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This article explains the developer-reported GPT-5.5 Codex `516` reasoning-token anomaly, including the main GitHub issue, the reported aggr...
516.The original report does not prove that OpenAI is secretly cutting off reasoning. The narrower claim is more careful: telemetry shared in a public GitHub issue shows an unusual GPT-5.5-specific clustering pattern around reasoning_output_tokens = 516, with additional spikes near 1034 and 1552.That detail matters because developers are not only asking whether GPT-5.5 is making mistakes. They are asking whether the model is sometimes taking a shorter reasoning path on complex tasks, then returning answers that feel less reliable than expected.## Source Note- Original article: BAAI Hub article
## GPT-5.5 Gets Stuck at “516”: Developers Notice an Unusual PatternThe discussion started with a surprisingly specific number: 516.According to developer reports, GPT-5.5 has recently shown weaker performance on some complex programming and reasoning tasks inside Codex. What made the issue stand out was not just that the model sometimes produced wrong answers. It was that several failed or suspicious responses appeared to stop at the same reasoning-token boundary.
The central question is simple: why would a top-tier reasoning model repeatedly land on one exact token count?## The GitHub Issue: A Larger Data Window Behind the ClaimThe most important public reference is GitHub issue #30364, opened in the openai/codex repository.In that issue, the developer reported an aggregate pattern in Codex token_count metadata. The claim was that gpt-5.5 responses disproportionately landed at exactly reasoning_output_tokens = 516, with additional fixed-boundary spikes around 1034 and 1552.
The issue also compared GPT-5.5 with other GPT-family models. The gap was large enough to make developers suspect this was not just a normal distribution of reasoning lengths.### 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% |
The original article summarized the point sharply: GPT-5.5 accounted for only a minority of total responses, yet appeared to account for most exact-516 events in this dataset.## The More Suspicious Part: Overall Reasoning Intensity FellOne possible defense would be that GPT-5.5 simply thinks more, so more responses naturally reach higher reasoning-token ranges.But the reported data points in the opposite direction.In May and June, when the exact-516 clustering reportedly became more obvious, GPT-5.5’s overall reasoning-token intensity dropped. Both the mean reasoning-token count and the P90 reasoning-token count were lower than earlier months.### Reasoning-Token Intensity by Month| Month | Mean Reasoning Tokens | P90 Reasoning Tokens |
The issue also connects to an earlier report, #29353, where a developer described a reproducible pattern in Codex Desktop using gpt-5.5 with xhigh reasoning.In that reproduction, some fresh runs went directly to a final answer, used exactly 516 reasoning output tokens, and returned the wrong answer. Other runs spent thousands of reasoning tokens, produced a visible intermediate phase, and returned the expected answer.This earlier issue did not settle the question, but it made the later aggregate report feel less isolated.## Reddit and Hacker News Discussions Add More PressureThe conversation moved beyond GitHub as well. Screenshots in the original article show developers discussing whether a portion of high-risk Codex requests might be silently degraded due to reasoning truncation.
## What Developers Want OpenAI to ClarifyThe community’s core request is not complicated. Developers want OpenAI or the Codex team to clarify what is happening around 516, 1034, and 1552.The open questions include:1. Is this caused by a reasoning budget?516 a normal stop point, a degraded tier, or an internal threshold?The original GitHub issue is careful not to claim that it proves hidden chain-of-thought truncation. The stronger claim is simply that the clustering looks model-specific and threshold-like enough to deserve investigation.## Not Just Less Reliable: Users Also Complain About PersonalityThe second half of the original article shifts from performance to personality.A developer named Angel compared ChatGPT using GPT-5.5 Instant with Claude Fable 5 using side-by-side screenshots. The complaint was not about whether the model could answer. It was about how the assistant behaved.
The article highlights three recurring frustrations.## Problem 1: Everything Becomes a Bullet ListThe first complaint is that ChatGPT tends to over-format even simple conversational responses.When asked to be more natural and less AI-like, ChatGPT reportedly responded with a structured explanation of how it would be natural. Claude, by contrast, gave a much shorter and more casual response.
For users who write casually, this can create friction. A model that always “fixes” the user may feel less like a helper and more like a strict editor.## Problem 3: You Ask for One, It Gives ThreeThe third complaint is that ChatGPT often gives more than requested.In the article’s example, a user asks for a joke. ChatGPT gives one joke, adds another, adds a third, and then asks for the user’s preferred style of humor. Claude gives a shorter response.
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