Gemini text-embedding-004 vs Voyage 4
Detailed comparison between Gemini text-embedding-004 and Voyage 4. 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 Gemini text-embedding-004 and Voyage 4 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 220 higher ELO rating
- Voyage 4 delivers better accuracy (nDCG@10: 0.624 vs 0.538)
- Gemini text-embedding-004 is 323ms faster on average
- Voyage 4 has a 28.6% higher win rate
Overview
Key metrics
ELO Rating
Overall ranking quality
Gemini text-embedding-004
Voyage 4
Win Rate
Head-to-head performance
Gemini text-embedding-004
Voyage 4
Accuracy (nDCG@10)
Ranking quality metric
Gemini text-embedding-004
Voyage 4
Average Latency
Response time
Gemini text-embedding-004
Voyage 4
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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 | Gemini text-embedding-004 | Voyage 4 | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1366 | 1586 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 28.4% | 57.0% | Percentage of comparisons won against other models |
| Pricing & Availability | |||
| Price per 1M tokens | $0.020 | $0.060 | Cost per million tokens processed |
| Dimensions | 768 | 1024 | Vector embedding dimensions (lower is more efficient) |
| Release Date | 2024-05-14 | 2026-01-15 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.538 | 0.624 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 16ms | 339ms | 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 | Gemini text-embedding-004 | Voyage 4 | 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 | 15ms | 309ms | Average response time |
| P50 | 15ms | 310ms | 50th percentile (median) |
| P90 | 15ms | 325ms | 90th percentile |
DBPedia
| Metric | Gemini text-embedding-004 | Voyage 4 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.747 | 0.815 | Ranking quality at top 5 results |
| nDCG@10 | 0.737 | 0.811 | Ranking quality at top 10 results |
| Recall@5 | 0.057 | 0.062 | % of relevant docs in top 5 |
| Recall@10 | 0.108 | 0.122 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 14ms | 327ms | Average response time |
| P50 | 14ms | 312ms | 50th percentile (median) |
| P90 | 14ms | 357ms | 90th percentile |
FiQa
| Metric | Gemini text-embedding-004 | Voyage 4 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.744 | 0.873 | Ranking quality at top 5 results |
| nDCG@10 | 0.730 | 0.859 | Ranking quality at top 10 results |
| Recall@5 | 0.647 | 0.763 | % of relevant docs in top 5 |
| Recall@10 | 0.752 | 0.840 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 16ms | 310ms | Average response time |
| P50 | 16ms | 311ms | 50th percentile (median) |
| P90 | 16ms | 324ms | 90th percentile |
SciFact
| Metric | Gemini text-embedding-004 | Voyage 4 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.728 | 0.737 | Ranking quality at top 5 results |
| nDCG@10 | 0.729 | 0.758 | Ranking quality at top 10 results |
| Recall@5 | 0.813 | 0.804 | % of relevant docs in top 5 |
| Recall@10 | 0.857 | 0.878 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 15ms | 321ms | Average response time |
| P50 | 15ms | 311ms | 50th percentile (median) |
| P90 | 15ms | 331ms | 90th percentile |
MSMARCO
| Metric | Gemini text-embedding-004 | Voyage 4 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.932 | 0.941 | Ranking quality at top 5 results |
| nDCG@10 | 0.918 | 0.931 | Ranking quality at top 10 results |
| Recall@5 | 0.117 | 0.123 | % of relevant docs in top 5 |
| Recall@10 | 0.208 | 0.221 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 18ms | 317ms | Average response time |
| P50 | 18ms | 307ms | 50th percentile (median) |
| P90 | 18ms | 323ms | 90th percentile |
ARCD
| Metric | Gemini text-embedding-004 | Voyage 4 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.021 | 0.916 | Ranking quality at top 5 results |
| nDCG@10 | 0.027 | 0.916 | Ranking quality at top 10 results |
| Recall@5 | 0.040 | 0.980 | % of relevant docs in top 5 |
| Recall@10 | 0.060 | 0.980 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 15ms | 477ms | Average response time |
| P50 | 15ms | 310ms | 50th percentile (median) |
| P90 | 15ms | 331ms | 90th percentile |
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