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OpenAI Reverses Course: GPT-4o Returns as Default for ChatGPT Subscribers

OpenAI GPT-4o GPT-5 Large Language Models AI ChatGPT Enterprise AI
August 13, 2025
Viqus Verdict Logo Viqus Verdict Logo 7
Stabilization, Not Revolution
Media Hype 6/10
Real Impact 7/10

Article Summary

OpenAI has reversed a recent shift, announcing that GPT-4o will once again be the default option for all paying ChatGPT subscribers. This change follows a turbulent initial rollout of GPT-5, which encountered infrastructure issues and user frustration. Subscribers will now have increased control with the new "Show Additional Models" setting, restoring access to GPT-4.1, o3, and o4-mini. The company is also introducing "Auto", "Fast", and "Thinking" modes for GPT-5, alongside adjusted rate limits (3,000 messages/week for "Thinking" mode). Sam Altman is planning a "warmer" personality for GPT-5, while also exploring per-user customization to address strong user attachments to previous models. These changes are designed to address initial user concerns and provide greater control for paying subscribers navigating the evolving landscape of OpenAI's LLMs.

Key Points

  • GPT-4o will now be the default language model for all paying ChatGPT subscribers.
  • Users can choose between ‘Auto’, ‘Fast’, and ‘Thinking’ modes for GPT-5, offering greater control over response speed and complexity.
  • OpenAI is planning to adjust the personality of GPT-5 and investigate per-user customization options.

Why It Matters

This news is critical for enterprise AI leaders navigating the rapidly evolving world of large language models. OpenAI's response to initial user feedback demonstrates the importance of iterative development and responsiveness in a market where user sentiment significantly impacts adoption. The continued experimentation with personality and customization options highlights the potential for personalized AI experiences, which could become increasingly valuable as organizations seek to maximize the ROI of their AI investments. It also underlines the need for robust infrastructure and testing before large-scale releases.

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