Voyage 3.5 vs Qwen3 Embedding 8B
Detailed comparison between Voyage 3.5 and Qwen3 Embedding 8B. 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 3.5 and Qwen3 Embedding 8B are powerful embedding models designed to improve retrieval quality in RAG applications. They show comparable performance across key metrics.
Key similarities:
- Similar ELO ratings (1515 vs 1516)
- Comparable accuracy metrics
- Similar latency characteristics
Overview
Key metrics
ELO Rating
Overall ranking quality
Voyage 3.5
Qwen3 Embedding 8B
Win Rate
Head-to-head performance
Voyage 3.5
Qwen3 Embedding 8B
Accuracy (nDCG@10)
Ranking quality metric
Voyage 3.5
Qwen3 Embedding 8B
Average Latency
Response time
Voyage 3.5
Qwen3 Embedding 8B
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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 3.5 | Qwen3 Embedding 8B | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1515 | 1516 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 48.8% | 47.9% | Percentage of comparisons won against other models |
| Pricing & Availability | |||
| Price per 1M tokens | $0.060 | $0.050 | Cost per million tokens processed |
| Dimensions | 1024 | 4096 | Vector embedding dimensions (lower is more efficient) |
| Release Date | 2025-05-20 | 2025-06-06 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.816 | 0.818 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 13ms | 56ms | 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.
PG
| Metric | Voyage 3.5 | Qwen3 Embedding 8B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 58887ms | 151470ms | Average response time |
| P50 | 57709ms | 148441ms | 50th percentile (median) |
| P90 | 67720ms | 174191ms | 90th percentile |
business reports
| Metric | Voyage 3.5 | Qwen3 Embedding 8B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| Latency Metrics | |||
| Mean | 16ms | 93874ms | Average response time |
| P50 | 16ms | 91997ms | 50th percentile (median) |
| P90 | 17ms | 107955ms | 90th percentile |
DBPedia
| Metric | Voyage 3.5 | Qwen3 Embedding 8B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.655 | 0.633 | Ranking quality at top 5 results |
| nDCG@10 | 0.637 | 0.625 | Ranking quality at top 10 results |
| Recall@5 | 0.246 | 0.235 | % of relevant docs in top 5 |
| Recall@10 | 0.366 | 0.381 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 6ms | 200373ms | Average response time |
| P50 | 6ms | 196366ms | 50th percentile (median) |
| P90 | 7ms | 230429ms | 90th percentile |
FiQa
| Metric | Voyage 3.5 | Qwen3 Embedding 8B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.721 | 0.760 | Ranking quality at top 5 results |
| nDCG@10 | 0.741 | 0.781 | Ranking quality at top 10 results |
| Recall@5 | 0.715 | 0.732 | % of relevant docs in top 5 |
| Recall@10 | 0.793 | 0.814 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 9ms | 189471ms | Average response time |
| P50 | 9ms | 185682ms | 50th percentile (median) |
| P90 | 10ms | 217892ms | 90th percentile |
SciFact
| Metric | Voyage 3.5 | Qwen3 Embedding 8B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.723 | 0.750 | Ranking quality at top 5 results |
| nDCG@10 | 0.751 | 0.765 | Ranking quality at top 10 results |
| Recall@5 | 0.778 | 0.843 | % of relevant docs in top 5 |
| Recall@10 | 0.853 | 0.883 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 14ms | 200031ms | Average response time |
| P50 | 14ms | 196030ms | 50th percentile (median) |
| P90 | 15ms | 230036ms | 90th percentile |
MSMARCO
| Metric | Voyage 3.5 | Qwen3 Embedding 8B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 1.000 | 1.000 | Ranking quality at top 5 results |
| nDCG@10 | 1.000 | 1.000 | Ranking quality at top 10 results |
| Recall@5 | 0.123 | 0.123 | % of relevant docs in top 5 |
| Recall@10 | 0.224 | 0.224 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 10ms | 180144ms | Average response time |
| P50 | 10ms | 176541ms | 50th percentile (median) |
| P90 | 10ms | 207166ms | 90th percentile |
ARCD
| Metric | Voyage 3.5 | Qwen3 Embedding 8B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.950 | 0.913 | Ranking quality at top 5 results |
| nDCG@10 | 0.950 | 0.919 | Ranking quality at top 10 results |
| Recall@5 | 0.980 | 0.920 | % of relevant docs in top 5 |
| Recall@10 | 0.980 | 0.940 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 25ms | 12156ms | Average response time |
| P50 | 25ms | 11913ms | 50th percentile (median) |
| P90 | 28ms | 13979ms | 90th percentile |
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