Voyage 4
Mid-sized model approaching the retrieval quality of voyage-3-large while maintaining mid-tier model efficiency. Part of shared embedding space with Voyage 4 family, enabling asymmetric retrieval. Supports Matryoshka learning (2048/1024/512/256 dims) and multiple quantization options. If you want to compare the best embedding models for your data, try Agentset.
Model Information
- Provider
- Voyage AI
- License
- Proprietary
- Price per 1M tokens
- $0.060
- Dimensions
- 1024
- Release Date
- 2026-01-15
- Model Name
- voyage-4
- Total Evaluations
- 1120
Performance Record
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Performance Overview
ELO ratings by dataset
Voyage 4's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
Voyage 4 - ELO by Dataset
Detailed Metrics
Dataset breakdown
Performance metrics across different benchmark datasets, including accuracy and latency percentiles.
PG
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 311ms
- P50 (Median)
- 309ms
- P90
- 321ms
business reports
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 309ms
- P50 (Median)
- 310ms
- P90
- 325ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.815
- nDCG@10
- 0.811
- Recall@5
- 0.062
- Recall@10
- 0.122
Latency Distribution
- Mean
- 327ms
- P50 (Median)
- 312ms
- P90
- 357ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.873
- nDCG@10
- 0.859
- Recall@5
- 0.763
- Recall@10
- 0.840
Latency Distribution
- Mean
- 310ms
- P50 (Median)
- 311ms
- P90
- 324ms
SciFact
Accuracy Metrics
- nDCG@5
- 0.737
- nDCG@10
- 0.758
- Recall@5
- 0.804
- Recall@10
- 0.878
Latency Distribution
- Mean
- 321ms
- P50 (Median)
- 311ms
- P90
- 331ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.941
- nDCG@10
- 0.931
- Recall@5
- 0.123
- Recall@10
- 0.221
Latency Distribution
- Mean
- 317ms
- P50 (Median)
- 307ms
- P90
- 323ms
ARCD
Accuracy Metrics
- nDCG@5
- 0.916
- nDCG@10
- 0.916
- Recall@5
- 0.980
- Recall@10
- 0.980
Latency Distribution
- Mean
- 477ms
- P50 (Median)
- 310ms
- P90
- 331ms
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);
}Compare Models
See how it stacks up
Compare Voyage 4 with other top embeddings to understand the differences in performance, accuracy, and latency.