OpenAI text-embedding-3-large vs Gemini text-embedding-004
Detailed comparison between OpenAI text-embedding-3-large and Gemini text-embedding-004. 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 Gemini text-embedding-004 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 92 higher ELO rating
- OpenAI text-embedding-3-large delivers better accuracy (nDCG@10: 0.811 vs 0.585)
- OpenAI text-embedding-3-large is 10178ms faster on average
- OpenAI text-embedding-3-large has a 27.8% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
OpenAI text-embedding-3-large
Gemini text-embedding-004
Win Rate
Head-to-head performance
OpenAI text-embedding-3-large
Gemini text-embedding-004
Accuracy (nDCG@10)
Ranking quality metric
OpenAI text-embedding-3-large
Gemini text-embedding-004
Average Latency
Response time
OpenAI text-embedding-3-large
Gemini text-embedding-004
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 | Gemini text-embedding-004 | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1539 | 1447 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 55.7% | 28.0% | Percentage of comparisons won against other models |
| Pricing & Availability | |||
| Price per 1M tokens | $0.130 | $0.020 | Cost per million tokens processed |
| Release Date | 2024-01-25 | 2024-05-14 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.811 | 0.585 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 32922ms | 43100ms | 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 | Gemini text-embedding-004 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 57366ms | 76115ms | Average response time |
| P50 | 56219ms | 74593ms | 50th percentile (median) |
| P90 | 65971ms | 87532ms | 90th percentile |
business reports
| Metric | OpenAI text-embedding-3-large | Gemini text-embedding-004 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 8013ms | 9184ms | Average response time |
| P50 | 7853ms | 9000ms | 50th percentile (median) |
| P90 | 9215ms | 10562ms | 90th percentile |
DBPedia
| Metric | OpenAI text-embedding-3-large | Gemini text-embedding-004 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.648 | 0.536 | Ranking quality at top 5 results |
| nDCG@10 | 0.641 | 0.517 | Ranking quality at top 10 results |
| Recall@5 | 0.255 | 0.200 | % of relevant docs in top 5 |
| Recall@10 | 0.377 | 0.304 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 42058ms | 59127ms | Average response time |
| P50 | 41217ms | 57944ms | 50th percentile (median) |
| P90 | 48367ms | 67996ms | 90th percentile |
FiQa
| Metric | OpenAI text-embedding-3-large | Gemini text-embedding-004 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.730 | 0.613 | Ranking quality at top 5 results |
| nDCG@10 | 0.752 | 0.649 | Ranking quality at top 10 results |
| Recall@5 | 0.700 | 0.645 | % of relevant docs in top 5 |
| Recall@10 | 0.781 | 0.748 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 48635ms | 62373ms | Average response time |
| P50 | 47662ms | 61126ms | 50th percentile (median) |
| P90 | 55930ms | 71729ms | 90th percentile |
SciFact
| Metric | OpenAI text-embedding-3-large | Gemini text-embedding-004 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.726 | 0.722 | Ranking quality at top 5 results |
| nDCG@10 | 0.761 | 0.745 | Ranking quality at top 10 results |
| Recall@5 | 0.768 | 0.797 | % of relevant docs in top 5 |
| Recall@10 | 0.863 | 0.860 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 55796ms | 65774ms | Average response time |
| P50 | 54680ms | 64459ms | 50th percentile (median) |
| P90 | 64165ms | 75640ms | 90th percentile |
MSMARCO
| Metric | OpenAI text-embedding-3-large | Gemini text-embedding-004 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 1.000 | 0.979 | Ranking quality at top 5 results |
| nDCG@10 | 1.000 | 0.977 | Ranking quality at top 10 results |
| Recall@5 | 0.123 | 0.118 | % of relevant docs in top 5 |
| Recall@10 | 0.224 | 0.209 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 41860ms | 62416ms | Average response time |
| P50 | 41023ms | 61168ms | 50th percentile (median) |
| P90 | 48139ms | 71778ms | 90th percentile |
NorQuAD
| Metric | OpenAI text-embedding-3-large | Gemini text-embedding-004 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 5613ms | 5693ms | Average response time |
| P50 | 5501ms | 5579ms | 50th percentile (median) |
| P90 | 6455ms | 6547ms | 90th percentile |
ARCD
| Metric | OpenAI text-embedding-3-large | Gemini text-embedding-004 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.899 | 0.030 | Ranking quality at top 5 results |
| nDCG@10 | 0.899 | 0.036 | Ranking quality at top 10 results |
| Recall@5 | 0.940 | 0.040 | % of relevant docs in top 5 |
| Recall@10 | 0.940 | 0.060 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 4036ms | 4118ms | Average response time |
| P50 | 3955ms | 4036ms | 50th percentile (median) |
| P90 | 4641ms | 4736ms | 90th percentile |
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