o4-mini

On April 16, 2025, the o4-mini model was released to all ChatGPT users (including free-tier users) as well as via the Chat Completions API and Responses API. This represents significant advancement in making powerful reasoning capabilities more accessible to broader developer community. The model introduces first iteration of o4 series, focusing on balance between performance and efficiency that many developers need for production applications.

o4-mini supports a context window of up to 200,000 tokens and can generate up to 100,000 tokens in a single output. This technical specification places it among models with large context capabilities, making it suitable for complex document analysis and long-form content generation. The model maintains competitive position in AI landscape by offering reasoning capabilities similar to larger models while providing cost benefits for high-volume usage scenarios.

The scope of our analysis covers comprehensive comparisons within OpenAI's model family, including GPT-4o mini and other variants, as well as cross-provider comparisons with major competitors like Anthropic's Claude models and Google's Gemini series. These comparisons help understand where o4-mini fits in current AI ecosystem and when developers should choose it over alternatives.

Our analysis shows o4-mini achieves strong performance across multiple domains while maintaining cost efficiency that makes it attractive for production deployments. It supports tool use and demonstrates competitive reasoning and coding performance across benchmarks like AIME (99.5% with Python) and SWE-bench, outperforming its predecessor o3-mini and even approaching o3 in some domains. The pricing structure at $4.40 per million output tokens positions it as premium option compared to GPT-4o mini but with enhanced reasoning capabilities that justify the cost difference for many use cases.

o4-mini Specifications

The following table provides comprehensive technical details about o4-mini's capabilities and availability options across different platforms and pricing tiers.

o4-mini vs GPT-4o mini

Both models represent different approaches to efficiency in OpenAI's model lineup, with o4-mini focusing on reasoning capabilities while GPT-4o mini emphasizes general-purpose cost efficiency. The comparison between these models helps developers understand which option better suits their specific requirements and budget constraints.

o4-mini supports vision capabilities and offers larger context window compared to GPT-4o mini's 128K tokens, making it more suitable for complex multimodal tasks and long document processing. The models target different segments of developer needs, with o4-mini serving applications requiring advanced reasoning and GPT-4o mini focusing on high-volume, cost-sensitive operations.

Is o4-mini better than GPT-4o mini

o4-mini provides superior capabilities for reasoning-intensive tasks and complex problem solving compared to GPT-4o mini. Despite its smaller size, o4-mini exhibits high accuracy in STEM tasks, visual problem solving (e.g., MathVista, MMMU), and code editing. This makes it better choice for applications requiring analytical depth and multi-step reasoning processes.

However, GPT-4o mini remains better option for cost-sensitive applications with simpler requirements. At $0.15 input and $0.60 output per 1M tokens compared to o4-mini's higher pricing, GPT-4o mini offers significant cost savings for basic text generation, simple classification tasks, and high-volume processing where advanced reasoning is not critical requirement.

Technical Specifications of o4-mini vs GPT-4o mini

This comparison examines key technical differences between OpenAI's efficiency-focused models to help developers choose appropriate option for their applications.

Performance Benchmarks between o4-mini vs GPT-4o mini

The benchmark comparison focuses on reasoning, coding, and multimodal capabilities where these models show most significant differences in their design philosophies and target applications.

Pricing of o4-mini and GPT-4o mini

The pricing structures reflect different positioning strategies, with o4-mini targeting reasoning-intensive applications while GPT-4o mini focuses on cost-efficient general-purpose usage scenarios.

API Integration Examples of o4-mini vs GPT-4o mini

Both models use OpenAI's standard API format but o4-mini includes additional parameters for reasoning tasks and supports longer context windows in practice.

# o4-mini Example
import openai

client = openai.OpenAI(api_key="your-api-key")

response = client.chat.completions.create(
    model="o4-mini",
    messages=[
        {"role": "system", "content": "You are a helpful reasoning assistant."},
        {"role": "user", "content": "Solve this complex mathematical problem step by step..."}
    ],
    max_tokens=10000,  # Can handle up to 100k tokens
    temperature=0.1
)

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

# GPT-4o mini Example
import openai

client = openai.OpenAI(api_key="your-api-key")

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Generate a summary of this document..."}
    ],
    max_tokens=4000,  # Limited to 16k tokens
    temperature=0.7
)

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

o4-mini vs Claude Sonnet 4

This comparison examines two leading reasoning-focused models from different providers, each representing advanced capabilities in their respective ecosystems with different strengths and target applications.

o4-mini brings OpenAI's reasoning approach with strong mathematical and coding capabilities, while Claude Sonnet 4 offers Anthropic's constitutional AI approach with emphasis on helpful, harmless, and honest interactions. Both models serve developers needing sophisticated AI capabilities but with different philosophical approaches to AI alignment and safety.

Is o4-mini better than Claude Sonnet 4

o4-mini excels in mathematical reasoning and coding benchmarks, particularly showing 99.5% performance on AIME with Python and strong results on technical problem-solving tasks. For applications requiring precise mathematical calculations, code generation, and STEM-related reasoning, o4-mini often provides more reliable results.

Claude Sonnet 4 demonstrates superior performance in nuanced conversational tasks, creative writing, and complex reasoning that requires understanding of context and human values. For applications involving content creation, analysis of sensitive topics, and tasks requiring careful consideration of ethical implications, Claude Sonnet 4 typically provides more appropriate responses with better alignment to human preferences.

Technical Specifications o4-mini vs Claude Sonnet 4

This comparison highlights architectural and capability differences between OpenAI's reasoning-focused model and Anthropic's constitutional AI approach.

Performance Benchmarks o4-mini vs Claude Sonnet 4

These benchmarks compare reasoning, coding, and general intelligence capabilities between two advanced models designed for different approaches to AI safety and capability.

Pricing comparison of o4-mini vs Claude Sonnet 4

The pricing structures reflect different business models and target markets, with both providers offering competitive rates for their respective capability levels.

API Integration Examples

The API examples show different approaches to accessing these models, with OpenAI using standard chat completions format while Anthropic uses messages API with specific formatting requirements.

# o4-mini API Example
import openai

client = openai.OpenAI(api_key="your-openai-key")

response = client.chat.completions.create(
    model="o4-mini",
    messages=[
        {"role": "system", "content": "You are an expert reasoning assistant."},
        {"role": "user", "content": "Analyze this complex problem step by step..."}
    ],
    max_tokens=20000,
    temperature=0.1
)

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

# Claude Sonnet 4 API Example
import anthropic

client = anthropic.Anthropic(api_key="your-anthropic-key")

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=8000,
    temperature=0.1,
    messages=[
        {"role": "user", "content": "Analyze this complex problem with careful consideration..."}
    ]
)

print(response.content[0].text)

// o4-mini Node.js Example
const OpenAI = require('openai');

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

async function callO4Mini() {
    const completion = await openai.chat.completions.create({
        model: "o4-mini",
        messages: [
            {"role": "system", "content": "You are a helpful reasoning assistant."},
            {"role": "user", "content": "Solve this step by step..."}
        ],
        max_tokens: 15000,
        temperature: 0.1
    });
    
    console.log(completion.choices[0].message.content);
}

// Claude Sonnet 4 Node.js Example
const Anthropic = require('@anthropic-ai/sdk');

const anthropic = new Anthropic({
    apiKey: process.env.ANTHROPIC_API_KEY
});

async function callClaude() {
    const message = await anthropic.messages.create({
        model: 'claude-sonnet-4-20250514',
        max_tokens: 8000,
        temperature: 0.1,
        messages: [
            {"role": "user", "content": "Analyze this problem carefully..."}
        ]
    });
    
    console.log(message.content[0].text);
}

o4-mini vs Gemini Pro

Google's Gemini Pro represents another major competitor in reasoning-capable AI models, offering multimodal capabilities and integration with Google's ecosystem. The comparison helps developers understand trade-offs between OpenAI's reasoning approach and Google's multimodal integration strategy.

Both models target similar use cases but with different strengths in their underlying architectures. o4-mini focuses on step-by-step reasoning and mathematical precision, while Gemini Pro emphasizes multimodal understanding and seamless integration with Google services and tools.

Is o4-mini better than Gemini Pro

o4-mini shows superior performance in mathematical reasoning tasks, particularly in benchmark tests like AIME where it achieves 99.5% with Python support. For applications requiring precise calculations, formal reasoning, and code generation with mathematical components, o4-mini typically provides more reliable and accurate results.

Gemini Pro excels in multimodal tasks involving image understanding, integration with Google Workspace tools, and real-time information access through Google Search integration. For applications needing current information, document processing across multiple formats, and seamless Google ecosystem integration, Gemini Pro offers advantages that o4-mini cannot match due to its knowledge cutoff limitations.

Technical Specifications o4-mini vs Gemini Pro

This comparison examines core technical capabilities and limitations of both models to help developers make informed decisions based on their specific requirements.

Performance Benchmarks o4-mini vs Gemini Pro

These benchmarks focus on areas where both models compete directly, particularly in reasoning, multimodal understanding, and general intelligence tasks.

Pricing comparison of o4-mini vs Gemini Pro

The pricing models reflect different strategies, with o4-mini focusing on premium reasoning capabilities while Gemini Pro offers competitive rates for multimodal processing.

API Integration Examples

The examples demonstrate different API approaches, with OpenAI's straightforward chat format versus Google's Vertex AI integration options.

# o4-mini API Example
import openai

client = openai.OpenAI(api_key="your-openai-key")

response = client.chat.completions.create(
    model="o4-mini",
    messages=[
        {"role": "system", "content": "You are a reasoning expert."},
        {"role": "user", "content": "Solve this mathematical problem..."}
    ],
    max_tokens=25000
)

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

# Gemini Pro API Example
import google.generativeai as genai

genai.configure(api_key="your-google-key")
model = genai.GenerativeModel('gemini-pro')

response = model.generate_content(
    "Solve this mathematical problem step by step...",
    generation_config={
        'max_output_tokens': 8000,
        'temperature': 0.1
    }
)

print(response.text)

How to get free API key for o4-mini

Getting access to o4-mini requires OpenAI account setup and API key generation through their developer platform. The model was released to all ChatGPT users (including free-tier users) as well as via the Chat Completions API and Responses API. This means free-tier users can access the model through ChatGPT interface, though API access requires paid account.

For API access, developers need to create OpenAI account, add payment method, and generate API key through platform.openai.com. While there is no completely free API tier for o4-mini due to its advanced capabilities and computational requirements, OpenAI provides $5 in free credits for new accounts which allows testing the model before committing to larger usage volumes.

Performance Summary

This summary table compares key performance metrics across major models to help developers quickly identify the best option for their specific requirements and budget constraints.

Use Case of o4-mini

o4-mini works best for applications requiring advanced mathematical reasoning, complex problem-solving, and multi-step analysis where accuracy is more important than speed or cost. It is especially well-suited for high-throughput scenarios where latency matters and developers need reliable reasoning capabilities without the full cost of larger models.

Consider alternatives like GPT-4o mini for simple text generation tasks, Claude Sonnet 4 for creative writing and ethical reasoning, or Gemini Pro for multimodal applications requiring real-time information access. The choice depends on balancing reasoning requirements, budget constraints, and specific domain needs of your application.

Pricing Analysis for o4-mini