAI text-embedding-3-large
BAAI/bge-m3
Accuracy (nDCG@10)
Ranking quality metric
OpenAI text-embedding-3-large
BAAI/bge-m3
Average Latency
Response time
OpenAI text-embedding-3-large
BAAI/bge-m3
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 | BAAI/bge-m3 | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1539 | 1491 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 55.7% | 40.9% | 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 | 2024-01-27 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.811 | 0.753 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 32922ms | 80874ms | 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 | BAAI/bge-m3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 57366ms | 96375ms | Average response time |
| P50 | 56219ms | 94448ms | 50th percentile (median) |
| P90 | 65971ms | 110831ms | 90th percentile |
business reports
| Metric | OpenAI text-embedding-3-large | BAAI/bge-m3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 8013ms | 24163ms | Average response time |
| P50 | 7853ms | 23680ms | 50th percentile (median) |
| P90 | 9215ms | 27787ms | 90th percentile |
DBPedia
| Metric | OpenAI text-embedding-3-large | BAAI/bge-m3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.648 | 0.625 | Ranking quality at top 5 results |
| nDCG@10 | 0.641 | 0.603 | Ranking quality at top 10 results |
| Recall@5 | 0.255 | 0.236 | % of relevant docs in top 5 |
| Recall@10 | 0.377 | 0.341 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 42058ms | 86619ms | Average response time |
| P50 | 41217ms | 84887ms | 50th percentile (median) |
| P90 | 48367ms | 99612ms | 90th percentile |
FiQa
| Metric | OpenAI text-embedding-3-large | BAAI/bge-m3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.730 | 0.597 | Ranking quality at top 5 results |
| nDCG@10 | 0.752 | 0.609 | Ranking quality at top 10 results |
| Recall@5 | 0.700 | 0.607 | % of relevant docs in top 5 |
| Recall@10 | 0.781 | 0.666 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 48635ms | 159871ms | Average response time |
| P50 | 47662ms | 156674ms | 50th percentile (median) |
| P90 | 55930ms | 183852ms | 90th percentile |
SciFact
| Metric | OpenAI text-embedding-3-large | BAAI/bge-m3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.726 | 0.578 | Ranking quality at top 5 results |
| nDCG@10 | 0.761 | 0.617 | Ranking quality at top 10 results |
| Recall@5 | 0.768 | 0.652 | % of relevant docs in top 5 |
| Recall@10 | 0.863 | 0.763 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 55796ms | 154804ms | Average response time |
| P50 | 54680ms | 151708ms | 50th percentile (median) |
| P90 | 64165ms | 178025ms | 90th percentile |
MSMARCO
| Metric | OpenAI text-embedding-3-large | BAAI/bge-m3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 1.000 | 0.997 | Ranking quality at top 5 results |
| nDCG@10 | 1.000 | 0.997 | Ranking quality at top 10 results |
| Recall@5 | 0.123 | 0.122 | % of relevant docs in top 5 |
| Recall@10 | 0.224 | 0.220 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 41860ms | 96153ms | Average response time |
| P50 | 41023ms | 94230ms | 50th percentile (median) |
| P90 | 48139ms | 110576ms | 90th percentile |
NorQuAD
| Metric | OpenAI text-embedding-3-large | BAAI/bge-m3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 5613ms | 16285ms | Average response time |
| P50 | 5501ms | 15959ms | 50th percentile (median) |
| P90 | 6455ms | 18728ms | 90th percentile |
ARCD
| Metric | OpenAI text-embedding-3-large | BAAI/bge-m3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.899 | 0.941 | Ranking quality at top 5 results |
| nDCG@10 | 0.899 | 0.941 | Ranking quality at top 10 results |
| Recall@5 | 0.940 | 0.960 | % of relevant docs in top 5 |
| Recall@10 | 0.940 | 0.960 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 4036ms | 12723ms | Average response time |
| P50 | 3955ms | 12469ms | 50th percentile (median) |
| P90 | 4641ms | 14631ms | 90th percentile |
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