Voyage 3 Large vs Cohere Embed Multilingual v3
Detailed comparison between Voyage 3 Large and Cohere Embed Multilingual v3. See which embedding best meets your accuracy and performance needs. If you want to compare these models on your data, try Agentset.
Model Comparison
Two competitive embeddings, closely matched.
Both Voyage 3 Large and Cohere Embed Multilingual v3 are powerful embedding models designed to improve retrieval quality in RAG applications. They show comparable performance across key metrics.
Key similarities:
- Voyage 3 Large has 23 higher ELO rating
- Cohere Embed Multilingual v3 delivers better accuracy (nDCG@10: 0.701 vs 0.501)
- Cohere Embed Multilingual v3 is 265ms faster on average
Overview
Key metrics
ELO Rating
Overall ranking quality
Voyage 3 Large
Cohere Embed Multilingual v3
Win Rate
Head-to-head performance
Voyage 3 Large
Cohere Embed Multilingual v3
Accuracy (nDCG@10)
Ranking quality metric
Voyage 3 Large
Cohere Embed Multilingual v3
Average Latency
Response time
Voyage 3 Large
Cohere Embed Multilingual v3
Embedding Models Are Just One Piece of RAG
Agentset gives you a managed RAG pipeline with the top-ranked models and best practices baked in. No infrastructure to maintain, no embeddings to manage.
Trusted by teams building production RAG applications
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 | Cohere Embed Multilingual v3 | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1534 | 1512 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 51.3% | 48.4% | Percentage of comparisons won against other models |
| Pricing & Availability | |||
| Price per 1M tokens | $0.180 | $0.100 | Cost per million tokens processed |
| Dimensions | 1024 | 512 | Vector embedding dimensions (lower is more efficient) |
| Release Date | 2025-01-07 | 2024-02-07 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.501 | 0.701 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 272ms | 7ms | 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 | Voyage 3 Large | Cohere Embed Multilingual v3 | 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 | 8ms | Average response time |
| P50 | 309ms | 8ms | 50th percentile (median) |
| P90 | 309ms | 8ms | 90th percentile |
DBPedia
| Metric | Voyage 3 Large | Cohere Embed Multilingual v3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.801 | 0.786 | Ranking quality at top 5 results |
| nDCG@10 | 0.790 | 0.783 | 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 | 7ms | Average response time |
| P50 | 188ms | 7ms | 50th percentile (median) |
| P90 | 188ms | 7ms | 90th percentile |
FiQa
| Metric | Voyage 3 Large | Cohere Embed Multilingual v3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.000 | 0.804 | Ranking quality at top 5 results |
| nDCG@10 | 0.000 | 0.812 | Ranking quality at top 10 results |
| Recall@5 | 0.000 | 0.624 | % of relevant docs in top 5 |
| Recall@10 | 0.000 | 0.696 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 319ms | 7ms | Average response time |
| P50 | 319ms | 7ms | 50th percentile (median) |
| P90 | 319ms | 7ms | 90th percentile |
SciFact
| Metric | Voyage 3 Large | Cohere Embed Multilingual v3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.766 | 0.696 | Ranking quality at top 5 results |
| nDCG@10 | 0.779 | 0.702 | Ranking quality at top 10 results |
| Recall@5 | 0.837 | 0.804 | % of relevant docs in top 5 |
| Recall@10 | 0.878 | 0.830 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 230ms | 7ms | Average response time |
| P50 | 230ms | 7ms | 50th percentile (median) |
| P90 | 230ms | 7ms | 90th percentile |
MSMARCO
| Metric | Voyage 3 Large | Cohere Embed Multilingual v3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.956 | 0.952 | 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 | 0.218 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 251ms | 8ms | Average response time |
| P50 | 251ms | 8ms | 50th percentile (median) |
| P90 | 251ms | 8ms | 90th percentile |
ARCD
| Metric | Voyage 3 Large | Cohere Embed Multilingual v3 | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.898 | 0.868 | Ranking quality at top 5 results |
| nDCG@10 | 0.905 | 0.875 | Ranking quality at top 10 results |
| Recall@5 | 0.960 | 0.940 | % of relevant docs in top 5 |
| Recall@10 | 0.980 | 0.960 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 300ms | 7ms | Average response time |
| P50 | 300ms | 7ms | 50th percentile (median) |
| P90 | 300ms | 7ms | 90th percentile |
Explore More
Compare more embeddings
See how all embedding models stack up. Compare OpenAI, Cohere, Jina AI, Voyage, and more. View comprehensive benchmarks, compare performance metrics, and find the perfect embedding for your RAG application.