Embedding Model Selection: From OpenAI to Open Source Models
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.
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Author goumangPublished 2026/03/22 06:05Updated 2026/06/10 18:24
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Overview
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 Comparison
| 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 |
OpenAI Embedding
from openai import OpenAI
client = OpenAI()
response = client.embeddings.create(
input="Text to embed",
model="text-embedding-3-large",
dimensions=1024
)
Open Source (BGE)
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("BAAI/bge-large-zh-v1.5")
embeddings = model.encode(["Text1", "Text2"])
Selection Guide
| 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 |
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