Qwen3 Embedding 4B
Mid-size 4 billion parameter model with strong multilingual capabilities across 100+ languages. Supports user-defined instructions for task-specific optimization in text retrieval, classification, and clustering applications. If you want to compare the best embedding models for your data, try Agentset.
Model Information
- Provider
- Qwen
- License
- Open Source
- Price per 1M tokens
- $0.020
- Dimensions
- 2560
- Release Date
- 2025-06-06
- Model Name
- qwen3-embedding-4b
- Total Evaluations
- 830
Performance Record
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Performance Overview
ELO ratings by dataset
Qwen3 Embedding 4B's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
Qwen3 Embedding 4B - 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
- 29ms
- P50 (Median)
- 29ms
- P90
- 29ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.799
- nDCG@10
- 0.787
- Recall@5
- 0.061
- Recall@10
- 0.119
Latency Distribution
- Mean
- 26ms
- P50 (Median)
- 26ms
- P90
- 26ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.838
- nDCG@10
- 0.836
- Recall@5
- 0.719
- Recall@10
- 0.839
Latency Distribution
- Mean
- 23ms
- P50 (Median)
- 23ms
- P90
- 23ms
SciFact
Accuracy Metrics
- nDCG@5
- 0.666
- nDCG@10
- 0.697
- Recall@5
- 0.782
- Recall@10
- 0.891
Latency Distribution
- Mean
- 38ms
- P50 (Median)
- 38ms
- P90
- 38ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.974
- nDCG@10
- 0.954
- Recall@5
- 0.124
- Recall@10
- 0.224
Latency Distribution
- Mean
- 31ms
- P50 (Median)
- 31ms
- P90
- 31ms
ARCD
Accuracy Metrics
- nDCG@5
- 0.857
- nDCG@10
- 0.864
- Recall@5
- 0.940
- Recall@10
- 0.960
Latency Distribution
- Mean
- 25ms
- P50 (Median)
- 25ms
- P90
- 25ms
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 Qwen3 Embedding 4B with other top embeddings to understand the differences in performance, accuracy, and latency.