GPT-4o vs O1-mini

In the fast-evolving world of artificial intelligence, selecting the right model can significantly impact the success of your projects. OpenAI, one of the leading model providers, has introduced two prominent models designed for distinct use cases: GPT-4o and o1-mini. These models cater to varying requirements, from solving complex problems to delivering cost-efficient reasoning solutions.

GPT-4o is tailored for high-complexity tasks, including advanced natural language processing (NLP), research, and coding assistance. It excels in scenarios that demand accuracy, depth, and nuanced understanding. On the other hand, o1-mini is optimized for efficiency and speed, making it an excellent choice for lightweight tasks such as STEM reasoning and educational applications.

In this blog post, we will delve into a detailed comparison of GPT-4o and o1-mini. We’ll explore their technical specifications, performance benchmarks, practical applications, API integrations, and cost structures to help developers make an informed decision when choosing between these two powerful models. Whether you prioritize advanced capabilities or cost efficiency, this article aims to clarify how these models align with your development goals.

Specifications and technical details

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Sources:

• OpenAI news release: https://openai.com/index/hello-gpt-4o/
• OpenAI documentation: https://platform.openai.com/docs/models

Performance benchmarks

To assess the capabilities of GPT-4o and O1-mini, we compared their performance across various standardized tests.

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Sources:

• OpenAI news release: https://openai.com/index/hello-gpt-4o/
• OpenAI documentation: https://platform.openai.com/docs/models

GPT-4o consistently outperforms O1-mini in high-complexity tasks, making it ideal for research-intensive applications. On the other hand, O1-mini’s speed and cost-efficiency make it a practical choice for lightweight tasks like chatbots and basic reasoning.

Practical applications and use cases

GPT-4o:

• Academic research: excels in understanding and generating complex scientific text.
• Coding assistance: provides high-accuracy solutions for coding problems, debugging, and code completion.
• Advanced content creation: Generates high-quality, context-aware text for blogs, technical documentation, and reports.

O1-mini:

• STEM applications: optimized for tasks in mathematics and coding, providing cost-effective reasoning capabilities.
• Customer support chatbots: delivers quick and cost-effective responses to user queries.
• Basic reasoning rasks: handles logical reasoning problems at a fraction of the cost.
• Educational tools: powers simple question-and-answer applications for learning environments.

Using the models with API

Developers can access both GPT-4o and o1-mini through OpenAI's API. Below are examples of how to interact with these models using Python and cURL.

Accessing APIs directly

GPT-4o requests example:

Python:

from openai import OpenAI
client = OpenAI()

completion = client.chat.completions.create(
  model="gpt-4o",
  messages=[
    {"role": "developer", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Hello!"}
  ]
)

print(completion.choices[0].message)

  

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Curl:

curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "gpt-4o",
    "messages": [
      {
        "role": "developer",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "Hello!"
      }
    ]
  }'

  

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O1-mini requests example:

Python:

from openai import OpenAI
client = OpenAI()

completion = client.chat.completions.create(
  model="o1-mini",
  messages=[
    {"role": "developer", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Hello!"}
  ]
)

print(completion.choices[0].message)

  

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Curl:

curl https://api.openai.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "o1-mini",
    "messages": [
      {
        "role": "developer",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "Hello!"
      }
    ]
  }'

  

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Simplifying access with Eden AI

Eden AI provides a unified platform to interact with both GPT-4o and O1-mini using a single API, eliminating the need for managing multiple keys and integrations. Through Eden AI, engineering and product teams can access hundreds of AI models. With a dedicated user interface and Python SDK, teams can orchestrate multiple models and connect custom data sources seamlessly. Additionally, Eden AI ensures reliability by offering advanced performance tracking and monitoring tools, enabling developers to maintain high standards of quality and efficiency in their projects.

Eden AI also offers a developer-friendly pricing model. Teams only pay for the API calls they make, at the same price as their preferred AI providers. There are no subscriptions or hidden fees. Eden AI’s margin is supplier-side, ensuring transparent and fair pricing. Furthermore, there are no API call limits, whether you make 10 calls or 10 million.

Finally, Eden AI is designed with a developer-first mindset. Built with engineering teams in mind, it prioritizes usability, reliability, and flexibility to empower developers to focus on creating impactful AI solutions.

Eden AI requests example for O1-mini:

Python:

const headers = {Authorization: "Bearer {{ api_key }}"};
const url = "https://api.edenai.run/v2/multimodal/chat";
const body = {
  providers: ["openai/o1-mini"],
  messages: []
};

const response = await fetch(url, {
  method: "POST",
  headers,
  body
});

const result = await response.json();
console.log(result)

  

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JavaScript:

const headers = {Authorization: "Bearer {{ api_key }}"};
const url = "https://api.edenai.run/v2/multimodal/chat";
const body = {
  providers: ["openai/o1-mini"],
  messages: []
};

const response = await fetch(url, {
  method: "POST",
  headers,
  body
});

const result = await response.json();
console.log(result)

  

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Source:

• Eden AI API documentation: https://docs.edenai.co/reference/multimodal_multimodal_chat_create

Cost analysis

For text:

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For audio (realtime):

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For fine tuning:

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Sources:

• Official OpenAI pricing: https://platform.openai.com/docs/pricing

These costs highlight the differences in capabilities and pricing structures between the two models. GPT-4o offers a broader range of functionalities, including support for audio and fine-tuning tasks, while o1-mini remains focused on efficient text-based operations at a lower cost.

Conclusion and recommendations

Both GPT-4o and o1-mini are valuable models, each excelling in specific areas. GPT-4o’s superior performance in complex NLP tasks, coding, and research makes it the go-to choice for projects requiring depth and precision. Its higher costs reflect its advanced capabilities, which are indispensable for high-complexity applications.

In contrast, o1-mini’s affordable pricing and optimized performance for STEM-related tasks make it a practical option for cost-sensitive applications. Developers working on educational tools, chatbots, or basic reasoning problems will find o1-mini an excellent fit.

Ultimately, the choice between GPT-4o and o1-mini depends on your project’s complexity, performance requirements, and budget. By leveraging Eden AI’s platform, developers can easily integrate both models into their application, test their capabilities, and choose the model that best aligns with their needs. Eden AI’s flexible pricing and developer-first approach further simplify the process, ensuring teams can focus on building impactful solutions without being bogged down by operational complexities.

Additional resources

• GPT-4o release note
• O1-mini release note
• Eden AI API documentation
• Open AI API documentation

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