Voyage 3 Large
Features 32K token context length with Matryoshka learning for flexible sizing. Offers binary quantization achieving up to 200x storage cost reduction with minimal quality loss. 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
- $0.180
- Dimensions
- 1024
- Release Date
- 2025-01-07
- Model Name
- voyage-3-large
- Total Evaluations
- 860
Performance Record
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Performance Overview
ELO ratings by dataset
Voyage 3 Large's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
Voyage 3 Large - 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
- 307ms
- P50 (Median)
- 307ms
- P90
- 307ms
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)
- 309ms
- P90
- 309ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.801
- nDCG@10
- 0.790
- Recall@5
- 0.062
- Recall@10
- 0.123
Latency Distribution
- Mean
- 188ms
- P50 (Median)
- 188ms
- P90
- 188ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 319ms
- P50 (Median)
- 319ms
- P90
- 319ms
SciFact
Accuracy Metrics
- nDCG@5
- 0.766
- nDCG@10
- 0.779
- Recall@5
- 0.837
- Recall@10
- 0.878
Latency Distribution
- Mean
- 230ms
- P50 (Median)
- 230ms
- P90
- 230ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.956
- nDCG@10
- 0.942
- Recall@5
- 0.122
- Recall@10
- 0.221
Latency Distribution
- Mean
- 251ms
- P50 (Median)
- 251ms
- P90
- 251ms
ARCD
Accuracy Metrics
- nDCG@5
- 0.898
- nDCG@10
- 0.905
- Recall@5
- 0.960
- Recall@10
- 0.980
Latency Distribution
- Mean
- 300ms
- P50 (Median)
- 300ms
- P90
- 300ms
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 3 Large with other top embeddings to understand the differences in performance, accuracy, and latency.