Voyage 4 vs OpenAI text-embedding-3-small
Detailed comparison between Voyage 4 and OpenAI text-embedding-3-small. See which embedding best meets your accuracy and performance needs. If you want to compare these models on your data, try Agentset.
Model Comparison
Voyage 4 takes the lead.
Both Voyage 4 and OpenAI text-embedding-3-small are powerful embedding models designed to improve retrieval quality in RAG applications. However, their performance characteristics differ in important ways.
Why Voyage 4:
- Voyage 4 has 103 higher ELO rating
- Voyage 4 delivers better accuracy (nDCG@10: 0.859 vs 0.762)
- Voyage 4 has a 17.1% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
Voyage 4
OpenAI text-embedding-3-small
Win Rate
Head-to-head performance
Voyage 4
OpenAI text-embedding-3-small
Accuracy (nDCG@10)
Ranking quality metric
Voyage 4
OpenAI text-embedding-3-small
Average Latency
Response time
Voyage 4
OpenAI text-embedding-3-small
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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 | Voyage 4 | OpenAI text-embedding-3-small | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1606 | 1503 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 61.7% | 44.6% | Percentage of comparisons won against other models |
| Pricing & Availability | |||
| Price per 1M tokens | $0.060 | $0.020 | Cost per million tokens processed |
| Dimensions | 1024 | 1536 | Vector embedding dimensions (lower is more efficient) |
| Release Date | 2026-01-15 | 2024-01-25 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.859 | 0.762 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 17ms | 10ms | Average response time across all datasets |
Build RAG in Minutes, Not Months
Agentset gives you a complete RAG API with top-ranked embedding models and smart retrieval built in. Upload your data, call the API, and get accurate results from day one.
import { Agentset } from "agentset";
const agentset = new Agentset();
const ns = agentset.namespace("ns_1234");
const results = await ns.search(
"What is multi-head attention?"
);
for (const result of results) {
console.log(result.text);
}Dataset Performance
By field
Comprehensive comparison of accuracy metrics (nDCG, Recall) and latency percentiles for each benchmark dataset.
PG
| Metric | Voyage 4 | OpenAI text-embedding-3-small | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 17ms | 9ms | Average response time |
| P50 | 17ms | 9ms | 50th percentile (median) |
| P90 | 19ms | 11ms | 90th percentile |
business reports
| Metric | Voyage 4 | OpenAI text-embedding-3-small | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 15ms | 7ms | Average response time |
| P50 | 15ms | 7ms | 50th percentile (median) |
| P90 | 17ms | 8ms | 90th percentile |
DBPedia
| Metric | Voyage 4 | OpenAI text-embedding-3-small | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.815 | 0.605 | Ranking quality at top 5 results |
| nDCG@10 | 0.811 | 0.604 | Ranking quality at top 10 results |
| Recall@5 | 0.062 | 0.230 | % of relevant docs in top 5 |
| Recall@10 | 0.122 | 0.365 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 13ms | 7ms | Average response time |
| P50 | 13ms | 7ms | 50th percentile (median) |
| P90 | 15ms | 8ms | 90th percentile |
FiQa
| Metric | Voyage 4 | OpenAI text-embedding-3-small | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.873 | 0.635 | Ranking quality at top 5 results |
| nDCG@10 | 0.859 | 0.647 | Ranking quality at top 10 results |
| Recall@5 | 0.763 | 0.623 | % of relevant docs in top 5 |
| Recall@10 | 0.840 | 0.681 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 14ms | 8ms | Average response time |
| P50 | 14ms | 8ms | 50th percentile (median) |
| P90 | 15ms | 9ms | 90th percentile |
SciFact
| Metric | Voyage 4 | OpenAI text-embedding-3-small | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.737 | 0.682 | Ranking quality at top 5 results |
| nDCG@10 | 0.758 | 0.707 | Ranking quality at top 10 results |
| Recall@5 | 0.804 | 0.778 | % of relevant docs in top 5 |
| Recall@10 | 0.878 | 0.843 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 16ms | 11ms | Average response time |
| P50 | 16ms | 11ms | 50th percentile (median) |
| P90 | 18ms | 13ms | 90th percentile |
MSMARCO
| Metric | Voyage 4 | OpenAI text-embedding-3-small | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.941 | 0.997 | Ranking quality at top 5 results |
| nDCG@10 | 0.931 | 0.990 | Ranking quality at top 10 results |
| Recall@5 | 0.123 | 0.122 | % of relevant docs in top 5 |
| Recall@10 | 0.221 | 0.213 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 13ms | 7ms | Average response time |
| P50 | 13ms | 7ms | 50th percentile (median) |
| P90 | 14ms | 8ms | 90th percentile |
ARCD
| Metric | Voyage 4 | OpenAI text-embedding-3-small | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.936 | 0.855 | Ranking quality at top 5 results |
| nDCG@10 | 0.936 | 0.862 | Ranking quality at top 10 results |
| Recall@5 | 1.000 | 0.900 | % of relevant docs in top 5 |
| Recall@10 | 1.000 | 0.920 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 28ms | 11ms | Average response time |
| P50 | 28ms | 10ms | 50th percentile (median) |
| P90 | 30ms | 12ms | 90th percentile |
Explore More
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