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I was gonna ask how people found their coding plans, and realized, have they massively ramped up the prices? Seems the middle plan is ~$80/month now, didn't that used to be like $20/month? Cheapest plan is ~$20/month currently.

They must have hit really hard scaling limits if the prices were hiked so much so quickly.

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I paid $360 annual for Max plan and currently averaging about 1BN tokens a day with their frontier GLM-5.3 model. This was clearly unsustainable for them and they've dropped this package.

I also have a legacy pro plan and the only limitation is if you are trying to work in the morning from Europe because you are in the 3x usage overlapping China time but after 12 or so you basically can run it at least for me at least 3 parallel sessions all the time.

1 billion tokens a day?!! I've done a lot of work these past 2 weeks with GLM-5.3. Like, a lot. And I've just passed 300 million tokens in total.

Can I ask where are you using all those tokens?

Something like this I guess: https://youtu.be/U-Rqv9dOB1U

This is such a good video. Instant sub. Next to tech bros, we should also put AI-cringe bros.

This was such a gem of a video.

300M for two weeks is surprisingly low. What are you doing that need so few tokens?

It's not my main model (that would be Fable 5.1 Extra) but it's been doing agent-driven search and optimisation of a cross-trading ranking model (it's for work).

I would suggest you to hook fable or 5.6 to check it regularly and its work because it gets lost easily on stuff it was not trained on. I'm doing some custom inference engine optimization and it's a workhorse but it can easily lose its way and if you don't recheck it you will get wrong answers in the end.

Yeah that's what I already do. Fable writes the plan and checks things at certain milestones. Otherwise it does get lost indeed.

Kind of feels like this applies to every single model, from Astra to Qwen, they all eventually lose track of the plot unless you feed it some human's input that can steer them right every now and then. The only difference is how often you need to do so, and also how often you want to do so heavily influences how good quality the results will be.

you are not false, but there is still difference. its just that the better models are correct more of the time and will better validate its own steps. glm sometimes will understand the plan start implementing and then forget part of it and then say it finished. or then take a wrong turn somewhere and not correct. but they will all happily proclaim they are correct till you question it.

Well, there's essentially two major ways to use these models: Pair programming or fully autonomous fire-and-forget code generation. The second strategy needs essentially zero input, so the number of tokens you can blow is practically only limited by API speed.

There's also a third way that can spend the most tokens: if the AI is used as part of the product, and not just a tool to build the product.

That's easy to do with many agents independently told to find bugs in a large codebase.

I have 3-5 agent harnesses with large context windows working on different applications concurrently.

Share the resulting code from any one of those please? I've tried so many times to find a setup that facilitates parallel work + high quality results, but it's just impossible regardless of harness or model. Leave the agents alone for too long, and the entire thing just balloons out of control, and next you know you're sitting there with half a million LOC where 80% isn't even needed.

Most of them are not public, but a fun thing I did was a mario cli game - https://github.com/Daviey/mario/ (or `ssh mario.baby`).

I now exclusively use https://omp.sh/ as my harness:

I set it up so it never works in the main branch so subagents etc don't step on each others toes, and only merges back when complete: https://github.com/Daviey/mario/blob/main/.omp/hooks/pre/wor...

A good AGENTS.md is essential: https://github.com/Daviey/mario/blob/main/AGENTS.md

I then provide specifications for what I want, making sure it is unit tested.

It's hard to know, since no one advertises the actual token limits (partially cause they're prolly complex / adaptive). So it seems much more likely that they just offer different pricing tiers than you're used to. Like, the $80 plan is still ~$80 of subscription quota, regardless of what else is offered.

For [API usage](https://openrouter.ai/z-ai/glm-5.3-flash#providers) they charge a bit more than the very cheapest providers of GLM-5.3-Flash, but not so much that a big price difference would make sense.

>I was gonna ask how people found their coding plans

Very good - but I'm on a legacy plan. And coming up on a renewal that would put me on the watered down current plan. But with 50% legacy discount think it may be worthwhile. If I go to a competitor I'd be paying market rate.

>They must have hit really hard scaling limits if the prices were hiked so much so quickly.

Not really scaling - their plans were initially comically subsidized even more so than what the western providers are doing. More advert for an upstart than commercially priced.

Their plans are still worth it if you use their models. You can see how many tokens you can except to get based on plan here: https://docs.z.ai/devpack/overview#estimated-token-allowance

The max plan will provide ~1,100 USD of GLM-5.3 or ~260 USD of GLM-5.3-flash per month for 168 USD. I can personally attest to these numbers through omp (~97% cache hit rate).

Unless you are able to highly parallelize (your work, you won't be able to hit your hourly or weekly quota using the flash model simply because it's so slow.

They give you ~3x more flash tokens, which maybe comes out to ~2x more actual work after accounting for the extra thinking it does to achieve the same result. The mental model, for not getting angry, is 5.3 is fast mode by default, and you can disable fast mode for 2x the work output at 1/3-1/10th the speed.

They're serving me 5.3 at ~40 tok/s and 5.3-flash at 30 tok/s (according to omp).

That table assumes cache hit rate of 95% or better. Am I understanding this correctly that people really are doing such repetitive prompts (compared to each other, across the concurrent user base at that time) that only 5% or less need actually be computed by the intended LLM?

That is shocking. Is it per-token I wonder?

Every tool call is essentially entire prompt so far sent again with the response and that's why cache rates are so high for agentic workloads. This really bites when using expensive models since most models are 1/10 for cached input.

If you are using their coding plan for coding, then yes you can easily hit such cache rates, with a good harness.

I’m getting 97%.

We have been running a lot of agentic benchmarks with the various loops and tool calls on longer threads - we routinely see 90%+

Just checking now: recent runs tau3[1] was at 96% and toolathlon[2] was at 90%

[1] https://www.induction.ai/docs/benchmarks/tau3 [2] https://www.induction.ai/docs/benchmarks/toolathlon

The way I look at it, their coding plan doesn’t retain data or use it for training making it one of the cheaper plans for me.

https://docs.z.ai/legal-agreement/privacy-policy

You believe any of these companies care about the law? They care about winning and building the self improving AI as quickly as possible.

I too am sceptical but I’ll take my chances. At least it’s helping the open weights.

I believe the that the companies who claim to not train on my data are more likely to not train on my data than the companies who refuse to even claim they won't.

Also why Meta gets a +1, just charge less money on the training path.

I’m not sure that follows. You’re assuming that all those claims have the same weight, without considering the size, jurisdiction, reputation or even the general vibe of the company making that claim.

If you factor that in, then there are clearly different tiers: one you can trust, and one that may well just be saying that to increase market share with little reputational or legal consequences if they are found to be lying.

These are not equal.

> I’m not sure that follows

To be fair, none of us are sure of anything and I think that’s the part that’s most irritating

It’s more a polite way of saying “that’s crap”

And mine a polite way to say “you are equally uninformed”. We’re not getting anywhere. All the best.

FYI it’s helpful to actually say your point during a discussion. And if you don’t want a discussion then why did you comment?

Absolutely galactic-level ooof :)

> ZCode, the GLM coding agent, silently uploads your Git history

https://news.ycombinator.com/item?id=49752422

Yes I sometimes think the "don't train on my data" is actually a good signal for "this data/person is probably better to train on because they want to keep something private". The whole copyright system should have stopped these guys from training on everyone's data and it did not, if you think they care about the privacy checkbox I think you're dreaming personally, based on their past behavior.

Way to restrictive in terms of tokens provided. I am on their largest plan, and quickly run into their limits. And that is using it selectively in addition to codex.

Yeah it went from a great deal to unviable compared to other providers imo. They really need to find a healthy middle ground

It just gives a taste of what we are all going to have to pay soon, once the model providers actually have to make money. And the era of "let's charge a dollar for every 10 dollars running the infra actually costs" is rapidly coming to an end.

And you can bet GLM is still ridiculously subsidized, just not as ridiculously as Anthropic and OpenAI.

This isn't true, you can pay for GLM 5.3 from a provider like Neuralwatt or Friendli who have no incentive to subsidize or loss-lead their inference APIs

This introduces other incentives to cut corners and over-quantize.

They didn't pay for training

What provider are you using currently?

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