Voyage 3.5
Distilled from voyage-3-large for cost efficiency with flexible sizing options. Achieves 99% vector database cost reduction through binary quantization while maintaining strong retrieval quality. 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
- 2025-05-20
- Model Name
- voyage-3.5
- Total Evaluations
- 830
Performance Record
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
Performance Overview
ELO ratings by dataset
Voyage 3.5's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
Voyage 3.5 - ELO by Dataset
Detailed Metrics
Dataset breakdown
Performance metrics across different benchmark datasets, including accuracy and latency percentiles.
business reports
Accuracy Metrics
- nDCG@5
- 0.000
- nDCG@10
- 0.000
- Recall@5
- 0.000
- Recall@10
- 0.000
Latency Distribution
- Mean
- 16ms
- P50 (Median)
- 16ms
- P90
- 16ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.783
- nDCG@10
- 0.782
- Recall@5
- 0.062
- Recall@10
- 0.121
Latency Distribution
- Mean
- 7ms
- P50 (Median)
- 7ms
- P90
- 7ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.848
- nDCG@10
- 0.825
- Recall@5
- 0.688
- Recall@10
- 0.783
Latency Distribution
- Mean
- 63ms
- P50 (Median)
- 63ms
- P90
- 63ms
SciFact
Accuracy Metrics
- nDCG@5
- 0.669
- nDCG@10
- 0.705
- Recall@5
- 0.733
- Recall@10
- 0.840
Latency Distribution
- Mean
- 7ms
- P50 (Median)
- 7ms
- P90
- 7ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.958
- nDCG@10
- 0.944
- Recall@5
- 0.122
- Recall@10
- 0.221
Latency Distribution
- Mean
- 6ms
- P50 (Median)
- 6ms
- P90
- 6ms
ARCD
Accuracy Metrics
- nDCG@5
- 0.867
- nDCG@10
- 0.873
- Recall@5
- 0.960
- Recall@10
- 0.980
Latency Distribution
- Mean
- 8ms
- P50 (Median)
- 8ms
- P90
- 8ms
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 3.5 with other top embeddings to understand the differences in performance, accuracy, and latency.