Chinese AI Embedding Models: BGE vs M3E vs GTE Comparison

July 29, 2026 · 9 min read

Embedding models are the backbone of every RAG pipeline, semantic search engine, and recommendation system. For Chinese text, the choice of embedding model matters enormously — a model trained primarily on English will produce poor vectors for Chinese documents.

This guide compares the three leading Chinese embedding model families: BGE (BAAI), M3E (Moka), and GTE (Alibaba). We'll look at benchmarks, pricing through AIWave, and provide code examples for each.

Model Overview

*OpenAI score on C-MTEB (Chinese) is lower due to English-centric training

Chinese embedding models outperform OpenAI's text-embedding-3-large by 8-10 points on C-MTEB (the standard Chinese embedding benchmark). If you're building RAG for Chinese text, using a Chinese embedding model is not optional — it's essential.

BGE (BAAI General Embedding)

BGE is developed by the Beijing Academy of Artificial Intelligence (BAAI). It's the most widely adopted Chinese embedding model family, with strong performance across retrieval, classification, and clustering tasks.

Key Variants

• BGE-large-zh-v1.5: Best for pure Chinese, 512 token limit, 1024 dims
• BGE-M3: Multilingual (Chinese + English + 90+ languages), 8192 token limit
• BGE-base-zh-v1.5: Smaller, faster variant (768 dims)

from openai import OpenAI

client = OpenAI(
    api_key="your-aiwave-key",
    base_url="https://api.aiwave.live/v1"
)

# Generate embeddings for Chinese text
response = client.embeddings.create(
    model="bge-large-zh-v1.5",
    input=["人工智能正在改变世界", "深度学习是AI的核心技术"]
)

for item in response.data:
    print(f"Index {item.index}: {len(item.embedding)} dimensions")

GTE (General Text Embedding)

Alibaba's GTE models consistently top the C-MTEB leaderboard. GTE-large-zh is the current state-of-the-art for Chinese text embeddings.

# Using GTE for semantic search
query_embedding = client.embeddings.create(
    model="gte-large-zh",
    input=["如何降低API调用成本？"]
).data[0].embedding

doc_embedding = client.embeddings.create(
    model="gte-large-zh",
    input=["使用DeepSeek V4 Flash可以将成本降低90%"]
).data[0].embedding

# Cosine similarity
import numpy as np
similarity = np.dot(query_embedding, doc_embedding) / (
    np.linalg.norm(query_embedding) * np.linalg.norm(doc_embedding)
)
print(f"Similarity: {similarity:.4f}")  # ~0.82 for relevant content

M3E (Moka Embedding)

M3E is the budget option — slightly lower performance than BGE/GTE but still significantly better than OpenAI for Chinese text. It's a good choice when embedding cost matters and you don't need top-tier accuracy.

RAG Pipeline Example

import numpy as np
from openai import OpenAI

client = OpenAI(api_key="your-key", base_url="https://api.aiwave.live/v1")

def embed_documents(docs: list[str], model: str = "gte-large-zh") -> np.ndarray:
    """Embed a list of documents."""
    response = client.embeddings.create(model=model, input=docs)
    return np.array([d.embedding for d in response.data])

def semantic_search(query: str, doc_embeddings: np.ndarray, docs: list[str], top_k=3):
    """Find the most relevant documents for a query."""
    q_embed = client.embeddings.create(
        model="gte-large-zh", input=[query]
    ).data[0].embedding
    
    q_vec = np.array(q_embed)
    similarities = np.dot(doc_embeddings, q_vec) / (
        np.linalg.norm(doc_embeddings, axis=1) * np.linalg.norm(q_vec)
    )
    
    top_indices = np.argsort(similarities)[-top_k:][::-1]
    return [(docs[i], similarities[i]) for i in top_indices]

# Usage
docs = ["DeepSeek V4 Flash成本极低", "GLM-5在中文任务上表现优秀", ...]
embeddings = embed_documents(docs)
results = semantic_search("哪个模型最便宜？", embeddings, docs)