Qwen3 Embedding 0.6B
Lightweight 600M parameter model supporting 100+ natural and programming languages for efficient processing. Optimized for high-volume applications including code retrieval, text classification, clustering, and bitext mining. 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
- $0.010
- Dimensions
- 1024
- Release Date
- 2025-06-06
- Model Name
- qwen3-embedding-0.6b
- Total Evaluations
- 830
Performance Record
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Performance Overview
ELO ratings by dataset
Qwen3 Embedding 0.6B's ELO performance varies across different benchmark datasets, showing its strengths in specific domains.
Qwen3 Embedding 0.6B - 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
- 21ms
- P50 (Median)
- 21ms
- P90
- 21ms
DBPedia
Accuracy Metrics
- nDCG@5
- 0.716
- nDCG@10
- 0.730
- Recall@5
- 0.053
- Recall@10
- 0.105
Latency Distribution
- Mean
- 13ms
- P50 (Median)
- 13ms
- P90
- 13ms
FiQa
Accuracy Metrics
- nDCG@5
- 0.755
- nDCG@10
- 0.755
- Recall@5
- 0.591
- Recall@10
- 0.683
Latency Distribution
- Mean
- 19ms
- P50 (Median)
- 19ms
- P90
- 19ms
SciFact
Accuracy Metrics
- nDCG@5
- 0.658
- nDCG@10
- 0.666
- Recall@5
- 0.718
- Recall@10
- 0.779
Latency Distribution
- Mean
- 62ms
- P50 (Median)
- 62ms
- P90
- 62ms
MSMARCO
Accuracy Metrics
- nDCG@5
- 0.943
- nDCG@10
- 0.933
- Recall@5
- 0.122
- Recall@10
- 0.215
Latency Distribution
- Mean
- 15ms
- P50 (Median)
- 15ms
- P90
- 15ms
ARCD
Accuracy Metrics
- nDCG@5
- 0.757
- nDCG@10
- 0.763
- Recall@5
- 0.880
- Recall@10
- 0.900
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 Qwen3 Embedding 0.6B with other top embeddings to understand the differences in performance, accuracy, and latency.