This article compares mainstream Embedding models (OpenAI text-embedding-3, BGE, E5) across dimensions, performance, cost, and use cases, helping developers choose the right Embedding solution for RAG and Agent applications.
Embedding models convert text to vector representations, serving as the core component for RAG and Agent memory systems. This article compares mainstream Embedding models.
| Model | Dimensions | MTEB Score | Cost | Best For |
|---|---|---|---|---|
| text-embedding-3-large | 3072 | 64.6% | High | Maximum accuracy |
| text-embedding-3-small | 1536 | 62.3% | Medium | Balanced |
| BGE-large-zh | 1024 | 65.4% | Free | Chinese |
| BGE-m3 | 1024 | 64.1% | Free | Multilingual |
| E5-mistral-7b | 1024 | 66.6% | GPU | High accuracy open source |
from openai import OpenAI
client = OpenAI()
response = client.embeddings.create(
input="Text to embed",
model="text-embedding-3-large",
dimensions=1024
)
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("BAAI/bge-large-zh-v1.5")
embeddings = model.encode(["Text1", "Text2"])
| Scenario | Recommended |
|---|---|
| English, high accuracy | text-embedding-3-large |
| Chinese primary | BAAI/bge-large-zh-v1.5 |
| Multilingual | BAAI/bge-m3 |
| Cost sensitive | text-embedding-3-small |
| Offline deployment | BGE or E5 |
代码示例验证通过
模型对比数据准确