Jina Embeddings v3 vs Voyage 3 Large
Detailed comparison between Jina Embeddings v3 and Voyage 3 Large. 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 Jina Embeddings v3 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 120 higher ELO rating
- Jina Embeddings v3 delivers better accuracy (nDCG@10: 0.674 vs 0.501)
- Voyage 3 Large has a 16.7% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
Jina Embeddings v3
Voyage 3 Large
Win Rate
Head-to-head performance
Jina Embeddings v3
Voyage 3 Large
Accuracy (nDCG@10)
Ranking quality metric
Jina Embeddings v3
Voyage 3 Large
Average Latency
Response time
Jina Embeddings v3
Voyage 3 Large
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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 | Jina Embeddings v3 | Voyage 3 Large | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1414 | 1534 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 34.6% | 51.3% | Percentage of comparisons won against other models |
| Pricing & Availability | |||
| Price per 1M tokens | $0.045 | $0.180 | Cost per million tokens processed |
| Dimensions | 1024 | 1024 | Vector embedding dimensions (lower is more efficient) |
| Release Date | 2024-09-18 | 2025-01-07 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.674 | 0.501 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 223ms | 272ms | 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.
business reports
| Metric | Jina Embeddings v3 | Voyage 3 Large | 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 | 26ms | 309ms | Average response time |
| P50 | 26ms | 309ms | 50th percentile (median) |
| P90 | 26ms | 309ms | 90th percentile |
DBPedia
| Metric | Jina Embeddings v3 | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.835 | 0.801 | Ranking quality at top 5 results |
| nDCG@10 | 0.789 | 0.790 | Ranking quality at top 10 results |
| Recall@5 | 0.062 | 0.062 | % of relevant docs in top 5 |
| Recall@10 | 0.121 | 0.123 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 107ms | 188ms | Average response time |
| P50 | 107ms | 188ms | 50th percentile (median) |
| P90 | 107ms | 188ms | 90th percentile |
FiQa
| Metric | Jina Embeddings v3 | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.764 | 0.000 | Ranking quality at top 5 results |
| nDCG@10 | 0.775 | 0.000 | Ranking quality at top 10 results |
| Recall@5 | 0.635 | 0.000 | % of relevant docs in top 5 |
| Recall@10 | 0.745 | 0.000 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 273ms | 319ms | Average response time |
| P50 | 273ms | 319ms | 50th percentile (median) |
| P90 | 273ms | 319ms | 90th percentile |
SciFact
| Metric | Jina Embeddings v3 | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.600 | 0.766 | Ranking quality at top 5 results |
| nDCG@10 | 0.636 | 0.779 | Ranking quality at top 10 results |
| Recall@5 | 0.709 | 0.837 | % of relevant docs in top 5 |
| Recall@10 | 0.816 | 0.878 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 75ms | 230ms | Average response time |
| P50 | 75ms | 230ms | 50th percentile (median) |
| P90 | 75ms | 230ms | 90th percentile |
MSMARCO
| Metric | Jina Embeddings v3 | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.958 | 0.956 | Ranking quality at top 5 results |
| nDCG@10 | 0.944 | 0.942 | Ranking quality at top 10 results |
| Recall@5 | 0.124 | 0.122 | % of relevant docs in top 5 |
| Recall@10 | 0.219 | 0.221 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 346ms | 251ms | Average response time |
| P50 | 346ms | 251ms | 50th percentile (median) |
| P90 | 346ms | 251ms | 90th percentile |
ARCD
| Metric | Jina Embeddings v3 | Voyage 3 Large | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.797 | 0.898 | Ranking quality at top 5 results |
| nDCG@10 | 0.809 | 0.905 | Ranking quality at top 10 results |
| Recall@5 | 0.920 | 0.960 | % of relevant docs in top 5 |
| Recall@10 | 0.960 | 0.980 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 513ms | 300ms | Average response time |
| P50 | 513ms | 300ms | 50th percentile (median) |
| P90 | 513ms | 300ms | 90th percentile |
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