The 5‑Hour Wall Is Back — And GPT‑5.6 Sol Is Eating Your Quota

On August 25, 2026, OpenAI reinstated the 5‑hour rolling usage limit for ChatGPT Plus subscribers using Codex and ChatGPT Work. The previous suspension of this limit, which had taken effect on July 12, lasted 44 days.

Within 48 hours of the restoration, more than 210 new replies appeared on the OpenAI forum and Reddit’s r/ChatGPTPro related threads. Among them, 78% mentioned that “available working time for the same workload had noticeably shortened.” This is not a perceptual bias. Community user @Tibo posted a set of trace comparison data in thread #32503, which recorded resource consumption across two model generations under identical task loads.

I. Rule Changes: From a Single Cap to a Dual Cap

July 12 – August 24
Only the weekly total quota remained. Users could concentrate usage during any continuous period, as long as the weekly quota was not exhausted. During this time, OpenAI performed multiple background resets (resetting the quota without public notice), which diluted users’ awareness of per‑session consumption speed.

From August 25 onward
The dual limitation of “every 5‑hour rolling window + weekly total quota” was restored. Even with sufficient weekly quota remaining, once consumption within any continuous 5‑hour window hits the ceiling, users must wait for the window to reset or purchase extra credits.

The substantive difference is not about the total quota itself, but about temporal distribution. During the suspension, one could “burn through a week’s worth in a single day and stay silent for the next six”; after restoration, consumption is spread across each 5‑hour interval, and long tasks can no longer circumvent the limit by compressing their time window.

II. Consumption Data Comparison: Token Difference Between Two Generations on the Same Task

The two sets of running logs extracted by @Tibo both came from the same code‑base analysis task, which comprised 17,912 conversation turns and 1,339 turns involving tool calls. The reasoning effort was set to High/XHigh for both.

For the same workload, GPT‑5.6 Sol consumed approximately 7.95 times more tokens per tool‑bearing turn than GPT‑5.5 (xhigh). Specifically, GPT‑5.5 had 3,104 tool calls and 2,192 model requests, while GPT‑5.6 Sol made only 764 tool calls and 783 requests – fewer steps, but each step carried a much heavier payload. The average token consumption per tool‑bearing turn jumped from ~2.25 million to ~17.88 million. The model’s context window also expanded from 258,400 to 353,400. Additionally, native/top‑level parallel batching, which accounted for ~43.6% in GPT‑5.5, was disabled by Responses Lite in the new model, and nested exec cells using Promise.all dropped from applicable to merely 5 out of 739 cases (0.7%).

Two other supplementary measurements confirmed the trend: under High/XHigh modes, the model’s effective processing cycles were reduced by 52%–55% compared to medium settings. For the same Juice value (a measure of task complexity), GPT‑5.5 xhigh required about 860M to 1B tokens to complete the task, whereas GPT‑5.6 Sol High consumed roughly 3B tokens for an identical set of operations.

III. Real‑World Experience of Plus Users

Scenario A: Medium‑sized codebase analysis (about 100,000 lines)
One user posted their operation log: they started a session, loaded the project index, performed three rounds of structural search, four cross‑file reference traces, and two refactoring suggestions. At the 47‑minute mark, the dashboard showed 42% of the window quota used. At the 89‑minute mark, a pop‑up alerted that the 5‑hour window quota was exhausted, while 67% of the weekly quota remained. The task was interrupted. The user closed the browser, reopened it, and saw the window countdown displaying 4 hours and 11 minutes remaining.

Scenario B: Agent‑based multi‑step automation (plan → execute → verify → correct)
Another user documented an 18‑step automation flow: steps 1–6 (planning and initial code generation) consumed 31% of the window quota; steps 7–12 (execution and error correction) consumed another 44%. The flow was terminated at step 13 when the cap was hit. They manually split the process into three sub‑sessions, each kept within 45 minutes, ultimately completing the original flow in 3.5 hours. Previously, under GPT‑5.5, the same type of workflow could run to completion within a single session without manual partitioning.

IV. Why OpenAI Restored the Limit

From a resource‑scheduling perspective, the motivation behind reinstating the 5‑hour window is not to tighten the total quota, but to smooth out peaks. The per‑inference cost of GPT‑5.6 Sol is significantly higher than its predecessor. If users were allowed to concentrate calls within arbitrary short time windows, the compute queue would experience pronounced spikes. The 5‑hour window distributes peak loads more evenly.

Another observable change: OpenAI concurrently raised the quota priority for Pro subscriptions and opened a pay‑as‑you‑go credit purchase entry. The Plus price remains unchanged (US$20/month), but per‑unit compute costs have risen. The boundary between pricing tiers has become clearer – heavy long‑task users are being steered toward higher‑tier payment plans.

V. Future Outlook: Resets Will Not Occur Frequently in the Short Term

Since August 25, OpenAI has not performed any background quota resets. This contrasts with the period from July to August, when implicit resets occurred every few days. Community monitoring threads show that the last reset happened on August 23, with no further actions since.

The combination of restoring the 5‑hour cap, halting frequent resets, and opening credit purchases sends a clear signal: before the next‑generation model is released, Plus quota scheduling will maintain the current rules and will not offer another “grace period.”

VI. Practical Actions for Plus Users

Based on strategies that have been tested and verified across the forum:

• Switch model variants: For tasks that do not require Sol‑level reasoning, manually select Luna or Lite. In real tests, Luna’s token consumption on the same task is about 28%–35% of Sol’s.

• Split session windows: Break a single long task into several independent sub‑tasks, each completed within one hour, to proactively avoid hitting the 5‑hour rolling threshold.

• Manually truncate context: After finishing each milestone, use /clear to reset the conversation context, preventing historical turns from accumulating and occupying the context window.

• Front‑load critical tasks: Place the portions that absolutely must use Sol + High mode at the very beginning of each 5‑hour window, ensuring sufficient remaining balance.

Closing

One user, after a task interruption, took a screenshot of the dashboard: remaining weekly quota 6.2B, 5‑hour window remaining 0 minutes. They dragged the screenshot into a desktop folder named “Quota Logs,” then switched the model option from Sol to Luna, and reopened the project index page. The window countdown began ticking again. The cursor sat in the input box; they did not type another word.