BAAI/bge-m3 vs Qwen3 Embedding 0.6B

Detailed comparison between BAAI/bge-m3 and Qwen3 Embedding 0.6B. See which embedding best meets your accuracy and performance needs. If you want to compare these models on your data, try Agentset.

Model Comparison

BAAI/bge-m3 takes the lead.

Both BAAI/bge-m3 and Qwen3 Embedding 0.6B are powerful embedding models designed to improve retrieval quality in RAG applications. However, their performance characteristics differ in important ways.

Why BAAI/bge-m3:

• BAAI/bge-m3 has 60 higher ELO rating
• BAAI/bge-m3 delivers better accuracy (nDCG@10: 0.674 vs 0.656)
• BAAI/bge-m3 has a 8.7% higher win rate

Overview

Key metrics

ELO Rating

Overall ranking quality

Win Rate

Head-to-head performance

Accuracy (nDCG@10)

Ranking quality metric

Average Latency

Response time

Visual Performance Analysis

Performance

ELO Rating Comparison

Qwen3 Embedding 0.6B BAAI/bge-m3 4000 8000 12000 15000

Win/Loss/Tie Breakdown

• BAAI/bge-m3
• Qwen3 Embedding 0.6B

Accuracy Across Datasets (nDCG@10)

• BAAI/bge-m3
• Qwen3 Embedding 0.6B

DBPedia FiQa SciFact MSMARCO ARCD 0.25 0.50 0.75 nDCG@10

Latency Distribution (ms)

• BAAI/bge-m3
• Qwen3 Embedding 0.6B

Mean P50 P90 918 2736 Latency (ms)

Breakdown

How the models stack up

Dataset Performance

By field

Comprehensive comparison of accuracy metrics (nDCG, Recall) and latency percentiles for each benchmark dataset.

business reports

DBPedia

FiQa

SciFact

MSMARCO

ARCD

Code Example

TypeScript

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);
}

Python

# Python example to query the models
# Add your implementation here