Differences Between Azure OpenAI GPT-4o and OpenAI’s Public GPT-4o

Question

Hello MS Community,

We are running a GPT-4o instance via Azure OpenAI Service to provide our employees with a data-sovereign alternative to OpenAI’s publicly available ChatGPT-4o. However, we receive frequent feedback that responses from our Azure-hosted model differ significantly from those given by OpenAI’s web version.

To investigate, we would like to understand:

1. Model Parity: Does Azure OpenAI Service use the exact same GPT-4o model weights as OpenAI’s public ChatGPT-4o? If not, what are the differences?
2. System Prompts: Does the Azure-hosted model include the same hidden system prompt as OpenAI’s version, or is it different? Is there a way to align them?
3. Moderation & Safety Filters: Are there additional content moderation rules or safety mechanisms applied in Azure OpenAI Service that could impact the model’s responses compared to OpenAI’s version?
4. API Parameter Impact: Could differences in default settings for temperature, Top-K, or Top-P cause response variations? What are the recommended settings to align the outputs?
5. Deployment Updates: How frequently does Azure OpenAI Service update its GPT-4o version to match improvements made to OpenAI’s public ChatGPT?

We want to ensure that our internal GPT-4o deployment is as close as possible to the OpenAI version while remaining within our Azure AI environment.

Are there specific configurations or best practices we should follow to minimize discrepancies?

Thanks for your insights!

Answer 1

Hi @Nadine Eilhoff,

Thank you for your query! I understand your concern about the differences between Azure OpenAI GPT-4o and OpenAI's public ChatGPT-4o, especially in terms of consistency and behaviour.

Model Parity: Both Azure OpenAI Service and OpenAI's public ChatGPT-4o use the same GPT-4o model architecture, co-developed by Azure and OpenAI. However, Azure OpenAI is specifically designed with additional enterprise features, such as security and private networking, and it may include slight variations in deployment settings to align with enterprise compliance needs.

System Prompts: The system prompts could differ. Azure OpenAI offers flexibility in how the system prompts are managed, allowing customers to customize the prompts for specific use cases. OpenAI's public version may have predefined system prompts that are not adjustable. If you are seeing differences in responses, it may be due to variations in these system-level configurations.

Moderation & Safety Filters: Both platforms implement safety filters and moderation systems. However, Azure OpenAI applies additional responsible AI content filtering and regional security features to meet enterprise requirements. This could lead to subtle differences in how the model handles sensitive content.

API Parameter Impact: The differences in responses can also be influenced by API parameters. The parameters control the randomness and diversity of the responses. By default, the two platforms may have different settings for these parameters, which can affect the output. We recommend explicitly specifying these parameters to ensure consistent behaviour across both platforms.

Deployment Updates: Azure OpenAI is updated regularly, but the deployment cadence may not always align with OpenAI's public updates. Azure focuses on stability and security for enterprise customers, and updates are rolled out carefully to ensure compliance and consistency.

Please refer to this documentation: comparing-azure-openai-and-openai.

I hope you understand. Do let us know if you any further queries.

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