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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/gemini-2.5-flash";
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.
Gemini 2.5 Flash: Multimodal AI Model
Edited by Segmind Team on October 27, 2025.
What is Gemini 2.5 Flash?
Gemini 2.5 Flash is a sophisticated multimodal AI model by Google Cloud, capable of processing various inputs: text, code, images, audio, and video, to produce high-quality text outputs. It can support up to one million tokens while handling enterprise-level use cases, where advanced AI capabilities and transparency are essential. It illustrates the steps during the reasoning process, providing its users with detailed insights into its workflow; hence, it excels as a high-end model on Vertex AI.
Key Features of Gemini 2.5 Flash
- Multimodal Understanding: It processes text, code, images, audio, and video inputs seamlessly
- Transparent Reasoning: It illustrates step-by-step thinking processes during response generation
- Google Search Integration: It is connected to real-time Google Search, hence it can generate responses grounded in current data
- Advanced Code Capabilities: It can seamlessly execute code and supports function calling
- Structured Output Control: It delivers responses in formats as per your requirements
- Massive Context Window: It is capable of handling up to 1 million tokens for large-scale processing
- Global Infrastructure: It leverages Google Cloud's worldwide network for reliable performance
Best Use Cases
- Enterprise Applications: It is a natural choice for large-scale data processing and analysis
- Software Development: It can handle code generation, debugging, and documentation
- Content Creation: It can perform multimodal content generation and editing
- Research & Analysis: It can execute complex data interpretation with explained reasoning
- Customer Service: It can produce intelligent response systems with context awareness
- Educational Tools: It is perfect for creating interactive learning experiences
Prompt Tips and Output Quality
- Provide clear, specific instructions for best results
- Leverage the model's multimodal capabilities by combining different input types
- Use structured prompts when specific output formats are needed
- Make use of the reasoning feature for complex tasks
- Include relevant context for more accurate and real, verifiable responses
FAQs
How is Gemini 2.5 Flash different from other language models? Gemini 2.5 Flash supports multimodal processing, transparent reasoning, and a massive context window, all while maintaining high performance on Google Cloud's infrastructure, making it an excellent option compared to other models.
Can I see how the model reaches its conclusions? Yes, one of Gemini 2.5 Flash demonstrates its reasoning process, making it easier to understand and verify outputs.
What types of inputs can the model handle? The model processes text, code, images, audio, and video inputs, making it ideal for multiple applications.
Is integration with existing systems straightforward? It is integrated within Google Cloud’s Vertex AI platform, making it compatible with existing cloud infrastructure and APIs for simple deployment and scaling.
How can I optimize prompt design for better results? To get the precise results, provide prompts with clear instructions, utilize multimodal inputs (when needed), and leverage the structured output control for specific format requirements.
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