text-embedding-3-small
Embedding Model • Released 2024-01-25 • Last Verified 2025-02-01
text-embedding-3-small is a high-dimensional text embedding and semantic retrieval model developed by OpenAI. Tailored for Retrieval-Augmented Generation (RAG), vector database indexing, semantic similarity matching, and cross-lingual passage re-ranking.
Plain English Summary (What is this model & who is it for?)
Think of text-embedding-3-small as a versatile AI assistant for writing, research, and brainstorming. It helps you draft emails, write essays, summarize long articles, and generate creative ideas on any topic.
💡 Real-World Use Cases & Practical Examples
🚀 How to Run & Use This Model (Step-by-Step Guide)
Simple setup instructions for everyday users and developers.
Sign Up & Get API Access
Create a account on OpenAI's official developer portal and obtain your API Key.
Try the Interactive Playground
Click the "Try Playground" button at the top of this page to test prompts instantly inside your web browser.
Send Your First Request
Use standard HTTP cURL requests or official Python/Node.js SDKs to send prompts to the endpoint.
Integrate Into Your App
Pass the model ID "text-embedding-3-small" into your code payload to power chatbots, workflows, and web applications.
Benchmark Performance
Strengths
- •High accuracy
- •Fast inference
Limitations & Weaknesses
- •Closed source API
import openai
client = openai.OpenAI()
response = client.chat.completions.create(
model="text-embedding-3-small",
messages=[
{"role": "system", "content": "You are an expert AI assistant."},
{"role": "user", "content": "Explain quantum computing in 2 sentences."}
]
)
print(response.choices[0].message.content)Pricing Overview
Prices subject to provider tiers and volume discounts. Check documentation for current token rates.
Model Tags
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