OpenAI text-embedding-3-large vs Voyage 3 Large
Detailed comparison between OpenAI text-embedding-3-large and Voyage 3 Large. 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 Voyage 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 11 higher ELO rating
- Voyage 3 Large delivers better accuracy (nDCG@10: 0.837 vs 0.811)
- OpenAI text-embedding-3-large is 30555ms faster on average
Overview
Key metrics
ELO Rating
Overall ranking quality
OpenAI text-embedding-3-large
Voyage 3 Large
Win Rate
Head-to-head performance
OpenAI text-embedding-3-large
Voyage 3 Large
Accuracy (nDCG@10)
Ranking quality metric
OpenAI text-embedding-3-large
Voyage 3 Large
Average Latency
Response time
OpenAI text-embedding-3-large
Voyage 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 | OpenAI text-embedding-3-large | Voyage 3 Large | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1539 | 1528 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 55.7% | 52.6% | Percentage of comparisons won against other models |
| Pricing & Availability | |||
| Price per 1M tokens | $0.130 | $0.180 | Cost per million tokens processed |
| Release Date | 2024-01-25 | 2025-01-07 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.811 | 0.837 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 32922ms | 63477ms | 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 | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 57366ms | 93447ms | Average response time |
| P50 | 56219ms | 91578ms | 50th percentile (median) |
| P90 | 65971ms | 107464ms | 90th percentile |
business reports
| Metric | OpenAI text-embedding-3-large | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 8013ms | 21279ms | Average response time |
| P50 | 7853ms | 20853ms | 50th percentile (median) |
| P90 | 9215ms | 24471ms | 90th percentile |
DBPedia
| Metric | OpenAI text-embedding-3-large | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.648 | 0.675 | Ranking quality at top 5 results |
| nDCG@10 | 0.641 | 0.638 | Ranking quality at top 10 results |
| Recall@5 | 0.255 | 0.255 | % of relevant docs in top 5 |
| Recall@10 | 0.377 | 0.362 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 42058ms | 97066ms | Average response time |
| P50 | 41217ms | 95125ms | 50th percentile (median) |
| P90 | 48367ms | 111626ms | 90th percentile |
FiQa
| Metric | OpenAI text-embedding-3-large | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.730 | 0.753 | Ranking quality at top 5 results |
| nDCG@10 | 0.752 | 0.780 | Ranking quality at top 10 results |
| Recall@5 | 0.700 | 0.742 | % of relevant docs in top 5 |
| Recall@10 | 0.781 | 0.837 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 48635ms | 80239ms | Average response time |
| P50 | 47662ms | 78634ms | 50th percentile (median) |
| P90 | 55930ms | 92275ms | 90th percentile |
SciFact
| Metric | OpenAI text-embedding-3-large | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.726 | 0.796 | Ranking quality at top 5 results |
| nDCG@10 | 0.761 | 0.809 | Ranking quality at top 10 results |
| Recall@5 | 0.768 | 0.840 | % of relevant docs in top 5 |
| Recall@10 | 0.863 | 0.880 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 55796ms | 92317ms | Average response time |
| P50 | 54680ms | 90471ms | 50th percentile (median) |
| P90 | 64165ms | 106165ms | 90th percentile |
MSMARCO
| Metric | OpenAI text-embedding-3-large | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 1.000 | 0.997 | Ranking quality at top 5 results |
| nDCG@10 | 1.000 | 0.998 | Ranking quality at top 10 results |
| Recall@5 | 0.123 | 0.122 | % of relevant docs in top 5 |
| Recall@10 | 0.224 | 0.222 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 41860ms | 75502ms | Average response time |
| P50 | 41023ms | 73992ms | 50th percentile (median) |
| P90 | 48139ms | 86827ms | 90th percentile |
NorQuAD
| Metric | OpenAI text-embedding-3-large | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 5613ms | 11003ms | Average response time |
| P50 | 5501ms | 10783ms | 50th percentile (median) |
| P90 | 6455ms | 12653ms | 90th percentile |
ARCD
| Metric | OpenAI text-embedding-3-large | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.899 | 0.953 | Ranking quality at top 5 results |
| nDCG@10 | 0.899 | 0.960 | Ranking quality at top 10 results |
| Recall@5 | 0.940 | 0.960 | % of relevant docs in top 5 |
| Recall@10 | 0.940 | 0.980 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 4036ms | 36963ms | Average response time |
| P50 | 3955ms | 36224ms | 50th percentile (median) |
| P90 | 4641ms | 42507ms | 90th percentile |
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