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const axios = require('axios');
const fs = require('fs');
const path = require('path');
// helper function to help you convert your local images into base64 format
async function toB64(imgPath) {
const data = fs.readFileSync(path.resolve(imgPath));
return Buffer.from(data).toString('base64');
}
const api_key = "YOUR API-KEY";
const url = "https://api.segmind.com/v1/gpt-5-nano";
const data = {
"messages": [
{
"role": "user",
"content" : "tell me a joke on cats"
},
{
"role": "assistant",
"content" : "here is a joke about cats..."
},
{
"role": "user",
"content" : "now a joke on dogs"
},
]
};
(async function() {
try {
const response = await axios.post(url, data, { headers: { 'x-api-key': api_key } });
console.log(response.data);
} catch (error) {
console.error('Error:', error.response.data);
}
})();Attributes
An array of objects containing the role and content
Could be "user", "assistant" or "system".
A string containing the user's query or the assistant's response.
To keep track of your credit usage, you can inspect the response headers of each API call. The x-remaining-credits property will indicate the number of remaining credits in your account. Ensure you monitor this value to avoid any disruptions in your API usage.
GPT-5 Nano: Lightweight Language Model
Edited by Segmind Team on October 28, 2025.
What is GPT-5 Nano?
GPT-5 Nano, developed by OpenAI, is a compact yet powerful language model designed for real-time applications and developer tools where speed is crucial. The model effortlessly integrates AI with production environments for prompt thinking and execution by combining low latency with dependable performance, making it the most streamlined member of the GPT-5 lineup. All these factors make it an invaluable tool for developers who want quick responses and efficient processing.
Key Features of GPT-5 Nano
- It offers ultra-fast response times optimized for real-time applications
- It has a multi-modal input support, i.e., text, images, and files
- It includes standard OpenAI API compatibility for seamless integration
- It has robust instruction-following capabilities
- It comes with built-in safety features and content filtering
- It requires minimal computational resources
- It is integrated with streamlined architecture for efficient deployment
Best Use Cases
- Real-time chat applications and customer service bots
- Developer tools requiring instant feedback
- Mobile applications with limited processing power
- Content moderation systems
- Quick data analysis and reporting
- Interactive documentation and help systems
- Rapid prototyping of AI features
Prompt Tips and Output Quality
- Prompts should be clear and concise for optimal performance
- Utilize specific and direct instructions rather than open-ended queries
- Leverage the multi-modal capabilities by combining text and images
- Break down complex tasks into smaller, focused requests
- Include relevant context in the prompt for more accurate responses
Note
GPT-5 Nano performs best with quick, simple tasks but may require support when performing complex reasoning. Though it delivers impressive accuracy for its size, it is primarily designed for speed over in-depth analysis.
FAQs
How does GPT-5 Nano compare to larger GPT-5 models? GPT-5 Nano prioritizes speed and efficiency over complex reasoning; therefore, it is ideal for applications that need quick responses rather than deep analysis.
Can GPT-5 Nano handle multiple input types? The model can handle inputs like text, images, and files, making it useful for a wide range of application needs.
Is GPT-5 Nano suitable for production environments? GPT-5 Nano's lightweight architecture and reliable performance make it a perfect model needed for production deployment in latency-sensitive applications.
What are the best practices for API integration? For optimum performance, use the standard OpenAI API format through focused requests and implement proper error handling. Also, GPT-5 Nano works seamlessly with existing OpenAI-compatible infrastructure.
How can I optimize prompt engineering for GPT-5 Nano? Provide clear and direct instructions, provide relevant context, and divide complex tasks into smaller components for precise results.
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