Is anyone else finding chatGPT 5.2 thinking too slow to be of practical use in coding?

Evaluating ChatGPT 5.2’s Performance in Coding Tasks: Is Speed a Limitation?

In recent discussions within the developer community, a recurring concern has emerged regarding the usability of ChatGPT 5.2 for coding assistance. Many users have observed that the model’s extended thinking capabilities, while potentially valuable for complex problem-solving, often result in significant delays, sometimes taking several minutes to produce a response or failing to complete the task entirely.

This slowdown can present a practical barrier for developers seeking prompt support during workflow-intensive tasks. The prolonged processing times diminish the tool’s efficiency, making it less viable for time-sensitive projects. Despite the advancements in language understanding and reasoning, these performance issues raise questions about the current state of ChatGPT 5.2’s suitability for real-world coding applications.

Interestingly, some users have reverted to earlier versions—specifically, the legacy 5 model—despite acknowledging that it may lack some of the newer model’s sophistication. This preference underscores a common trade-off: prioritizing response speed and reliability over the perceived enhancements in intelligence or reasoning.

As AI-powered coding assistants become more integral to software development processes, evaluating their performance in real-world scenarios remains crucial. Ongoing improvements and optimizations are essential to balancing advanced reasoning capabilities with the practical demands of developers. For now, many continue to rely on older versions that deliver faster, more dependable results, highlighting the importance of efficiency in AI tools used for programming.

Conclusion

While ChatGPT 5.2 introduces promising features, its current pace—particularly during extended thinking modes—can hinder practical use in coding workflows. Developers and users should weigh the benefits of the latest functionalities against the operational delays and consider reverting to earlier models when immediate responses are essential. Continued development and refinement are necessary to ensure AI assistants meet the evolving needs of the programming community effectively.