suddenly switching to gpt-5 in the middle of a convo

Understanding Unintended GPT Model Switching During Conversations: Insights and Challenges

In recent discussions within various AI user communities, a peculiar and somewhat disruptive issue has come to light: unexpected shifts between different versions of GPT models during ongoing conversations. Specifically, users have reported instances where, despite explicitly choosing to interact solely with GPT-4, the system unexpectedly generates responses using GPT-5 or a similar variant. This phenomenon has caused frustration and raised questions about the stability and consistency of current AI deployment frameworks.

The Core Issue

Many users utilize specific GPT models—most commonly GPT-4—due to preferences related to response quality, safety features, or simply personal alignment with the model’s capabilities. However, during extended interactions, some have observed that responses spontaneously switch to what appears to be GPT-5 or a different model version, despite the user explicitly requesting GPT-4 responses. This switch can happen intermittently, often seemingly triggered by the complexity or emotional weight of the conversation, such as discussing personal issues or tackling technical questions.

Repeated attempts to regenerate responses using the preferred model often fail to prevent the switch. While some settings or interface options allow for the reinforcement of model choice, these do not always appear reliable or effective. The main concern is that even after instructing the system to use GPT-4 repeatedly, it continues to produce outputs consistent with GPT-5, leading to inconsistent conversational experiences.

Potential Causes and Considerations

While definitive causes are still being investigated, several factors might contribute to this issue:

1. Backend Model Routing: AI platforms may dynamically select models based on load balancing or performance optimization, which could unintentionally override user preferences during a session.

2. Session State Management: The conversation’s context or session state might influence model selection, especially if certain prompts or triggers (e.g., emotionally charged topics) cause the system to switch to a different model version automatically.

3. Platform Limitations or Bugs: Temporary glitches or bugs in the user interface or the underlying API could result in inconsistent model application, particularly if the system lacks robust mechanisms to lock in a user’s model choice.

4. User-Induced Factors: Occasionally, user prompts or commands may inadvertently cause the system to alter its behavior or model selection, especially if prompts are ambiguous or mimic instructions that the system interprets as cues to switch models.

Community and Developer Responses

This issue appears to be affecting multiple users, indicating it is not isolated to a single account or session. Discussions suggest that the