GPT-4o vs Deepseek-R1

TABLE OF CONTENTS

As AI technology evolves, choosing the right model can be tough. Two standout options are GPT-4o and DeepSeek-R1, each with its own unique strengths and innovations.

GPT-4o is a major advancement in multimodal AI, handling text, audio, and images in real-time. In contrast, DeepSeek-R1, an open-source model, specializes in advanced reasoning, code generation, and scientific computations.

This post explores the key features, strengths, and applications of these two cutting-edge AI models, highlighting their impact on the future of AI and various industries.

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Specifications and Technical Details

Feature	GPT-4o	DeepSeek-R1
Alias	gpt-4o	DeepSeek R1
Description (provider)	Our versatile, high-intelligence flagship model	Open-source model for advanced reasoning and code generation.
Release date	May 13, 2024	20th January, 2025
Developer	OpenAI	DeepSeek
Primary use cases	Complex NLP tasks, coding, and research	Scientific research, problem solving, programming tasks
Context window	128k tokens	64k tokens
Max output tokens	16,384 tokens	8k tokens
Processing speed	Average response time of 320 ms for audio inputs	-
Knowledge cutoff	October 2023	-
Multimodal	Accepted input: text, audio, image, and video	Accepted input: text
Fine tuning	Yes	Yes

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Sources:

DeepSeek news release: https://api-docs.deepseek.com/news/news250120
Deepsake Documentation: https://api-docs.deepseek.com/quick_start/pricing‍
OpenAI news release: https://openai.com/index/hello-gpt-4o/‍
OpenAI documentation: https://platform.openai.com/docs/models

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Performance Benchmarks

Benchmark	GPT-4o	DeepSeek-R1
MMLU (multitask accuracy)	88.7%	90.8%
HumanEval (code generation capabilities)	90.2%	-
MATH (math problems)	76.6%	97.3%
MGSM (multilingual capabilities)	90.5%	-

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Sources:

DeepSeek news release: https://api-docs.deepseek.com/news/news250120
OpenAI news release: https://openai.com/index/hello-gpt-4o/‍
OpenAI documentation: https://platform.openai.com/docs/models

DeepSeek R1 excels in multitask accuracy, math problem-solving, and coding, positioning itself as a strong rival to GPT-4o, though there are no official benchmark numbers for its code generation capabilities. While GPT-4o leads in multilingual tasks, DeepSeek R1 remains a competitive choice for technical and coding-heavy applications, offering strong performance in areas where GPT-4o excels as well. The choice between the two depends on the specific focus and requirements of your project.

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Practical Applications

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GPT-4o

Advanced data analysis: Legal document analysis, financial forecasting, spreadsheet processing and complex data analysis
Code generation: Strong performance in generating and debugging code.
Content creation and marketing: assists in generating engaging content, optimizing SEO, and brainstorming creative ideas for marketing campaigns

Deepseek-R1

Mathematical and Technical Problem-Solving: DeepSeek R1 is ideal for scientific research, engineering, and finance applications
Open-Source Customization: Businesses requiring custom AI solutions can benefit from DeepSeek R1's open-source nature
Software Development and Coding: The model ranks among the top for code generation.

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Using the Models with APIs

The following examples provide a practical guide for developers to integrate GPT-4o through OpenAI's API and DeepSeek R1 via DeepSeek into their applications. By using Python and cURL, developers can easily interact with these models, streamlining the integration process.

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Accessing APIs Directly

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GPT-4o requests example

  
    import openai

    openai.api_key = "your-gpt4o-api-key"

    response = openai.Completion.create(

      model="gpt-4o",

      prompt="Explain quantum computing in simple terms.",

      max_tokens=300

    )

    print(response["choices"][0]["text"])

Deepseek-R1 requests example

  
    import requests

    api_key = "your-deepseek-api-key"

    url = "https://api.deepseek.com/v1/completion"

    headers = {"Authorization": f"Bearer {api_key}"}

    data = {

      "model": "deepseek-r1",

      "prompt": "Describe the impact of deep learning on AI research.",

      "max_tokens": 300

    }

    response = requests.post(url, headers=headers, json=data)

    print(response.json()["text"])

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Simplified Access with Eden AI

Eden AI offers a seamless platform that allows developers to interact with both GPT-4o and DeepSeek R1 through a single API, simplifying integration by consolidating access and eliminating the need to manage multiple keys and systems. With hundreds of AI models available, engineering and product teams can effortlessly orchestrate them and connect custom data sources through a user-friendly interface and Python SDK. The platform also includes robust performance tracking and monitoring features, helping developers maintain optimal quality and efficiency throughout their projects.

Eden AI’s pricing model is designed to be transparent and cost-effective, charging only for the API calls made, at the same rates as the selected AI providers. There are no subscriptions or hidden fees, and the platform has no restrictions on the number of API calls—whether it's 10 or 10 million. The supplier-side margin ensures fairness in pricing.

Focusing on flexibility, usability, and reliability, Eden AI allows engineering teams to dedicate their efforts to building powerful AI solutions, without being bogged down by complex integration processes or opaque pricing.

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Eden AI Example Workflow:

  
    import edenai

    client = edenai.Client(api_key="your-edenai-api-key")

    response = client.generate_text(

      model="gpt-4o",

      prompt="Explain the fundamentals of reinforcement learning.",

      max_tokens=300

    )

    print(response["output"])

    response = client.generate_text(

      model="deepseek-r1",

      prompt="Discuss ethical concerns in AI development.",

      max_tokens=300

    )

    print(response["output"])

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Cost Analysis

For text:

Cost (per 1M tokens)	GPT-4o	DeepSeek R1
Input	$2.50	$0.55
Output	$10	$2.19
Cached input	$1.25	$0.14

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For audio (realtime):

Cost (per 1M tokens)	GPT-4o	DeepSeek R1
Input	$40	-
Output	$80	-
Cached input	$2.50	-

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For fine tuning:

Cost (per 1M tokens)	GPT-4o	DeepSeek R1
Input	$3.75	-
Output	$15	-
Cached input	$1.875	-
Training	$25	-

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Sources:

Official OpenAI pricing: https://platform.openai.com/docs/pricing
Official DeepSeek pricing: https://api-docs.deepseek.com/quick_start/pricing

GPT-4o offers advanced capabilities at a higher cost, particularly for text and audio processing, with significant charges for input, output, and fine-tuning. In contrast, DeepSeek R1 provides a more affordable option for text-based tasks, with much lower costs for input, output, and cached input, but lacks support for audio and fine-tuning. The choice depends on the complexity of the task and budget constraints.

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Conclusion and Recommendations

In conclusion, both GPT-4o and DeepSeek-R1 offer distinct advantages depending on the specific needs of developers and engineering teams. GPT-4o excels in advanced reasoning, high-performance NLP tasks, and extensive context handling, making it an ideal choice for complex text, audio, and image processing. On the other hand, DeepSeek-R1 shines in scientific research, mathematical problem-solving, and code generation, offering an open-source solution that’s cost-effective for teams seeking flexibility and customization.

Ultimately, the choice between GPT-4o and DeepSeek-R1 depends on your needs. For seamless integration, Eden AI provides a unified platform that enables developers to harness the power of both models efficiently. By consolidating access and simplifying API management, Eden AI empowers teams to focus on building innovative AI-driven solutions without the hassle of multiple integrations or complex pricing models.

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Additional Resources

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Create your Account on Eden AI

GPT-4o vs Deepseek-R1

Specifications and Technical Details

Performance Benchmarks

Practical Applications

GPT-4o

Deepseek-R1

Using the Models with APIs

Accessing APIs Directly

GPT-4o requests example

Deepseek-R1 requests example

Simplified Access with Eden AI

Eden AI Example Workflow:

Cost Analysis

Conclusion and Recommendations

Additional Resources

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