Codex 的 5 小时使用限额将于明天恢复

Sol 的各位朋友，大家好！我已重置所有 ChatGPT Work 和 Codex 用户的使用限额。

与此同时，快速更新一下 GPT-5.6 Sol 的使用限额情况。过去几周，许多用户告诉我们，Sol 消耗 Codex 限额的速度快于预期。需要明确的是：我们没有下调任何订阅方案的使用额度。

我们一直在调查其中的原因，并已推出多项改进。因此，我们预计在 Sol 的典型使用场景中，你们的使用额度续航时间将延长约 18%。从今天开始，部分用户应该已经能够看到显著更大的改善。

明天，我们还将恢复此前在调查期间临时暂停的 5 小时限额。

以下是我们的发现：

• GPT-5.6 Sol 更愿意进行更长时间的工作、发起额外的工具调用，并协调跨工具和子 Agent 的复杂工作流。这使它更擅长解决困难问题，但也意味着某些任务的用量远高于我们的预期。
• 在相同的推理强度下，Sol 的工作量也高于此前的模型。在 Sol 上使用 High 档位，消耗的 token 可能比 GPT-5.5 上的 High 档位更多。
• 编程式工具调用（也称为代码模式，code mode）让 Sol 能够更灵活地并行运行工具调用，或在等待期间继续工作。但这也导致每轮产生更多响应、更多缓存输入 token，以及高于预期的用量消耗。
• 当 Sol 等待工具调用完成，或执行大量网页搜索时，这一现象尤为明显。我们已改进对这两种情况的处理方式，并将继续提升代码模式的效率。
• 这种影响也极不均衡。实际上，中位数用户认为 Sol 的 token 效率相当不错；但一些处理更困难任务的重度用户发现，他们的额度消耗速度快得多。我们在发布前非常关注平均用量和中位数用量，却忽略了一些长尾场景：这些场景的用量可能显著更高。

Sol 是 Codex 能力上的一次重要进步，但能力与效率并不总是以相同速度提升；有些问题也只有当人们在真实世界规模下使用模型时才会显现。我们本应更早意识到这一点，并更坦诚地说明情况。

你们不断推动前沿，我们也会持续提升效率，并在推进过程中分享更新。

Hello people of Sol! I've reset usage limits for all ChatGPT Work and Codex users. Together with that, a quick update on GPT-5.6 Sol usage limits. Over the past few weeks, many of you have told us that Sol was using your Codex limits faster than expected. To be clear, we have not reduced usage on any subscription plans. We’ve been digging into what was happening and have landed several improvements. As a result, we expect your usage to last around 18% longer during typical use of Sol. Some of you should already see significantly larger improvements from today. Tomorrow, we’ll also restore the five-hour limit that we temporarily paused while investigating. Here’s what we found: - GPT-5.6 Sol is much more willing to work for longer, make additional tool calls, and coordinate complex workflows across tools and subagents. That makes it better at solving hard problems, but some tasks were using far more than we intended. - Sol also works harder at the same reasoning effort than previous models. High on Sol can use more tokens than High did on GPT-5.5. - Programmatic tool calling, also referred to as code mode, gives Sol much more flexibility to run tool calls in parallel or continue working while waiting. But it also led to more responses per turn, more cached input tokens, and higher usage than expected. - This was particularly noticeable when Sol was waiting for tool calls to finish or running many web searches. We’ve improved how we handle both cases and are continuing to make code mode more efficient. - The impact was also very uneven. The median user actually found Sol quite token efficient, while some power users working on harder tasks saw their usage drain much faster. We were very focused on average and median usage before launch and missed some cases where the long tail could use significantly more usage. Sol is a significant step forward in what Codex can do, but capability and efficiency do not always improve at the same pace, and some issues only become clear once people are using the model at real-world scale. We should have recognized this sooner and been more upfront about it. You keep pushing the frontier and we’ll keep improving efficiency and sharing updates as we go.