Introduction
In the rapidly evolving world of AI, choosing the right large language model (LLM) for your needs can be challenging. OpenAI’s GPT-4o, released in May 2024, is a powerful multimodal model handling text, audio, images, and video, accessible via API. However, open-source models like Mistral’s Mixtral, Meta’s Llama 3, and others offer compelling alternatives, especially when deployed on platforms like Runpod. This article compares GPT-4o with leading open-source models in terms of cost, speed (latency), and control (flexibility), emphasizing how Runpod enables affordable and controlled deployment of open-source models.

How does OpenAI’s GPT-4o compare to open-source models like Mistral, Llama 3, and Mixtral in terms of cost, speed, and control?
This question is central for developers and businesses deciding between proprietary and open-source LLMs, balancing performance, budget, and customization needs.

Cost Comparison

• GPT-4o: Priced at approximately $3 per million input tokens and $10 per million output tokens (as of 2025). For a typical request with 500 input tokens and 50 output tokens, the cost is around $0.002, making it suitable for low-volume, occasional use.
• Open-Source Models on Runpod: Running models like Llama 3.3 70B on an A100 80GB GPU costs about $1.64 per hour. Assuming an inference speed of 20 tokens per second, processing a request takes approximately 3 seconds, costing about $0.0014 per request. For high-volume usage, this can be more cost-effective, potentially saving significant amounts over time.

Speed (Latency) Comparison

• GPT-4o: Designed for fast response times, with audio inputs processed in as little as 232 milliseconds, optimized for real-time applications like chatbots and interactive systems.
• Open-Source Models on Runpod: Latency depends on hardware and setup. With proper configuration on high-performance GPUs like A100 or H100, open-source models can achieve low latency suitable for many applications, though achieving the same level of optimization as OpenAI’s managed service may require additional effort.

Control and Flexibility

• GPT-4o: Managed by OpenAI, offering ease of use but limited control over the model and data, with users adhering to OpenAI’s terms and conditions, which may restrict customization for specific use cases.
• Open-Source Models on Runpod: Provide full control, including fine-tuning, modification, and deployment as needed. Data privacy and security are managed by the user, crucial for sensitive applications, and offer flexibility to choose hardware and optimize costs based on specific requirements.

Why Choose Runpod for Open-Source Models?
Runpod offers a scalable and cost-effective platform to deploy open-source LLMs, with features like affordable GPU rentals (e.g., A100 at $1.64/hr, H100 at $1.99/hr), flexible deployment options (pods and serverless endpoints), and an active community for support. This makes it ideal for custom AI applications, especially where cost savings and control are priorities.

Get Started with Runpod
Ready to deploy your own LLMs? Sign up for Runpod and explore our GPU pricing to find the best option for your needs. For more information on deploying models, check out our deployment guide.

FAQ

• What are the main differences between GPT-4o and open-source models?
  A: GPT-4o is a proprietary model with advanced multimodal capabilities, while open-source models like Llama 3 and Mixtral offer flexibility and control, allowing deployment on your own infrastructure.
• Is it cheaper to use open-source models on Runpod compared to GPT-4o?
  A: For high-volume usage, deploying open-source models on Runpod can be more cost-effective due to lower per-request costs.
• How does the speed of open-source models on Runpod compare to GPT-4o?
  A: While GPT-4o is optimized for fast response times, open-source models on Runpod can achieve competitive speeds with proper hardware and configuration.
• What are the benefits of using Runpod for deploying LLMs?
  A: Runpod offers affordable GPU rentals, flexible deployment options, and full control over your models and data, making it ideal for custom AI applications.