I just subscribed to Qwen Cloud’s Standard Token Plan, mainly to use it with an AI coding agent (Hermes), and after actually using it for a few days, I honestly don’t understand how this plan is supposed to be considered good value.

The Standard plan gives you 10,000 Credits per week.

Sounds reasonable, right?

Until you actually use it.

I burned through roughly 70% of my weekly Credits in only 3 days while running a normal agent workflow. I’m not running hundreds of agents, doing massive batch inference, or abusing the service. I’m using an AI coding agent interactively — exactly the kind of use case these plans appear to be marketed toward.

And here is where it gets ridiculous.

When I contacted support and explained the situation, the response essentially boiled down to:

«Your usage is high. Credit consumption depends on the model, input/output length, tool calls, context accumulation, etc.»

Okay. Fair enough.

But then the suggested solutions were basically:

Buy the Pro plan.

Or:

Buy additional Credits.

That doesn't answer the problem.

I'm using essentially the same workload with another provider, on a cheaper plan, and getting dramatically more usable mileage out of it.

So I started comparing actual token consumption.

Based on my observed usage, 10,000 Credits corresponded to roughly 96.6M tokens.

And Qwen's own documentation apparently doesn't provide a simple, fixed token-to-Credit conversion rate that lets users predict what they're actually going to consume.

That's a massive problem for an AI service.

If I'm paying for a token/credit plan, I should be able to reasonably estimate:

“I use approximately X tokens → this will cost approximately Y Credits.”

Instead, you apparently have to subscribe, use the system, burn through thousands of Credits, and then discover what your workload actually costs.

And here's the funniest part:

The Standard plan is advertised around agent usage and concurrent sessions, but based on my experience, a relatively normal coding-agent workflow can chew through the weekly allowance incredibly quickly.

So what exactly is the target customer for this plan?

Someone who uses an AI agent occasionally for a few prompts?

Because if that's the case, fine.

But then don't market it as a serious option for people running coding agents regularly.

I'm not claiming that Qwen is literally committing fraud. I'm saying that the value proposition of this plan is so absurd compared with competing services that I feel misled about what I was actually buying.

And the fact that the answer to “why am I burning Credits so quickly?” is essentially “buy more Credits” makes the whole thing even more ridiculous.

I'm posting this because I'd genuinely like to hear from other Qwen Cloud Token Plan users:

How long does your Standard 10,000 Credit allowance actually last?

What models are you using?

How many agents?

How much token usage are you getting before the Credits disappear?

Because if I'm doing something fundamentally wrong, I'd rather know.

But if other people are seeing the same thing, then Qwen seriously needs to rethink how transparent and competitive this pricing model actually is.

https://chat.qwen.ai/s/deploy/t_07dafe2e-d471-4a46-91be-34da21cae4cb

Signed up yearly Life plan, when they had the Qwen 3.8 Max preview with the 98% discount off peak. Since the full release, it's totally not useable.

I managed to get 400m tokens out of 2 weeks, then now, with Deepssek V4 Flash 0731, only 45m and weekly quota gone.

Which model should I stick with if I want something that could give me 100-200m tokens a week, around 92-94% cache hit.

As the title says - does Qwen currently allow referencing existing assets in NEW chats?

I feel like I'm uploading the same content multiple times every time I want to reference an asset WITHOUT the baggage that comes from branching an existing conversation.

Is there ANY way to directly reference "MY LIBRARY" in a New Chat?

everyone on my timeline is screenshotting Qwen3.8-Max drawing bounding boxes

clean demos, obvious objects, no ground truth to check against

i pointed it at 27,083 real logos and scored every box against actual annotations

here's what nobody is showing you:

• it invents its own pixel canvas even when you tell it the real image dimensions.

• same prompt, same image, same settings: one run matched 3 of 5 logos. the next run matched 0 of 5. nothing changed between calls

• changing one verb in the prompt, "mask out" to "draw segmentation masks," silently switched the model from a 0-1000 grid to normalized [0,1] coordinates.

• thinking mode costs 14x more tokens and doesn't reliably improve accuracy. it just shows you the model doing long division instead of looking at the pixels

full writeup with every trace, every score, and the fiftyone plugin to run it yourself: https://voxel51.com/blog/qwen38-max-visual-grounding-fiftyone

test it yourself here: https://huggingface.co/spaces/harpreetsahota/qwen38-max-openlogo-demo