OpenAI API - Industry Standard AI API Service

📋 Service Information

Provider: OpenAI
Service Type: API Service
API Endpoint: https://api.openai.com/v1
Free Type: Trial Credits ($18, 3 months validity)
API Compatibility: OpenAI Standard Format (Industry Standard)

🎯 Service Overview

OpenAI API is the industry’s most comprehensive AI API service, providing top models like GPT-4o, GPT-4o-mini, and o1. New users get $18 trial credits upon registration, supporting text generation, image understanding, speech recognition, and other multimodal features.

Core Advantages:

• 🏆 Industry Standard - OpenAI API format is the de facto industry standard
• 💰 Trial Policy - New users may receive trial credits (policies subject to change)
• 🤖 Top Models - GPT-4o and other strongest AI models
• 🌐 Multimodal - Comprehensive text, image, and voice support
• 🔌 Mature Ecosystem - Extensive third-party tools and integrations
• 📚 Complete Documentation - Detailed official docs and community support
• ⚡ Reliable Performance - Enterprise-grade stability and speed

Update Note: Trial credit policies change over time. Please visit OpenAI Platform for the latest information.

🚀 Quick Start

Prerequisites

Required:

• ✅ Registered OpenAI account
• ✅ Verified phone number
• ✅ Created API key
• ⚠️ Accessible network environment (Mainland China needs VPN)

For detailed steps, please refer to: OpenAI Registration Guide

Get API Key

5-Minute Quick Example

Using OpenAI Python SDK (Recommended)

from openai import OpenAI

# Initialize client
client = OpenAI(
    api_key="YOUR_API_KEY"  # Replace with your API key
)

# Send request
response = client.chat.completions.create(
    model="gpt-4o-mini",  # Use cost-effective model
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is machine learning?"}
    ]
)

# Print response
print(response.choices[0].message.content)

# Check usage
print(f"\nTokens Used: {response.usage.total_tokens}")

🤖 Available Models

Text Generation Models

Model Details

GPT-4o (Recommended for Complex Tasks)

• Context Window: 128K tokens
• Main Use: Complex tasks, creative writing, deep analysis
• Advantages: Strongest comprehensive capabilities, multimodal support
• Price: Input $2.5/1M, Output $10/1M

GPT-4o-mini (🏆 Most Recommended)

• Context Window: 128K tokens
• Main Use: Daily conversations, code generation, text processing
• Advantages: Extremely high value, fast, excellent quality
• Price: Input $0.15/1M, Output $0.6/1M

GPT-3.5-turbo

• Context Window: 16K tokens
• Main Use: Simple conversations, quick responses
• Advantages: Cheap and fast
• Price: Input $0.5/1M, Output $1.5/1M

o1-preview / o1-mini (Reasoning Models)

• Context Window: 128K tokens
• Main Use: Math, logical reasoning, complex problems
• Advantages: Strong reasoning capabilities, suitable for tasks requiring deep thinking
• Price: o1-preview: $15/1M (input), $60/1M (output)

Other Models

Image Generation:

• DALL·E 3: $0.040 - $0.120/image (based on quality and size)

Speech Recognition:

• Whisper: $0.006/minute

Text-to-Speech:

• TTS: $15/1M characters (standard), $30/1M characters (HD)

Embedding Models:

• text-embedding-3-small: $0.02/1M tokens
• text-embedding-3-large: $0.13/1M tokens

🔢 Quotas and Limits

Free Trial Credits

Note: Trial credit policies change over time and are not guaranteed for all new users. Please refer to the official Billing page.

Free Tier Rate Limits

Important: Specific rate limits vary by account Tier, model, and organization settings. Please refer to the Rate Limits documentation and your account Limits page for accurate information.

Previous Rate Limit Example (for reference only)

| Tokens Per Minute (TPM) | 40,000 tokens/min | Shared across all models | | Batch Queue Limit | 2,000,000 tokens | Batch API |

Post-Payment Rate Limits (Tier 1)

After topping up at least $5, automatically upgrade to Tier 1:

⚠️ Important Limits

1. Trial Credits: Policies vary by time period, check official Billing page for details
2. Rate Limits: Vary by account Tier, suitable for testing initially
3. Tier Upgrades: Recharge and usage over time automatically upgrades your Tier
4. Access Restriction: Mainland China requires VPN

💰 Pricing

Trial Policy

• Trial Credits: New users may receive trial credits (amount varies by period)
• How to Obtain: Check Billing page after registration and verification
• Usage Suggestion: Use for testing and small-scale applications
• Note: Policies subject to change, please refer to official information

Post-Recharge Pricing

Recommended Model Pricing:

*Assumes average 200 tokens input + 200 tokens output per conversation

Note: Prices may change at any time. For the latest pricing, please refer to the official OpenAI Pricing page.

Cost Optimization Suggestions

1. Prioritize gpt-4o-mini

   • Best value
   • Quality good enough
   • Fast

2. Choose Models Wisely

   • Simple tasks use gpt-3.5-turbo
   • Complex tasks use gpt-4o
   • Reasoning tasks use o1 series

3. Optimize Token Usage

   • Simplify system prompts
   • Avoid repeated context
   • Use streaming output to improve experience

💻 Code Examples

1. Basic Conversation

from openai import OpenAI

client = OpenAI(api_key="YOUR_API_KEY")

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "You are a professional AI assistant."},
        {"role": "user", "content": "Explain what deep learning is"}
    ],
    max_tokens=500,
    temperature=0.7
)

print(response.choices[0].message.content)

2. Streaming Output

# Streaming output (real-time display)
stream = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "Write an article about artificial intelligence"}
    ],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

3. Image Understanding (Vision)

# Analyze image
response = client.chat.completions.create(
    model="gpt-4o",  # Vision requires gpt-4o
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "What's in this image?"},
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://example.com/image.jpg"
                    }
                }
            ]
        }
    ]
)

print(response.choices[0].message.content)

4. Multi-turn Conversation

# Maintain conversation history
messages = [
    {"role": "system", "content": "You are a Python programming assistant"}
]

# First round
messages.append({"role": "user", "content": "How to read CSV files?"})
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=messages
)
messages.append({"role": "assistant", "content": response.choices[0].message.content})

# Second round (with context)
messages.append({"role": "user", "content": "How to filter data?"})
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=messages
)

print(response.choices[0].message.content)

5. Function Calling

# Define function
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get weather for specified city",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {
                        "type": "string",
                        "description": "City name"
                    }
                },
                "required": ["city"]
            }
        }
    }
]

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "user", "content": "What's the weather in Beijing today?"}
    ],
    tools=tools
)

# Check if function call needed
if response.choices[0].message.tool_calls:
    tool_call = response.choices[0].message.tool_calls[0]
    print(f"Need to call function: {tool_call.function.name}")
    print(f"Parameters: {tool_call.function.arguments}")

6. cURL Example

curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "gpt-4o-mini",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "Hello, introduce yourself"
      }
    ],
    "max_tokens": 500,
    "temperature": 0.7
  }'

7. Node.js Example

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: process.env.OPENAI_API_KEY,
});

async function main() {
  const completion = await client.chat.completions.create({
    model: 'gpt-4o-mini',
    messages: [
      { role: 'system', content: 'You are a helpful assistant.' },
      { role: 'user', content: 'What is machine learning?' }
    ],
    max_tokens: 500,
    temperature: 0.7,
  });

  console.log(completion.choices[0].message.content);
  console.log(`\nTokens Used: ${completion.usage.total_tokens}`);
}

main();

🌟 Core Advantages

Technical Advantages

1. Industry Standard Interface

   • OpenAI API format is de facto industry standard
   • Extensive tool and library support
   • Easy migration and integration
   • Rich community resources

2. Top Model Quality

   • GPT-4o represents highest level
   • Excellent natural language understanding and generation
   • Complete multimodal capabilities
   • Continuous updates and improvements

3. Mature Ecosystem

   • Official SDKs (Python, Node.js, etc.)
   • Third-party libraries and frameworks (LangChain, LlamaIndex, etc.)
   • Extensive tutorials and documentation
   • Active developer community

4. Enterprise-Grade Reliability

   • High availability (99.9% SLA)
   • Stable performance
   • Complete monitoring and logging
   • Professional technical support

Comparison with Other APIs

💡 Practical Suggestions

✅ Recommended Practices

1. Secure API Key Management

   import os
   from dotenv import load_dotenv
   
   load_dotenv()
   api_key = os.getenv('OPENAI_API_KEY')
   
   client = OpenAI(api_key=api_key)

2. Error Handling and Retry

   import time
   from openai import OpenAI, APIError, RateLimitError
   
   def call_with_retry(messages, max_retries=3):
       for i in range(max_retries):
           try:
               return client.chat.completions.create(
                   model="gpt-4o-mini",
                   messages=messages
               )
           except RateLimitError:
               if i < max_retries - 1:
                   wait_time = 2 ** i
                   print(f"Rate limit, waiting {wait_time} seconds...")
                   time.sleep(wait_time)
               else:
                   raise
           except APIError as e:
               print(f"API error: {e}")
               if i == max_retries - 1:
                   raise
       return None

3. Monitor Usage

   # Log token usage for each call
   def track_usage(response):
       usage = response.usage
       print(f"Input tokens: {usage.prompt_tokens}")
       print(f"Output tokens: {usage.completion_tokens}")
       print(f"Total tokens: {usage.total_tokens}")
       print(f"Estimated cost: ${usage.total_tokens * 0.0000015:.6f}")
   
   response = client.chat.completions.create(...)
   track_usage(response)

4. Optimize Token Usage

   # Simplify system prompt
   system_prompt = "You are an AI assistant"  # Concise version
   
   # Not
   system_prompt = "You are a very helpful AI assistant who always provides detailed and accurate answers..."  # Verbose version
   
   # Use max_tokens to limit output
   response = client.chat.completions.create(
       model="gpt-4o-mini",
       messages=[...],
       max_tokens=200  # Limit output length
   )

5. Set Usage Limits

   • Set monthly spending limit in console
   • Avoid accidental overspending
   • Recommend setting lower limit initially

⚠️ Notes

1. Key Security

   • Never hardcode API keys
   • Use environment variables or key management services
   • Regularly rotate keys
   • Use different keys for different projects

2. Rate Limits

   • Free tier only has 3 RPM, very low
   • Recommend topping up to Tier 1 ($5)
   • Use exponential backoff for rate limits
   • Consider using queue system

3. Cost Control

   • Set usage limits
   • Monitor API calls
   • Prioritize gpt-4o-mini
   • Avoid overly long contexts

4. Error Handling

   • Handle network errors
   • Handle rate limits
   • Handle timeouts
   • Log errors

5. Compliant Use

   • Follow usage policies
   • Don’t generate harmful content
   • Respect copyright
   • Protect user privacy

🔧 Common Questions

1. How to get free $18 credits?

Steps:

1. Register OpenAI account
2. Verify email
3. Verify phone number (supports China +86)
4. Visit platform.openai.com
5. Create API key
6. Credits automatically added to account

2. What to do when trial credits run out?

Solution:

• Bind payment method (credit or debit card)
• Top up to continue using
• Minimum top-up $5
• Pay-as-you-go

3. How to use from Mainland China?

Solution:

• Need stable VPN tool
• Use overseas phone number for verification (or China +86)
• API access requires stable network
• Recommend using servers in Hong Kong/US

4. How to monitor usage?

Method:

• Log in to platform.openai.com
• Visit “Usage” page
• View detailed usage statistics
• Can view by date, model, project

5. Rate limits too low?

Solution:

• Top up at least $5 to upgrade to Tier 1
• Tier 1: 500 RPM (gpt-4o-mini)
• Continue using to automatically upgrade to higher tiers
• Enterprise users can contact to apply for higher quotas

6. Why API calls fail?

Common Causes:

1. Incorrect or revoked API key
2. Rate limit reached
3. Insufficient balance
4. Network issues
5. Incorrect request format

Solutions:

• Check if API key is correct
• Check error message
• Check balance and limits
• Check official status page
• Refer to official documentation

📚 Related Resources

Official Documentation

• Complete OpenAI Introduction
• ChatGPT Usage Guide
• Official API Documentation
• API Reference

Development Tools

• Python SDK
• Node.js SDK
• OpenAI Cookbook
• Postman Collection

Community Resources

• Official Community
• GitHub Discussions
• Stack Overflow

Service Provider: OpenAI