OpenAI text-embedding-3-small vs Voyage 3 Large
Detailed comparison between OpenAI text-embedding-3-small and Voyage 3 Large. See which embedding best meets your accuracy and performance needs.
Model Comparison
Voyage 3 Large takes the lead.
Both OpenAI text-embedding-3-small 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 Voyage 3 Large:
- Voyage 3 Large has 25 higher ELO rating
- Voyage 3 Large delivers better accuracy (nDCG@10: 0.837 vs 0.762)
- OpenAI text-embedding-3-small is 33519ms faster on average
- Voyage 3 Large has a 8.0% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
OpenAI text-embedding-3-small
Voyage 3 Large
Win Rate
Head-to-head performance
OpenAI text-embedding-3-small
Voyage 3 Large
Accuracy (nDCG@10)
Ranking quality metric
OpenAI text-embedding-3-small
Voyage 3 Large
Average Latency
Response time
OpenAI text-embedding-3-small
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-small | Voyage 3 Large | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1503 | 1528 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 44.6% | 52.6% | Percentage of comparisons won against other models |
| Pricing & Availability | |||
| Price per 1M tokens | $0.020 | $0.180 | Cost per million tokens processed |
| Release Date | 2024-01-25 | 2025-01-07 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.762 | 0.837 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 29958ms | 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-small | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 55877ms | 93447ms | Average response time |
| P50 | 54759ms | 91578ms | 50th percentile (median) |
| P90 | 64259ms | 107464ms | 90th percentile |
business reports
| Metric | OpenAI text-embedding-3-small | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 5645ms | 21279ms | Average response time |
| P50 | 5532ms | 20853ms | 50th percentile (median) |
| P90 | 6492ms | 24471ms | 90th percentile |
DBPedia
| Metric | OpenAI text-embedding-3-small | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.605 | 0.675 | Ranking quality at top 5 results |
| nDCG@10 | 0.604 | 0.638 | Ranking quality at top 10 results |
| Recall@5 | 0.230 | 0.255 | % of relevant docs in top 5 |
| Recall@10 | 0.365 | 0.362 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 35365ms | 97066ms | Average response time |
| P50 | 34658ms | 95125ms | 50th percentile (median) |
| P90 | 40670ms | 111626ms | 90th percentile |
FiQa
| Metric | OpenAI text-embedding-3-small | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.635 | 0.753 | Ranking quality at top 5 results |
| nDCG@10 | 0.647 | 0.780 | Ranking quality at top 10 results |
| Recall@5 | 0.623 | 0.742 | % of relevant docs in top 5 |
| Recall@10 | 0.681 | 0.837 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 41338ms | 80239ms | Average response time |
| P50 | 40511ms | 78634ms | 50th percentile (median) |
| P90 | 47539ms | 92275ms | 90th percentile |
SciFact
| Metric | OpenAI text-embedding-3-small | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.682 | 0.796 | Ranking quality at top 5 results |
| nDCG@10 | 0.707 | 0.809 | Ranking quality at top 10 results |
| Recall@5 | 0.778 | 0.840 | % of relevant docs in top 5 |
| Recall@10 | 0.843 | 0.880 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 55544ms | 92317ms | Average response time |
| P50 | 54433ms | 90471ms | 50th percentile (median) |
| P90 | 63876ms | 106165ms | 90th percentile |
MSMARCO
| Metric | OpenAI text-embedding-3-small | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.997 | 0.997 | Ranking quality at top 5 results |
| nDCG@10 | 0.990 | 0.998 | Ranking quality at top 10 results |
| Recall@5 | 0.122 | 0.122 | % of relevant docs in top 5 |
| Recall@10 | 0.213 | 0.222 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 35961ms | 75502ms | Average response time |
| P50 | 35242ms | 73992ms | 50th percentile (median) |
| P90 | 41355ms | 86827ms | 90th percentile |
NorQuAD
| Metric | OpenAI text-embedding-3-small | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 6467ms | 11003ms | Average response time |
| P50 | 6338ms | 10783ms | 50th percentile (median) |
| P90 | 7437ms | 12653ms | 90th percentile |
ARCD
| Metric | OpenAI text-embedding-3-small | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.855 | 0.953 | Ranking quality at top 5 results |
| nDCG@10 | 0.862 | 0.960 | Ranking quality at top 10 results |
| Recall@5 | 0.900 | 0.960 | % of relevant docs in top 5 |
| Recall@10 | 0.920 | 0.980 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 3464ms | 36963ms | Average response time |
| P50 | 3395ms | 36224ms | 50th percentile (median) |
| P90 | 3984ms | 42507ms | 90th percentile |
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