OpenAI has reset Codex rate limits 35 times in six months while quietly cutting its context window by 27 percent

OpenAI Quietly Shrank Codex's Memory. Developers Noticed.

Codex grew from three million to nine million weekly users in a matter of months 1. OpenAI celebrated each milestone on social media. What went uncelebrated: a 27 percent reduction to the model's context window, the working memory that determines how much code the assistant can process in a single session 2.

The context limit moved from 372,000 tokens to 272,000 tokens in a pull request titled "Backport refreshed bundled model metadata" 2. The phrasing reads like routine maintenance. The impact is structural. For developers working on monorepos or large codebases with extensive dependencies, 100,000 fewer tokens is the gap between loading an entire project and loading fragments of it. The cut surfaced first in community discussion on r/codex, where users questioned whether the change was intentional or temporary 3. OpenAI offered no official explanation.

The context reduction didn't happen in isolation. A community tracker called Codex Resets has documented 35 rate-limit resets over 26 weeks, averaging one every 8.9 days 1. The longest stretch without a reset was 67.7 days. Several came in clusters, with two landing in a single day on multiple occasions.

Thibault Sottiaux, Head of Codex at OpenAI, who personally announces each reset on social media, has become the unofficial signal for when Codex's infrastructure is straining. His announcements alternate between milestone celebrations and damage control. Read in sequence, they reveal a pattern the company hasn't published anywhere else.

Some resets coincide with user-count milestones. Sottiaux announced them at three million, seven million, eight million, and nine million weekly users 1. Others follow incidents. In one announcement, he wrote that there had been "three separate small incidents that affected Codex reliability" in a 24-hour window, adding they were "three too many" 1. Another followed a billing-system rewrite that required backfilling usage data 1. A third came after requests were briefly blocked and rejected as high cyber risk, a glitch resolved in approximately eight minutes 1.

The admissions buried in the reset tweets are where the strain becomes legible. Sottiaux acknowledged that Codex usage was being "consumed faster than expected" and attributed the drain to multiple compounding issues rather than a single root cause 1. In another, he revealed that a performance optimization had backfired, reducing cache hit rates during session compaction and causing limits to drain faster than normal 1. And in a moment of candor: "Don't just reset Codex rate limits for fun, it costs money" 1.

Each reset is a compute concession. When the person responsible for the product says resets cost real money, he is confirming that returning processing capacity to millions of users carries a direct expense. The frequency of those concessions, 35 in 26 weeks, traces a curve of demand outpacing supply.

The categories of failure documented by the tracker are revealing on their own. Billing bugs. Fraudulent account purges. Cache optimizations that backfire. Security false positives that block legitimate requests. Reliability incidents clustered in 24-hour windows. None of these are catastrophic individually. Together, they form a rhythm of reactive fixes that has become routine over six months 1.

The context window cut approaches the same problem from a different angle. Reducing a model's working memory by 27 percent lowers the compute cost per session. It also narrows the range of projects Codex can handle without breaking context. For a developer who spent months building a workflow around the assumption that 372,000 tokens were available, the change is a silent revision of the terms. The pull request that delivered it bundled the reduction with other metadata updates, making the functional impact easy to overlook in the diff 2.

For builders who architected around Codex's capabilities, the risk isn't a single outage or a slow week. It's that the parameters they depend on can change without notice. A context window defines what an architecture can handle. When it shrinks by 100,000 tokens, every pipeline designed around the old limit needs rethinking. OpenAI has not explained the reduction. The resets continue. The tracker counts.