7月29日 Tibo 确认重置：天呐天呐天呐，回到 100% 了！

@0xrsydn 的帖子（被回复）· 原帖

HOLYSHIT I WAS AT AROUND 10% WEEKLY USAGE LEFT OMG

Tibo 自己之前的帖子（被引用）· 原帖

Sol 的用户们，大家好！我已经为所有 ChatGPT Work 和 Codex 用户重置了用量额度。同时，也顺便简要更新一下 GPT-5.6 Sol 的用量额度。
过去几周，很多人告诉我们，Sol 消耗 Codex 额度的速度比预期更快。需要明确的是，我们并没有降低任何订阅套餐的用量。
我们一直在深入排查问题所在，并已上线了多项改进。因此，我们预计在典型的 Sol 使用场景下，你的额度能用得久大约 18%。有些用户从今天起应该就能看到明显更大的改善。明天，我们还会恢复此前在调查期间暂时暂停的五小时额度限制。
以下是我们发现的情况：
- GPT-5.6 Sol 更愿意长时间工作、发起更多工具调用，并在各种工具和子代理之间协调复杂工作流。这让它更擅长解决难题，但有些任务的消耗远远超出我们的预期。
- 在相同推理强度下，Sol 也比之前的模型更卖力。Sol 上的 High 可能比 GPT-5.5 上的 High 消耗更多 token。
- 程序化工具调用（也称为 code mode）让 Sol 能够更灵活地并行运行工具调用，或在等待时继续工作。但这也导致每轮回复更多、缓存输入 token 更多，用量高于预期。
- 当 Sol 在等待工具调用完成，或运行大量网络搜索时，这一点尤其明显。我们已经改进了这两种情况的处理方式，并会继续让 code mode 更高效。
- 影响也非常不均衡。中位数用户其实觉得 Sol 相当省 token，而一些处理更难任务的深度用户则看到额度消耗快得多。上线前我们非常关注平均和中位用量，忽略了一些长尾用户可能消耗明显更多额度的情况。
Sol 是 Codex 能力上的一大进步，但能力和效率并不总是同步提升，有些问题只有在人们以真实规模使用模型后才会显现。我们应该更早意识到这一点，也应该更坦诚地说明。
你们继续推进前沿，我们就会继续提升效率并随时分享进展。

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.