OpenAI text-embedding-3-large vs Voyage 3.5
Detailed comparison between OpenAI text-embedding-3-large and Voyage 3.5. 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 3.5 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 24 higher ELO rating
- OpenAI text-embedding-3-large has a 6.9% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
OpenAI text-embedding-3-large
Voyage 3.5
Win Rate
Head-to-head performance
OpenAI text-embedding-3-large
Voyage 3.5
Accuracy (nDCG@10)
Ranking quality metric
OpenAI text-embedding-3-large
Voyage 3.5
Average Latency
Response time
OpenAI text-embedding-3-large
Voyage 3.5
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 3.5 | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1539 | 1515 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 55.7% | 48.8% | Percentage of comparisons won against other models |
| Pricing & Availability | |||
| Price per 1M tokens | $0.130 | $0.060 | Cost per million tokens processed |
| Dimensions | 3072 | 1024 | Vector embedding dimensions (lower is more efficient) |
| Release Date | 2024-01-25 | 2025-05-20 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.811 | 0.816 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 11ms | 13ms | 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 3.5 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 10ms | 10ms | Average response time |
| P50 | 10ms | 10ms | 50th percentile (median) |
| P90 | 11ms | 12ms | 90th percentile |
business reports
| Metric | OpenAI text-embedding-3-large | Voyage 3.5 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 10ms | 16ms | Average response time |
| P50 | 10ms | 15ms | 50th percentile (median) |
| P90 | 12ms | 18ms | 90th percentile |
DBPedia
| Metric | OpenAI text-embedding-3-large | Voyage 3.5 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.648 | 0.655 | Ranking quality at top 5 results |
| nDCG@10 | 0.641 | 0.637 | Ranking quality at top 10 results |
| Recall@5 | 0.255 | 0.246 | % of relevant docs in top 5 |
| Recall@10 | 0.377 | 0.366 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 8ms | 6ms | Average response time |
| P50 | 8ms | 6ms | 50th percentile (median) |
| P90 | 9ms | 7ms | 90th percentile |
FiQa
| Metric | OpenAI text-embedding-3-large | Voyage 3.5 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.730 | 0.721 | Ranking quality at top 5 results |
| nDCG@10 | 0.752 | 0.741 | Ranking quality at top 10 results |
| Recall@5 | 0.700 | 0.715 | % of relevant docs in top 5 |
| Recall@10 | 0.781 | 0.793 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 10ms | 9ms | Average response time |
| P50 | 9ms | 9ms | 50th percentile (median) |
| P90 | 11ms | 11ms | 90th percentile |
SciFact
| Metric | OpenAI text-embedding-3-large | Voyage 3.5 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.726 | 0.723 | Ranking quality at top 5 results |
| nDCG@10 | 0.761 | 0.751 | Ranking quality at top 10 results |
| Recall@5 | 0.768 | 0.778 | % of relevant docs in top 5 |
| Recall@10 | 0.863 | 0.853 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 11ms | 14ms | Average response time |
| P50 | 11ms | 13ms | 50th percentile (median) |
| P90 | 13ms | 16ms | 90th percentile |
MSMARCO
| Metric | OpenAI text-embedding-3-large | Voyage 3.5 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 1.000 | 1.000 | Ranking quality at top 5 results |
| nDCG@10 | 1.000 | 1.000 | Ranking quality at top 10 results |
| Recall@5 | 0.123 | 0.123 | % of relevant docs in top 5 |
| Recall@10 | 0.224 | 0.224 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 8ms | 10ms | Average response time |
| P50 | 8ms | 9ms | 50th percentile (median) |
| P90 | 9ms | 11ms | 90th percentile |
NorQuAD
| Metric | OpenAI text-embedding-3-large | Voyage 3.5 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 14ms | 20ms | Average response time |
| P50 | 14ms | 19ms | 50th percentile (median) |
| P90 | 16ms | 22ms | 90th percentile |
ARCD
| Metric | OpenAI text-embedding-3-large | Voyage 3.5 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.899 | 0.950 | Ranking quality at top 5 results |
| nDCG@10 | 0.899 | 0.950 | Ranking quality at top 10 results |
| Recall@5 | 0.940 | 0.980 | % of relevant docs in top 5 |
| Recall@10 | 0.940 | 0.980 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 14ms | 23ms | Average response time |
| P50 | 14ms | 23ms | 50th percentile (median) |
| P90 | 16ms | 27ms | 90th percentile |
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