Voyage 3.5 Lite
Most cost-optimized variant at $0.02 per 1M tokens achieving retrieval quality within 0.3% of Cohere-v4 at 1/6 the cost. Supports quantization options including 32-bit, int8, and binary precision for storage efficiency. 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
- $0.020
- Dimensions
- 512
- Release Date
- 2025-05-20
- Model Name
- voyage-3.5-lite
- Total Evaluations
- 830
Performance Record
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Performance Overview
ELO ratings by dataset
Voyage 3.5 Lite's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
Voyage 3.5 Lite - 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
- 54ms
- P50 (Median)
- 54ms
- P90
- 54ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.793
- nDCG@10
- 0.787
- Recall@5
- 0.061
- Recall@10
- 0.120
Latency Distribution
- Mean
- 7ms
- P50 (Median)
- 7ms
- P90
- 7ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.812
- nDCG@10
- 0.796
- Recall@5
- 0.718
- Recall@10
- 0.796
Latency Distribution
- Mean
- 12ms
- P50 (Median)
- 12ms
- P90
- 12ms
SciFact
Accuracy Metrics
- nDCG@5
- 0.704
- nDCG@10
- 0.726
- Recall@5
- 0.774
- Recall@10
- 0.850
Latency Distribution
- Mean
- 9ms
- P50 (Median)
- 9ms
- P90
- 9ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.965
- nDCG@10
- 0.944
- Recall@5
- 0.123
- Recall@10
- 0.223
Latency Distribution
- Mean
- 15ms
- P50 (Median)
- 15ms
- P90
- 15ms
ARCD
Accuracy Metrics
- nDCG@5
- 0.874
- nDCG@10
- 0.874
- Recall@5
- 0.980
- Recall@10
- 0.980
Latency Distribution
- Mean
- 18ms
- P50 (Median)
- 18ms
- P90
- 18ms
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 Lite with other top embeddings to understand the differences in performance, accuracy, and latency.