Comparison of mainstream embedding models

Written by

Iris Vance

Updated on:June-29th-2025

1. Comparison of mainstream Embedding models

Model Name	Core Features	Advantages of Chinese scenarios	Performance Indicators	Applicable scenarios
BGE-M3	- Multi-language support (covering 194 languages) - Supports 8192 tokens long text - Integrated dense/sparse/hybrid retrieval	The average Chinese STS score was 83.54, with outstanding long text comprehension ability	- Response delay 28ms (RTX3090) - First hit rate increased by 42%	Cross-language search, technical documents, legal texts
M3E	- Special optimization for Chinese and English - Lightweight design (model volume is only 60% of BGE-M3)	The recall rate of Chinese question-answering scenarios is 18% higher than that of general models	- Inference speed 35ms - Memory usage is only 3.2GB	Lightweight deployment, edge computing, short text interaction
DeepSeek-R1	- Homologous to DeepSeek LLM - General scene baseline model	The average accuracy rate of basic question-answering scenarios is 67%	- 512 tokens takes 22ms - The accuracy of long text retrieval is significantly reduced	Rapid prototyping, non-professional field Q&A
Nomic-Embed-Text	- Open source and free - Supports 32K tokens long window	Chinese semantics capture is weaker than BGE-M3 (15-20% lower in testing)	- Long text processing takes 42ms - Professional field recall rate is only 58%	Academic research, low-cost multilingual experiments

2. In-depth analysis of key dimensions

Language support • BGE-M3 performs best in cross-language alignment, especially in semantic association of mixed Chinese, Japanese and Korean texts • M3E handles mixed Chinese and English content (such as code comments in technical documents) more accurately
Long text processing • BGE-M3 uses a hierarchical attention mechanism to maintain semantic coherence within 8192 tokens (tests show that the recall rate of 5000+ tokens documents is 28% higher than Nomic) • Although Nomic-Embed-Text supports longer windows, the error rate of Chinese paragraph boundary detection is as high as 12%
Domain adaptability • Legal/medical domain : BGE-M3 can improve the recall rate of professional terms from 71% to 89% through fine-tuning • Financial data : M3E’s vector mapping error for table values is 0.08 lower than BGE-M3 (cosine similarity)

Hardware Requirements

Model	Video memory usage (FP16)	Quantification compatibility	CPU inference speed (i9-13900K)
BGE-M3	6.8GB	Support 4bit/8bit quantization	78ms/token
M3E	3.2GB	Only supports 8-bit quantization	45ms/token
DeepSeek-R1	5.1GB	No support for quantization	62ms/token

3. Comparison of measured cases

Government document retrieval scenario : • Test data : 100,000 PDF/Word files (average length 1200 tokens) • Result comparison :

Model	MAP@10	First hit rate	Missed detection rate of long documents
BGE-M3	0.79	83%	7%
M3E	0.68	75%	15%
DeepSeek-R1	0.52	61%	twenty two%

Technical manual Q&A scenario : • The accuracy of the BGE-M3+DeepSeek combination is 31% higher than that of pure DeepSeek, and the response delay is only increased by 5ms

4. Selection Suggestions

BGE-M3 is preferred : • Need to process multi-language mixed content • Document length exceeds 2000 tokens • High data security requirements (local deployment)
Consider M3E : • Limited hardware resources (such as edge devices) • Mainly processes short texts in Chinese and English (<512 tokens)
Use with caution : • DeepSeek-R1: only recommended for non-critical business prototype verification • Nomic-Embed-Text: avoid using it for Chinese retrieval in professional fields