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const { OpenAI } = require('openai');
const api = new OpenAI({
baseURL: 'https://api.ai.cc/v1',
apiKey: '',
});
const main = async () => {
const result = await api.chat.completions.create({
model: 'openai/o4-mini-2025-04-16',
messages: [
{
role: 'system',
content: 'You are an AI assistant who knows everything.',
},
{
role: 'user',
content: 'Tell me, why is the sky blue?'
}
],
});
const message = result.choices[0].message.content;
console.log(`Assistant: ${message}`);
};
main();
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.ai.cc/v1",
api_key="",
)
response = client.chat.completions.create(
model="openai/o4-mini-2025-04-16",
messages=[
{
"role": "system",
"content": "You are an AI assistant who knows everything.",
},
{
"role": "user",
"content": "Tell me, why is the sky blue?"
},
],
)
message = response.choices[0].message.content
print(f"Assistant: {message}")
-
AI Playground
Test all API models in the sandbox environment before you integrate.
We provide more than 300 models to integrate into your app.
Product Detail
🚀 Introducing O4-Mini: OpenAI's Cost-Effective Multimodal AI Model
O4-Mini is OpenAI's innovative small-scale reasoning model, engineered to strike an optimal balance between high performance and cost-efficiency. It stands out with its robust multimodal capabilities, seamless tool integration, and exceptional performance in demanding mathematics and coding tasks. Designed for agility, O4-Mini ensures faster response times and more accessible pricing compared to its larger counterparts. A significant leap from previous mini models, it now fully supports image understanding, web browsing, and Python code execution.
Technical Specifications
Context Window and Token Capacity
- ✅ Context Window: 200,000 tokens
- ✅ Maximum Output: 100,000 tokens
API Pricing: Affordable Performance
Input tokens: $1.155 per million tokens
Output tokens: $4.62 per million tokens
Total Cost for 1,000 tokens: $0.00116 (input) + $0.00462 (output) = $0.00578
Performance Benchmarks: Near Flagship Capabilities
- ⭐ MMLU: 83.2% accuracy
- ⭐ AIME (Mathematics): 92.7% accuracy without tools
- ⭐ Codeforces: ELO 2719 (slightly above O3's 2706)
- ⭐ SWE-Bench Verified: 68.1% (just behind O3's 69.1%)
- ⭐ Aider Polyglot (Code Editing): 68.9% (whole file) / 58.2% (diff format)
Key Capabilities
💡 Advanced Reasoning and Problem-Solving
- Utilizes chain-of-thought processing for complex problem decomposition.
- Achieves 92.7% on AIME, demonstrating strong mathematical problem-solving prowess.
- Handles multi-step logical reasoning with structured thinking.
- Especially proficient in STEM fields and analytical tasks.
📸 Comprehensive Multimodal Understanding
- Processes both text and image inputs by default.
- Capable of analyzing diagrams, charts, and even whiteboard sketches.
- Integrates visual information directly into reasoning chains for richer context.
- Effective with both high-quality and lower-quality images.
🛠️ Robust Tool Integration
- Supports Python code execution, web browsing, and image processing.
- Enables chaining tools together for complex, multi-step workflows.
- Available in standard and "high" variants for optimized response times.
- The first mini model to offer full tool support out-of-the-box.
💻 Efficient Code Generation and Editing
- Achieves near-O3 performance on various coding benchmarks.
- Versatile across multiple programming languages.
- Highly effective for both generating new code and editing existing codebases.
- Demonstrates strong capabilities in real-world software engineering tasks.
Integration and Availability
O4-Mini is readily available for integration into your applications. Developers can leverage its power through the OpenAI API, facilitating the creation of intelligent systems.
(Note: The original content referenced a specific snippet for integration, which is not rendered here.)
References and Further Information
- API Documentation: O4-Mini API Documentation
Limitations and Considerations
- ⚠️ Higher First-Token Latency: Due to its in-depth reasoning process, O4-Mini may exhibit higher first-token latency (e.g., 32.04s).
- ⚠️ Performance Tradeoffs: While powerful, some performance aspects might slightly lag behind the larger O3 model.
- ⚠️ Creative Writing: It may struggle with exceptionally complex or nuanced creative writing tasks compared to models specifically optimized for creativity.
Optimal Use Cases for O4-Mini
- ✔️ Mathematical Problem-Solving: Ideal for tasks requiring precise calculations and logical deductions.
- ✔️ Code Generation & Debugging: Excellent for development workflows, generating code snippets, and identifying errors.
- ✔️ Data Analysis with Visual Components: Processes charts and diagrams for insightful data interpretation.
- ✔️ Cost-Effective Agent Development: Powers intelligent agents where balancing quality and efficiency is key.
- ✔️ Balanced AI Applications: Perfect for scenarios demanding strong performance without the premium cost of larger models.
Comparison with Other OpenAI Models
- ⚡ Cost-Effectiveness: Offers near-O3 performance at approximately 1/10th the cost.
- ⚡ Superior Performance: Outperforms previous O3-Mini and O1 models across most benchmarks.
- ⚡ Multimodal Pioneer: It is the first mini model with full multimodal capabilities.
- ⚡ Strategic Positioning: Represents a crucial middle ground between larger, more resource-intensive reasoning models and smaller, faster, less capable models.
✨ Summary: O4-Mini - Smart, Fast, and Affordable AI
O4-Mini delivers impressive reasoning capabilities with comprehensive multimodal support at an exceptionally accessible price point. It excels in complex mathematical and coding tasks while maintaining strong performance across general benchmarks. This makes it an outstanding choice for developers and businesses seeking a balanced solution that offers high-quality AI performance and efficiency for a diverse array of applications.
❓ Frequently Asked Questions (FAQ)
Q1: What is O4-Mini?
A1: O4-Mini is OpenAI's small-scale, cost-effective reasoning model offering multimodal capabilities (text and image understanding), tool integration, and strong performance in mathematics and coding, designed for efficiency and faster responses.
Q2: What are O4-Mini's key strengths?
A2: Its main strengths include advanced mathematical problem-solving (92.7% on AIME), robust code generation, comprehensive multimodal understanding, full tool integration (Python, browsing, image processing), and a highly competitive price-to-performance ratio.
Q3: How does O4-Mini compare to larger models like O3?
A3: O4-Mini achieves near-O3 performance in many benchmarks (e.g., coding) but at approximately 1/10th of the cost. It also offers multimodal features not present in previous mini models, providing a strong balance between capability and cost.
Q4: What are the primary use cases for O4-Mini?
A4: It is ideally suited for mathematical and logical problem-solving, code generation and debugging, data analysis involving visual components, and developing cost-effective AI agents or applications where efficiency and quality are both crucial.
Q5: Are there any limitations to O4-Mini?
A5: Yes, it may have higher first-token latency due to its reasoning depth and might show some performance tradeoffs compared to much larger models, especially in highly nuanced creative writing tasks.
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