Bug that makes GPT-5.2-Thinking context window 32K

Understanding the Limitations of GPT-5.2: A Potential Context Window Bug

In recent interactions with the GPT-5.2 language model, some users have reported unexpected behavior related to the model’s context window—particularly when handling large volumes of code or extensive instructions. This article aims to shed light on a specific issue that might point to a bug affecting the model’s effective context capacity and discusses potential implications for users.

Experiencing Truncated Inputs

In a recent testing scenario, a user attempted to input approximately 4,000 lines of code, along with accompanying instructions, into the GPT-5.2 model. While the model immediately responded with an “Input too large” message—which aligns with expectations when exceeding input limits—the behavior of the “thinking” mode was notably different. Instead of outright rejecting the input, the model appeared to process the data but then indicated it had been truncated mid-statement, rendering it unable to access critical instructions and context.

Diagnosis and Token Counting

To investigate this anomaly, the user manually counted the tokens in the pasted code, using an external tool (a Google Colab environment). The total token count for the code, combined with the instructions, exceeded 32,000 tokens—close to the apparent maximum context window size for GPT-5.2, which aligns with the typical upper limit of 32K tokens for such models.

This suggests the model’s internal context window might not be functioning correctly when handling large inputs, leading to unintended truncation and incomprehensibility of the provided instructions. Interestingly, this issue did not surface when using temporary chat modes or within specific project environments, indicating that the behavior may be tied to larger input sizes or certain modes of operation.

Impact on User Experience and Model Performance

The absence of clear feedback from the model makes diagnosing such issues challenging. Users experiencing context memory problems—such as the model forgetting previous outputs or failing to reference earlier instructions—might encounter the same underlying bug. Furthermore, without integrated token counters or explicit input size feedback, users remain unaware of when their inputs approach or exceed effective limits.

The user also expressed frustration over the inability to report this bug directly to developers, emphasizing the need for more transparent diagnostics within the platform. Incorporating features like real-time token counting, input size warnings, or detailed error messages could improve user awareness and help identify issues before they impact productivity.

Conclusion and Recommendations

While it is hoped that this behavior is a temporary glitch that will be addressed in future updates, the