OpenAI text-embedding-3-large vs Voyage 3.5 Lite
Detailed comparison between OpenAI text-embedding-3-large and Voyage 3.5 Lite. 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 Lite 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 36 higher ELO rating
- OpenAI text-embedding-3-large is 3214ms faster on average
- OpenAI text-embedding-3-large has a 11.3% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
OpenAI text-embedding-3-large
Voyage 3.5 Lite
Win Rate
Head-to-head performance
OpenAI text-embedding-3-large
Voyage 3.5 Lite
Accuracy (nDCG@10)
Ranking quality metric
OpenAI text-embedding-3-large
Voyage 3.5 Lite
Average Latency
Response time
OpenAI text-embedding-3-large
Voyage 3.5 Lite
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 Lite | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1539 | 1503 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 55.7% | 44.4% | 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 | 2025-05-20 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.811 | 0.803 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 32922ms | 36136ms | 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 Lite | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 57366ms | 47120ms | Average response time |
| P50 | 56219ms | 46178ms | 50th percentile (median) |
| P90 | 65971ms | 54188ms | 90th percentile |
business reports
| Metric | OpenAI text-embedding-3-large | Voyage 3.5 Lite | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 8013ms | 5629ms | Average response time |
| P50 | 7853ms | 5516ms | 50th percentile (median) |
| P90 | 9215ms | 6473ms | 90th percentile |
DBPedia
| Metric | OpenAI text-embedding-3-large | Voyage 3.5 Lite | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.648 | 0.641 | Ranking quality at top 5 results |
| nDCG@10 | 0.641 | 0.632 | Ranking quality at top 10 results |
| Recall@5 | 0.255 | 0.219 | % of relevant docs in top 5 |
| Recall@10 | 0.377 | 0.367 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 42058ms | 32706ms | Average response time |
| P50 | 41217ms | 32052ms | 50th percentile (median) |
| P90 | 48367ms | 37612ms | 90th percentile |
FiQa
| Metric | OpenAI text-embedding-3-large | Voyage 3.5 Lite | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.730 | 0.708 | Ranking quality at top 5 results |
| nDCG@10 | 0.752 | 0.736 | Ranking quality at top 10 results |
| Recall@5 | 0.700 | 0.687 | % of relevant docs in top 5 |
| Recall@10 | 0.781 | 0.780 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 48635ms | 85143ms | Average response time |
| P50 | 47662ms | 83440ms | 50th percentile (median) |
| P90 | 55930ms | 97914ms | 90th percentile |
SciFact
| Metric | OpenAI text-embedding-3-large | Voyage 3.5 Lite | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.726 | 0.670 | Ranking quality at top 5 results |
| nDCG@10 | 0.761 | 0.719 | Ranking quality at top 10 results |
| Recall@5 | 0.768 | 0.718 | % of relevant docs in top 5 |
| Recall@10 | 0.863 | 0.843 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 55796ms | 58245ms | Average response time |
| P50 | 54680ms | 57080ms | 50th percentile (median) |
| P90 | 64165ms | 66982ms | 90th percentile |
MSMARCO
| Metric | OpenAI text-embedding-3-large | Voyage 3.5 Lite | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 1.000 | 0.994 | Ranking quality at top 5 results |
| nDCG@10 | 1.000 | 0.995 | 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 | 49482ms | Average response time |
| P50 | 41023ms | 48492ms | 50th percentile (median) |
| P90 | 48139ms | 56904ms | 90th percentile |
NorQuAD
| Metric | OpenAI text-embedding-3-large | Voyage 3.5 Lite | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 5613ms | 7081ms | Average response time |
| P50 | 5501ms | 6939ms | 50th percentile (median) |
| P90 | 6455ms | 8143ms | 90th percentile |
ARCD
| Metric | OpenAI text-embedding-3-large | Voyage 3.5 Lite | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.899 | 0.928 | Ranking quality at top 5 results |
| nDCG@10 | 0.899 | 0.935 | 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 | 3679ms | Average response time |
| P50 | 3955ms | 3605ms | 50th percentile (median) |
| P90 | 4641ms | 4231ms | 90th percentile |
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