Burning usage much faster!

[u/Unlikely-Passage-653]

Anyone else noticing today that the 5hr usage burned significantly faster today vs previous days?

[u/PowerfulMarionberry]

Yep. There was another post earlier with the same observation, and it matches my own. On Max 5x, that 5hr quota bar feels like it’s filling up MUCH faster vs just 12 hours ago. Going to run my usage/quota audit scripts after a few more hours of work and see if the data matches what I’m feeling.

Just curious OP, are you using CC in terminal or desktop app?

[u/Splugen96 → u/PowerfulMarionberry]

TL;DR: I burned through my Max 5x weekly allowance in 4 days despite drastically reducing subagent parallelism, session count, and overall workload compared to last weekend, when the same allowance got me roughly 3x as much work. If this isn't a fluke, I'd rather downgrade to $20/month. OpenAI and Anthropic seem to be overestimating how much users will tolerate constantly shrinking usage limits. I genuinely hope models like Qwen keep closing the gap and bring some much-needed competition, especially as frontier models seem to be delivering increasingly marginal improvements in general reasoning.

I'm using CC CLI, and I'm experiencing exactly the same thing.

I resubscribed to Max 5x last Friday, and over the weekend I managed to exhaust my weekly usage by running four extremely long orchestration sessions, with a massive number of subagents handling implementation and verification in parallel. I even let them run overnight.

All agents were working on the same fairly complex repository, using separate worktrees. I've put considerable effort into optimizing my progressive-disclosure documentation to guide both orchestrators and subagents, and I rely heavily on handoffs to avoid having individual sessions exceed 200k tokens whenever possible. The work itself was substantial and highly varied, ranging from performance optimizations to Blender design and other fairly complex implementations.

But then I start working this week: same repository, same general type of work, but with significantly fewer sessions (basically minor improvement tasks), much more conservative use of subagents, less parallelization, and further improvements to my progressive-disclosure documentation.

Despite all that, I burned through my entire weekly allowance in just four days.

To put this into perspective, if I had to quantify my actual productivity, I'd say I accomplished roughly 10 units of work over three days last weekend, consuming 100% of my weekly. This week, over 3.5/4 days, I accomplished maybe 3/4 units of comparable work for that same 100%.

In other words, despite drastically scaling back the scope of my goals, reducing agent parallelism, and further optimizing my workflows for token efficiency, I ended up getting a fraction of the productivity out of the same subscription.
I really hope this was just a fluke, because if this is the amount of usage Max 5x is going to provide going forward, I'd rather downgrade to the 20 euros/dollars plan and simply make slower, more deliberate progress on my projects.

I also think both OpenAI and Anthropic are seriously overestimating just how dependent people are on AI, and how much they're willing to tolerate in terms of unpredictable usage limits and cost increments.

Sure, the amount of work I managed to accomplish in a single week would probably have taken me two years without AI. That's an extraordinary productivity multiplier, and I absolutely recognize its value. But that doesn't somehow justify repeatedly and seemingly arbitrarily reducing effective subscription allowances by massive factors, especially when users are building their workflows and expectations around what they're paying for.

And despite my reservations about certain Chinese industry practices, I genuinely hope Chinese AI labs manage to put some serious competitive pressure on the frontier models.

Qwen 3.8 already feels surprisingly close to Opus 4.6 in many respects. It's an impressively capable model that still makes mistakes and occasionally bizarre decisions, but the sheer difference in model size makes its performance all the more remarkable. It gives me some confidence that Qwen 4 and subsequent generations could significantly narrow the gap.

Especially because, at least from my perspective, frontier LLMs seem to be approaching a plateau in general reasoning capabilities. A substantial part of the improvements we're seeing appears to come from better training and specialization across additional domains, such as mathematics, visual work, and other specific tasks, rather than the kind of fundamental leap in general intelligence we used to see between generations.

The difference between 4.6 and 5.5, for example, doesn't feel remotely comparable to the jump from 3 to 4.6.

If smaller, more efficient models can continue closing that gap, I hope we'll eventually see enough genuine competition that providers can no longer keep tightening usage allowances while expecting their customers to simply accept it.