A GitHub issue on OpenAI's Codex repository raises concerns that GPT-5.5's reasoning-token clustering may be leading to degraded performance. The report, posted on Hacker News, points to a potential flaw in how the model clusters tokens during reasoning steps. This issue has drawn attention from the developer community, though details remain sparse.
The problem appears to affect the model's ability to generate coherent or accurate code completions when reasoning tokens are clustered. Such clustering, if confirmed, could undermine the reliability of Codex for complex programming tasks. OpenAI has yet to issue an official response or fix.
The GitHub issue currently carries nine points and one comment, indicating limited but growing awareness. No concrete metrics on the frequency or severity of the degradation have been provided by the reporter. Independent verification is lacking due to the niche nature of the claim.
If the performance degradation is widespread, it could impact developers who rely on Codex for production-level coding assistance. Downstream applications built on top of the API may also see inconsistent behavior. OpenAI will need to investigate and potentially roll out a patch.
One commenter questioned whether the issue is specific to GPT-5.5 or exists in earlier versions, suggesting that further testing is needed before drawing firm conclusions.