Codex reasoning-token clustering at 516 may be leading to degraded performance

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:

1. Query token_count events with reasoning_output_tokens by model.

2. Compare exact-value counts for 0, 516, 1034, and 1552.

3. Compute count(reasoning_output_tokens = 516) / count(reasoning_output_tokens >= 516) by model and day.

4. Compare gpt-5.5 against gpt-5.2, gpt-5.4, and Codex-specific variants.

5. Replay matched complex tasks across GPT-5.2 and GPT-5.5 with quality evals, especially separating exact-516 responses from longer-reasoning responses.