GPT-5.5 Codex reasoning-token clustering at 516/1034/1552 may be leading to degraded performance on complex tasks

Summary I found an aggregate pattern in Codex token_count metadata: gpt-5.5 responses disproportionately land at exactly reasoning_output_tokens = 516, with additional fixed-boundary spikes around 1034 and 1552. This appears model-specific and coincides with lower overall reasoning-token intensity, which may help explain degraded performance on complex/high-stakes Codex tasks. This is related to #29353, which reported a task-level reproduction where gpt-5.5 runs ending at exactly 516 reasoning tokens returned the wrong answer. This issue adds aggregate evidence across a larger Feb-Jun window. I am not claiming this proves hidden chain-of-thought truncation. The narrower claim is that Codex telemetry shows a GPT-5.5-specific fixed-token clustering anomaly that looks consistent with thresholded reasoning-budget behavior. Environment

Product: Codex Model most implicated: gpt-5.5 Data source: Codex token_count metadata Time window analyzed: Feb 1-Jun 27, 2026 UTC Related issue: gpt-5.5 xhigh sometimes short-circuits with reasoning_output_tokens=516 and wrong final_answer in Codex Desktop #29353

Evidence

Metric Value

Response-level token records analyzed 390,195

Sessions represented 865

Exact reasoning_output_tokens = 516 events 3,363

GPT-5.5 share of all responses 19.3%

GPT-5.5 share of exact-516 events 82.0%

GPT-5.5 exact-516 / >=516 ratio 44.0%

Non-GPT-5.5 exact-516 / >=516 ratio 1.3%

Model-level result:

Model Response

records Exact 516 / >=516

gpt-5.5 75,401 44.0%

gpt-5.4 25,214 19.8%

gpt-5.2 247,575 0.34%

gpt-5.3-codex 13,333 0.0%

gpt-5.3-codex-spark 26,179 0.0%

Monthly exact-516 clustering increased sharply:

Month Exact 516 / >=516

Feb 2026 0.11%

Mar 2026 2.45%

Apr 2026 4.25%

May 2026 53.30%

Jun 2026 35.84%

At the same time, overall reasoning-token intensity decreased:

Month Mean reasoning tokens P90 reasoning tokens

Feb 2026 268.1 772

Mar 2026 256.8 723

Apr 2026 228.7 669

May 2026 106.9 344

Jun 2026 168.5 515

Why this looks suspicious The anomaly is not simply higher reasoning-token usage overall. Mean and P90 reasoning-token intensity fell from February-April to May-June, while exact-516 clustering rose sharply. The clustering is also not evenly distributed across models. gpt-5.5 accounts for only 19.3% of responses but 82.0% of exact-516 events. Its exact-516 / >=516 ratio is about 33.6x higher than the non-GPT-5.5 baseline. The fixed values are also notable: 516, 1034, and 1552 look like repeated threshold boundaries rather than a naturally varying reasoning-token distribution.

Expected behavior Reasoning-token counts for complex Codex tasks should vary naturally with task complexity and should not disproportionately cluster at exact fixed values for one model family. Actual behavior gpt-5.5 responses cluster heavily at exactly 516 reasoning tokens, with related spikes around 1034 and 1552. This pattern is much weaker or absent in several other models. Ask Could the Codex team investigate whether gpt-5.5 has a reasoning-budget, routing, truncation, fallback, or scheduler behavior that causes responses to terminate around 516/1034/1552 reasoning tokens? If this is expected behavior, it would be useful to know whether exact 516 indicates a normal stopping point, a budget cap, a degraded tier, or another internal threshold. Useful internal validation checks:

Query token_count events with reasoning_output_tokens by model. Compare exact-value counts for 0, 516, 1034, and 1552. Compute count(reasoning_output_tokens = 516) / count(reasoning_output_tokens >= 516) by model and day. Compare gpt-5.5 against gpt-5.2, gpt-5.4, and Codex-specific variants. Replay matched complex tasks across GPT-5.2 and GPT-5.5 with quality evals, especially separating exact-516 responses from longer-reasoning responses.