GPT-5.5 Codex: Reasoning Token Clustering May Cause Performance Degradation
Original: GPT-5.5 Codex reasoning-token clustering may be leading to degraded performance
Why This Matters
Token budget truncation in AI coding agents could silently reduce output quality on complex software engineering tasks.
A GitHub issue filed on June 27, 2026 reports that GPT-5.5 responses in OpenAI's Codex disproportionately cluster at exactly 516, 1034, and 1552 reasoning output tokens, potentially causing degraded performance on complex tasks.
GitHub user vguptaa45 opened issue #30364 on the openai/codex repository, reporting an anomalous pattern in GPT-5.5 token_count metadata. According to the report, reasoning_output_tokens values disproportionately land at exactly 516, with additional fixed-boundary spikes at 1034 and 1552 — multiples of 516. The reporter describes this as model-specific behavior that coincides with lower overall reasoning-token intensity compared to expectations. The issue is labeled 'bug,' 'model-behavior,' and 'rate-limits,' suggesting the clustering may relate to internal rate limiting, quota enforcement, or token budget truncation within the GPT-5.5 architecture. The reporter suggests this pattern may explain degraded performance observed on complex and high-stakes Codex tasks, where deeper reasoning chains would be expected. The issue is currently open, and no official response from OpenAI has been recorded in the available content.