Name: GPT-o4-mini-2025-04-16 API
Brand: OpenAI

Overview

GPT-o4-mini-2025-04-16 Description

O4-Mini is OpenAI's small-scale reasoning model designed to balance performance with cost-efficiency. It features multimodal capabilities, tool integration, and strong performance in mathematics and coding tasks while maintaining faster response times and lower pricing than larger models. O4-Mini represents a significant advancement over previous mini models by supporting images, browsing, and Python execution.

Technical Specifications

Context Window and Token Capacity

Context window: 200,000 tokens
Maximum output: 100,000 tokens

API Pricing

Cost Input tokens $1.155 per million tokens

Output tokens $4.62 per million tokens

Cost for 1,000 tokens $0.00116 (input) + $0.00462 (output) = $0.00578

Performance Benchmarks

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

Reasoning and Problem-Solving

Uses chain-of-thought process to break down complex problems
Excels in mathematical problem-solving (92.7% on AIME benchmark)
Handles multi-step logical reasoning with structured thinking
Particularly strong in STEM fields and analytical tasks

Multimodal Understanding

Processes both text and image inputs by default
Analyzes diagrams, charts, and whiteboard sketches
Integrates visual information directly into reasoning chains
Works with both high-quality and lower-quality images

Tool Integration

Supports Python code execution, web browsing, and image processing
Chains tools together for complex multi-step workflows
Available in standard and "high" variants (with more time spent on responses)
First mini model to offer full tool support out of the box

Code Generation

Near-O3 performance on coding benchmarks
Works across multiple programming languages
Effective at both generating new code and editing existing code
Strong performance in real-world software engineering tasks

Integration and Availability

References and Examples

For more detailed information, usage references, and additional examples, please refer to our comprehensive documentation at:

API Documentation: https://docs.aimlapi.com/api-references/text-models-llm/openai/o4-mini

Limitations and Considerations

Higher first-token latency (32.04s) due to reasoning process
Some performance tradeoffs compared to larger O3 model
May struggle with particularly complex creative writing tasks

Optimal Use Cases

Mathematical problem-solving and logical reasoning
Code generation and debugging
Data analysis with visual components
Cost-effective agent development
Applications requiring balance between quality and efficiency

Comparison with Other Models

Offers near-O3 performance at approximately 1/10th the cost
Outperforms previous O3-Mini and O1 models across most benchmarks
First mini model with full multimodal capabilities
Represents middle ground between larger reasoning and smaller, faster models

Summary

O4-Mini delivers impressive reasoning capabilities with multimodal support at an accessible price point. It excels in mathematical and coding tasks while maintaining strong performance across general benchmarks, making it an excellent choice for developers seeking balanced performance and efficiency for a wide range of applications.

‍

Uptime and performance

Uptime (24h): 71.0%
Uptime (7d): 95.0%
Availability (24h): 68.0%
Error rate (24h): 32.0%

Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.

Use cases

Customer support and high-volume conversational AI
Coding assistants and AI pair programming tools
Data extraction, summarization, and report generation
Multilingual text and image understanding applications
Scalable NLP workflows for enterprises and developers

‍

How to use GPT-o4-mini-2025-04-16 API

Install any OpenAI-compatible SDK, point it at api.aimlapi.com/v1, and set the model to openai/o4-mini-2025-04-16.

import requests

r = requests.post(
    "https://api.aimlapi.com/v1/chat/completions",
    headers={"Authorization": "Bearer " + AIMLAPI_KEY},
    json={
      "model": "openai/o4-mini-2025-04-16",
      "messages": [
        {
          "role": "user",
          "content": "Hello!"
        }
      ]
    },
)
print(r.json())

const r = await fetch("https://api.aimlapi.com/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "model": "openai/o4-mini-2025-04-16",
    "messages": [
      {
        "role": "user",
        "content": "Hello!"
      }
    ]
  }),
});
console.log(await r.json());

curl -X POST https://api.aimlapi.com/v1/chat/completions \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"openai/o4-mini-2025-04-16","messages":[{"role":"user","content":"Hello!"}]}'

OpenAI-compatible — swap the base URL and it works with your existing SDK.