Voyage 3 Large vs BAAI/bge-m3
Detailed comparison between Voyage 3 Large and BAAI/bge-m3. 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 3 Large takes the lead.
Both Voyage 3 Large and BAAI/bge-m3 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 54 higher ELO rating
- BAAI/bge-m3 delivers better accuracy (nDCG@10: 0.674 vs 0.501)
- BAAI/bge-m3 is 238ms faster on average
- Voyage 3 Large has a 6.9% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
Voyage 3 Large
BAAI/bge-m3
Win Rate
Head-to-head performance
Voyage 3 Large
BAAI/bge-m3
Accuracy (nDCG@10)
Ranking quality metric
Voyage 3 Large
BAAI/bge-m3
Average Latency
Response time
Voyage 3 Large
BAAI/bge-m3
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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 3 Large | BAAI/bge-m3 | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1534 | 1480 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 51.3% | 44.3% | Percentage of comparisons won against other models |
| Pricing & Availability | |||
| Price per 1M tokens | $0.180 | $0.010 | Cost per million tokens processed |
| Dimensions | 1024 | 1024 | Vector embedding dimensions (lower is more efficient) |
| Release Date | 2025-01-07 | 2024-01-27 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.501 | 0.674 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 272ms | 34ms | Average response time across all datasets |
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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.
business reports
| Metric | Voyage 3 Large | BAAI/bge-m3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.000 | 0.000 | Ranking quality at top 5 results |
| nDCG@10 | 0.000 | 0.000 | Ranking quality at top 10 results |
| Recall@5 | 0.000 | 0.000 | % of relevant docs in top 5 |
| Recall@10 | 0.000 | 0.000 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 309ms | 27ms | Average response time |
| P50 | 309ms | 27ms | 50th percentile (median) |
| P90 | 309ms | 27ms | 90th percentile |
DBPedia
| Metric | Voyage 3 Large | BAAI/bge-m3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.801 | 0.801 | Ranking quality at top 5 results |
| nDCG@10 | 0.790 | 0.785 | Ranking quality at top 10 results |
| Recall@5 | 0.062 | 0.061 | % of relevant docs in top 5 |
| Recall@10 | 0.123 | 0.122 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 188ms | 21ms | Average response time |
| P50 | 188ms | 21ms | 50th percentile (median) |
| P90 | 188ms | 21ms | 90th percentile |
FiQa
| Metric | Voyage 3 Large | BAAI/bge-m3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.000 | 0.743 | Ranking quality at top 5 results |
| nDCG@10 | 0.000 | 0.755 | Ranking quality at top 10 results |
| Recall@5 | 0.000 | 0.608 | % of relevant docs in top 5 |
| Recall@10 | 0.000 | 0.667 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 319ms | 22ms | Average response time |
| P50 | 319ms | 22ms | 50th percentile (median) |
| P90 | 319ms | 22ms | 90th percentile |
SciFact
| Metric | Voyage 3 Large | BAAI/bge-m3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.766 | 0.571 | Ranking quality at top 5 results |
| nDCG@10 | 0.779 | 0.599 | Ranking quality at top 10 results |
| Recall@5 | 0.837 | 0.645 | % of relevant docs in top 5 |
| Recall@10 | 0.878 | 0.759 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 230ms | 37ms | Average response time |
| P50 | 230ms | 37ms | 50th percentile (median) |
| P90 | 230ms | 37ms | 90th percentile |
MSMARCO
| Metric | Voyage 3 Large | BAAI/bge-m3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.956 | 0.956 | Ranking quality at top 5 results |
| nDCG@10 | 0.942 | 0.941 | Ranking quality at top 10 results |
| Recall@5 | 0.122 | 0.121 | % of relevant docs in top 5 |
| Recall@10 | 0.221 | 0.219 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 251ms | 51ms | Average response time |
| P50 | 251ms | 51ms | 50th percentile (median) |
| P90 | 251ms | 51ms | 90th percentile |
ARCD
| Metric | Voyage 3 Large | BAAI/bge-m3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.898 | 0.879 | Ranking quality at top 5 results |
| nDCG@10 | 0.905 | 0.879 | Ranking quality at top 10 results |
| Recall@5 | 0.960 | 0.960 | % of relevant docs in top 5 |
| Recall@10 | 0.980 | 0.960 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 300ms | 48ms | Average response time |
| P50 | 300ms | 48ms | 50th percentile (median) |
| P90 | 300ms | 48ms | 90th percentile |
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