OpenAI text-embedding-3-small vs Qwen3 Embedding 4B
Detailed comparison between OpenAI text-embedding-3-small and Qwen3 Embedding 4B. See which embedding best meets your accuracy and performance needs. If you want to compare these models on your data, try Agentset.
Model Comparison
Qwen3 Embedding 4B takes the lead.
Both OpenAI text-embedding-3-small and Qwen3 Embedding 4B are powerful embedding models designed to improve retrieval quality in RAG applications. However, their performance characteristics differ in important ways.
Why Qwen3 Embedding 4B:
- Qwen3 Embedding 4B delivers better accuracy (nDCG@10: 0.705 vs 0.689)
Overview
Key metrics
ELO Rating
Overall ranking quality
OpenAI text-embedding-3-small
Qwen3 Embedding 4B
Win Rate
Head-to-head performance
OpenAI text-embedding-3-small
Qwen3 Embedding 4B
Accuracy (nDCG@10)
Ranking quality metric
OpenAI text-embedding-3-small
Qwen3 Embedding 4B
Average Latency
Response time
OpenAI text-embedding-3-small
Qwen3 Embedding 4B
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
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 | OpenAI text-embedding-3-small | Qwen3 Embedding 4B | Description |
|---|---|---|---|
| Overall Performance | |||
| ELO Rating | 1480 | 1482 | Overall ranking quality based on pairwise comparisons |
| Win Rate | 43.9% | 44.6% | Percentage of comparisons won against other models |
| Pricing & Availability | |||
| Price per 1M tokens | $0.020 | $0.020 | Cost per million tokens processed |
| Dimensions | 1536 | 2560 | Vector embedding dimensions (lower is more efficient) |
| Release Date | 2024-01-25 | 2025-06-06 | Model release date |
| Accuracy Metrics | |||
| Avg nDCG@10 | 0.689 | 0.705 | Normalized discounted cumulative gain at position 10 |
| Performance Metrics | |||
| Avg Latency | 15ms | 29ms | 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.
business reports
| Metric | OpenAI text-embedding-3-small | Qwen3 Embedding 4B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.000 | 0.000 | Ranking quality at top 5 results |
| nDCG@10 | 0.000 | 0.000 | Ranking quality at top 10 results |
| Recall@5 | 0.000 | 0.000 | % of relevant docs in top 5 |
| Recall@10 | 0.000 | 0.000 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 16ms | 29ms | Average response time |
| P50 | 16ms | 29ms | 50th percentile (median) |
| P90 | 16ms | 29ms | 90th percentile |
DBPedia
| Metric | OpenAI text-embedding-3-small | Qwen3 Embedding 4B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.858 | 0.799 | Ranking quality at top 5 results |
| nDCG@10 | 0.807 | 0.787 | Ranking quality at top 10 results |
| Recall@5 | 0.062 | 0.061 | % of relevant docs in top 5 |
| Recall@10 | 0.123 | 0.119 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 9ms | 26ms | Average response time |
| P50 | 9ms | 26ms | 50th percentile (median) |
| P90 | 9ms | 26ms | 90th percentile |
FiQa
| Metric | OpenAI text-embedding-3-small | Qwen3 Embedding 4B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.801 | 0.838 | Ranking quality at top 5 results |
| nDCG@10 | 0.814 | 0.836 | Ranking quality at top 10 results |
| Recall@5 | 0.624 | 0.719 | % of relevant docs in top 5 |
| Recall@10 | 0.682 | 0.839 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 16ms | 23ms | Average response time |
| P50 | 16ms | 23ms | 50th percentile (median) |
| P90 | 16ms | 23ms | 90th percentile |
SciFact
| Metric | OpenAI text-embedding-3-small | Qwen3 Embedding 4B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.663 | 0.666 | Ranking quality at top 5 results |
| nDCG@10 | 0.684 | 0.697 | Ranking quality at top 10 results |
| Recall@5 | 0.774 | 0.782 | % of relevant docs in top 5 |
| Recall@10 | 0.840 | 0.891 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 17ms | 38ms | Average response time |
| P50 | 17ms | 38ms | 50th percentile (median) |
| P90 | 17ms | 38ms | 90th percentile |
MSMARCO
| Metric | OpenAI text-embedding-3-small | Qwen3 Embedding 4B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.959 | 0.974 | Ranking quality at top 5 results |
| nDCG@10 | 0.946 | 0.954 | Ranking quality at top 10 results |
| Recall@5 | 0.122 | 0.124 | % of relevant docs in top 5 |
| Recall@10 | 0.212 | 0.224 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 20ms | 31ms | Average response time |
| P50 | 20ms | 31ms | 50th percentile (median) |
| P90 | 20ms | 31ms | 90th percentile |
ARCD
| Metric | OpenAI text-embedding-3-small | Qwen3 Embedding 4B | Description |
|---|---|---|---|
| Accuracy Metrics | |||
| nDCG@5 | 0.786 | 0.857 | Ranking quality at top 5 results |
| nDCG@10 | 0.793 | 0.864 | Ranking quality at top 10 results |
| Recall@5 | 0.900 | 0.940 | % of relevant docs in top 5 |
| Recall@10 | 0.920 | 0.960 | % of relevant docs in top 10 |
| Latency Metrics | |||
| Mean | 15ms | 25ms | Average response time |
| P50 | 15ms | 25ms | 50th percentile (median) |
| P90 | 15ms | 25ms | 90th percentile |
Explore More
Compare more embeddings
See how all embedding models stack up. Compare OpenAI, Cohere, Jina AI, Voyage, and more. View comprehensive benchmarks, compare performance metrics, and find the perfect embedding for your RAG application.