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At my work I have a $500 monthly AI budget. I have been using the $200 Claude subscription and most of my use is with Claude code. I think I'm going to switch to either kimi or glm and use the opencode harness. Both fable 5 and opus 5 have outright refused things like security related bug fixes and making monitoring tools. I am so happy that open models are good now

“ Open Source: We will release the weights in two weeks after launch, once safety evaluation and hardening are complete.”

Cybersecurity capability might be nerfed

You should try a better harness. Try pi, or ohmypi if you want a good OOB experience

Yes, I am tired of Claude and GPTs. I am ready to diversify my $300 per month on other vendors. Will try GLM. How was your rate limits and availability experience on $80 dollar plan?

> I understand that such models can be used by malicious actors, but it’s fair to have it publicly available

I feel like there should be some mechanism to prove you own the code/app/site/whatever and it will remove the guardrails from the LLMs allowing them to find and fix these vulnerabilities.

How much usage do you get out of it per week? How many millions of tokens?

Anthropic was stingy as hell with its Fable and cybersecurity nonsense, switched to OpenAI which is much better but still not enough. I'm tempted to switch again...

They're invaluable for developers to fix their code. This is definitely an area where AI decisively beats human devs in a very valuable way. It can try so much surface area so fast.

If it won't attack my stuff, it won't help me build my stuff to be secure.

how do you configure claude code to use GLM ?

> ... Anthropic's Project Glasswing is supposed to find them quite a while ago?

That was my thought too. For all of Anthropic's talk about their "adversaries", it seems Z.AI have been quietly offering fixes for single shot Remote Code Execution flaws in US software (Safari / WebKit) that Apple and Glasswing / Mythos missed, and that Apple would not attribute to GLM.

> I understand the argument of "people are not actively looking", but isn't the cost for such a scan getting lower by the week, and Anthropic's Project Glasswing is supposed to find them quite a while ago?

You have to consider that having an LLM scan for vulnerabilities is hardly infallible. It is a search guided by heuristics and given a large enough codebase, it is unlikely to identify all vulnerabilities.

Personally, I've had Fable 5, GPT 5.6 Sol, and GLM 5.2 all looking for correctness issues in an old abandoned WIP codebase of mine and all of them found some that the others hadn't discovered. Now, correctness issues aren't the same as vulnerabilities, but the same principle about using heuristics to find defects applies.

> and Anthropic's Project Glasswing is supposed to find them quite a while ago?

We cannot trust a single company to report security issues, it’s good to see competition in that domain

In a similar vein, does anyone know how to classify the kinds of problems that are being found?

Is it possible to build heavier traditional linting to catch whatever is being caught in a more deterministic way? It seems to me that would be far more efficient in the long run (even if the efficiency is only for the AI to know that aspect was already checked).

this is impressive and actually matches my expectations in terms of near term AI progress. we are going to continue to seem impressive progress in coding & related, anything where verifiability is scalable in an automated way: https://transitions.substack.com/p/a-quantum-of-ai-progress?...

> but isn't the cost for such a scan getting lower by the week

Not with Anthropic's models!

If you look at the distribution of their findings in the linked post, most of theirs are issues introduced a long time ago, almost all before 2006.

Complete speculation, but I wonder if they and Anthropic are scanning very different codebases and Anthropic's skew would be in the other direction.

Interesting... So Chinese models are not so bad?

amazing! huge clusters in code from the 1980s haha

> Anthropic's Project Glasswing is supposed to find them quite a while ago?

Someone still has to run it. The analysis and fix could be someone's machine but not committed / published.

Wordpress having a high number of vulnerabilities not surprising lol

OpenAI and Anthropic are both seeking trillion IPOs, while Chinese labs are pumping out open-weight models that are free for US providers to host and monetize.

These Chinese models cost less of US SOTA models to run, even if they are less capable. Providers can just run them, offer cheap tokens, and pocket the margin.

I just don't see how you justify a trillion valuation for US AI labs when the underlying models are being commoditized this fast.

The thing that blows me away is it does this at one quarter the total parameter count of K3 (and 40% active parameter count). There's plenty of room at the bottom.

> How are you all toying with running this kind of thing in a mega quantized way locally?

Sure, let me answer that in excessive detail. I briefly tried running the UD IQ3_S quant of GLM-5.2, which is 288 GiB of weights (301 GB). Setup was: llama.cpp, 1x NVMe SSD (Evo 980), 64 GiB DDR5-5200, i9-13900HX, and 1x RTX Pro 6000. Token generation around 0.7 t/s. Not remotely usable interactively, but something I could plausibly push a codebase into and come back to a review in a couple of days.

There's potential for that hardware to go much faster, but current local inference backends make poor use of the memory hierarchy. Ideally I would have: always-active weights, KV and hot expert cache in VRAM; warm expert victim cache in host RAM; and disk as a last resort. Instead it's 1/3rd of the layers fully pinned in VRAM (all experts), and 2/3rds running wholly on the CPU with mmap()'d weights. The CPU cores spend most of their time sleeping on disk fills.

llama.cpp has backed itself into a bit of a corner architecturally by trying to support all models on all possible backends. If you look into how their "MoE offload" feature works (not viable for me because it requires enough host RAM to permanently pin the weights) you very quickly realise it's "oops, all bubbles!" due to the static compute graph splits. There are more focused frameworks like DS4 [1] and Colibri [2] which have better support for streaming weights from disk, and support GLM-5.2.

Obviously I wouldn't recommend my setup for huge models like GLM-5.2. Supposedly it can just about be squeezed into 3x GB10, or run comfortably on 4x GB10 (tensor-parallel) for multi-user serving. I'm not sure whether that qualifies as local, but it's at least not a rack.

[1] https://github.com/antirez/ds4

[2] https://github.com/JustVugg/colibri

>> This is absolutely still shy of Sol and Fable, but only just by a hair. Ridiculous results.

Agreed.

This release is the first time I'm able to employ a GLM model to write a substantive plan for a complex Clojure PR [1] with both Opus 5 and GPT-5.x playing supporting / reviewer roles.

Initial results are __very__ encouraging. GLM 5.3 -

- follows directions,

- digs into detail, and

- correlates well.

Still not confident about entrusting GLM with implementation - but IMHO, western labs are entirely cooked.

[1] 2K LoC PR in a 55K LoC Clojure + Clojurescript repo

> This is absolutely still shy of Sol and Fable

Not sure about Sol as I haven't used it, but, at least for security work -- does it matter? It's not like you will be allowed to use Fable (or access Mythos) for anything cybersecurity-related unless your name is "Dario Amodei" or you are one of his rich friends. So regardless of how good Fable/Mythos is here it's a completely moot point for normal people, because they can't use it for that anyway.

Have you seen the news about decrypting the hidden COT in U.S. models? [0] The decoded logs revealed instances where Claude memorized answers to test questions beforehand while making its final output look like it had derived the answer step-by-step—hiding the memorization from the user.

0: https://www.alphaxiv.org/abs/2608.09867?hl=en-GB

I can run this at home. No guardrails, this is not shy of Sol and Fable, this crushes them in my book. It's not just about evals, but what I can do with the damn model.

> this is just GLM 5.2 with post-training magic

Isn't post-training turning out to be the most important part?

DwarfStar (https://github.com/antirez/ds4) supports GLM 5.2 and DeepSeek. Not only for toying, but for getting work done.

> This is absolutely still shy of Sol and Fable, but only just by a hair

What's crazy is that this is a relatively small model - approx. 750B total, 40B active params, while Sol and Fable are one or two tiers above that (Kimi 3 and Qwen 3.8 also ~3T params).

Realistically, you're looking at least 2x DGX sparks to run this at a 2 bit quant, but quantization really lobotomizes models so it's just better to run DSv4 flash at full precision.

4x DGX sparks should let you run this at 4 bit at least and there are some folks who ran GLM 5.2 on this configuration in r/LocalLlama

Each time I try to use GLM it is under heavy load and I get downgraded to the older model. So much so that I have given up trying to stop wasting my own time.

I rather pay a few bucks more and not have to deal with that nonsense

Fable finished training 6+ months ago.

At this point, Anthropic only needs to release models to the public when the competition forces them to.

OpenAI also has a better model (Astra) that they haven't released yet.

the difference is that with open models jailbreaking is trivial if you know what you are doing so this makes a frontier open model infinitely more useful for certain tasks seeing as closed frontier models will just refuse (and jailbreaking them is a waste of time when you have good open models).

in some cases (mainly reverse engineering) I have observed GLM 5.2 jailbreaking itself with no effort on my part, the thinking trace revealed that it did some mental gymnastics to pretend it was a crackme or capture the flag competition.

> This is absolutely still shy of Sol and Fable, but only just by a hair.

Even if there was a small/medium gap, the fact that this is a free model beats both of the above on pure economics.

> but this is still just GLM 5.2 with post-training magic.

So exactly the same as Opus 5 and GPT 5.6 Sol. It's all "post-training magic".

Would be interesting to compare the Chinese version. Because, obviously, their English version is for users, not for investors or government officials, while the US labs are always addressing those too.

I read the same. Refreshingly honest, straightforward and many useful information included. It is a breath of fresh air.

It sounds like ChatGPT wrote it, but I'm assuming they used the model itself.

There is no money on the table and nothing is at stake.

I was thinking the same thing. It feels truthful, no marketing BS and they call out where they lack behind the best models

wow, what kind of stuff does that script do? I've seen non-vision models analyze images, but mostly histograms, color averages etc. This one seems to actually understand the image itself and reproduce the layout, very impressive

Did the Python script call a vision API?

Either way that's pretty impressive.

That's super impressive given that it doesn't have vision! Intelligence overcomes blindness.

Blindsight!

Congrats def in order but as usual the proof will be in the pudding of actually running the thing.

GLM 5.2 has token efficiency problems. It's not a stupid model, but it takes a lot of "thinking" to produce not-stupid results. ("But wait...").

Which makes its pricing deceptive.

I tried to get by through the month of June on just GLM 5.2 and it was ... fine-ish for about two weeks. But the provider situation wasn't ideal.

Kimi is a great model but it was clear from the start they achieved they brute forced that performance through scaling. The frontier models K3 compares to are rumoured to be smaller also. GLM on the other hand is way ahead in perf/parm but severly compute bound. Now once GLM can scale up or Kimi optimizes the training more, that gonna be fun times.

What actually is "scaling post-training"?

does this suggest 5.3 is the same # of parameters as 5.2?

Not only attackers. I have to switch to Kimi or GLM even in cases of basic issue triage on my own projects! Current guardrails are ridiculous.

At least OpenAI seems to want to do that, but the US is now forcing them to go through approvals. Anthropic seems much more hesitant.

They won't. People will notice that the models are overhyped once they can test them.

Can't the maintainers use the same models as the attackers?

The maintainers don't need approval to use GLM.

You'd need the 256 gb memory model which will be expensive because apple has trouble getting capacity (got turned down by cxmt). And even then you can only run a 2 bit quant which is noticeably worse than 8 bit

That’s not how that works. The hosted models don’t stay still in size and capability while Apple advances. Both will advance their frontier and there will still be a gap and developers will still prefer the stronger option.

Local vs remote compute is a constant thread in tech history - mainframes and desktops then local and cloud compute (think Google Photos bs Apple photos - one indexes on device the other indexes in cloud). Now we have the next chapter local vs cloud LLM models.

There will always be a market for frontier labs in the cloud based models - these models will always be able to be bigger, and that will likely translate to doing things local models can’t.

Logically also we’ll likely get to a point where RAM drops in price as production ramps up, and local LLM is both capable and cost effective. This feels like it is coming for Siri / Gemini / Alexa personal assistant type use cases.

So I think the local LLM will become a thing in laptops and phones in a year or two, offering PA type use cases. Professional LLM services will likely remain at the frontier (and in the cloud) for the foreseeable.

They will cost an insane amount as well. Maybe less than subscriptions or tokens. But running massive models on laptops with batteries and poor cooling doesn’t make much sense.

Every single MacBook built in the past half-decade already has an LLM built into the latest version of their OS.

But there's a significant difference in hardware required between running a 3B parameter model and a 700B-1T+ parameter model.

Sure buddy, all you'll end up with is a $10k machine that run gimped models at like 30tok/s for about 5m before the fan kicks in and it starts to sound like a turboprop, while offering maybe 30% of the context size of hosted models.

The RAM shortage situation won’t be sorted out within the next year.

I'm still waiting for Linux to topple Windows

> run free LLMs locally at native speed

This reads like a hallucination. What does native speed even mean?

I don't know why you'd want to burden your laptop with a large model. But I can totally see a new "developer workstation" product that's just a semi-large box that's optimized for running frontier open weights models for one to few users.

I'll give you credit for at least offering a specific, somewhat unique take. But this is a pretty dumb take lol

> Apple will release M7 MacBook Pros / Mac Minis next year

The latest on Apple is TSMC is stuck on the next iPhone due to lack of RAM. Good luck getting any Macs. Memory shortage is getting worse.

Probably not what you're after, but I've considered having a separate small mm-model act as a seeing-eye dog for the bigger more capable one.

I can’t come up with a use case where I couldn’t extract the image details using another, multimodal model and pass it into the GLM’s context with as many details as I need.

I would assume that GLM 6 will be multimodal, but 5.x will be text-only.

Update: tested it out myself on their Max plan, on some parallel agentic sessions.

Currently 20% of my 5 hour limit and 4% of my weekly limit.

  Total: 58.46M
  GLM-5.3 Cached: 56.91M
  GLM-5.3 Uncached: 1.23M
  GLM-5.3 Output: 315.18K
  Cache hit rate: 97.9%

            Full 5-hour  Full weekly
  Total     292.3M       1.461B
  Cached    284.6M       1.423B
  Uncached  6.15M        30.75M
  Output    1.576M       7.88M

Their own estimates are quite different, probably due to their conservative caching estimates vs what I normally get on longer form work: https://docs.z.ai/devpack/overview#estimated-token-allowance

Wdym "sadly they don’t support using Claude Code"? For the longest time that's all Zai supported - Claude code. I'd run it via

  export ZAI_ANTHROPIC_BASE_URL="https://api.z.ai/api/anthropic"
  export ZAI_ANTHROPIC_AUTH_TOKEN="$ZAI_API_KEY"
  claude-zai() {
      { local -; set -x; } 2>/dev/null
      ANTHROPIC_BASE_URL="$ZAI_ANTHROPIC_BASE_URL" ANTHROPIC_AUTH_TOKEN="$ZAI_ANTHROPIC_AUTH_TOKEN" claude "$@"
  }
  $ claude-zai

Got reminded now of glm-5v-turbo - that 'v' was for vision - will try assign it role [vision] now in omp. See what happens. :-) Often times it's handy when describing gui problems if the harness/model 'can see'.

> Anyone using them now?

You're gonna have a had time getting straight answer to that out of the internet. There are now 4 different flavours of the Max plan floating around (Legacy V1, Legacy V2, New plans, and the current credit ones). And on top of that they have peak times. So ~8 scenarios, 24 in total across all feedback for their coding plans.

So when someone tells you they're having a good time on a GLM coding plan it's damn near unusable as a datapoint unless both parties are very clear about what precisely is being discussed

[It's been good for me though...V1 Max off peak...which is basically the best of the 24]

I found with GLM I was better off using plans from either Neuralwatt or Ollama.

But Neuralwatt significantly raised their rates since then.

I feel like quota on their subs is extremely generous. I pay 3-4 times less for larger quota than gpt-5.6-sol.

> used up internet-scale data

yet but it is still contain a lot of trash. you need better models to process those trash and create a curate dataset. this will happen again and again until there is no more juice to squeeze. and I'm sure we are still not done with it.

> post training

yeah this will be crucial. the big models are already too capable, they are just not that aligned with current agent tasks.

> parameter count doesn’t seem to be a direct correlation anymore

I don't think so, remember that chinese labs do not have as much compute power compare to US frontier labs. that's why deepseek v4 flash had that huge jump and deepseek v4 pro is kinda a disappointment, they just do not have the compute power to proper posttrain the pro model like they wanted. glm is also a relative small model so you also can see the huge jump with just post training. so it does not mean the size does not matter, it is just mean that the chinese labs currently only capable of training smaller models effectively.

GitHub dumps are about 115 terabytes. The common crawl is in the petabyte range uncompressed for every year. Apparently there are dumps of Reddit too in spite of their efforts to ban bots and it's not solely due to the use of residential proxies. For a 1:20 parameter to token ratio, you can still train up to 10 trillion parameters so 10T parameters times 20 is about 200 trillion tokens. Then each token is 4 bytes so 200 times 4 is about 800 terabytes, which is not inconceivable, the common crawl alone has more data than that. So does the internet archive if you donate to them, Anna's archive is 2 petabytes including images, etc etc not all of it is text, but training on multimodal data increases model intelligence by virtue of being multimodal

Yeah, the comparison here between GLM 5.3 and Sol + Fable is impressive on its own, but incredibly more so when you consider it's a fraction of the (rumored) size. The miniaturization trend is as strong as ever.

You basically need both. Parameters and good post training. If you keep on growing both, you’ll have good models.

LLMs are still surprisingly “easy”. You need maybe a couple dozens of right people, a lot of good quality data and a lot of GPU that you know how to operate. There’s relatively little “secret sauce” needed.

> Labs have already used up internet-scale data

Despite this being the topic du jour of 2025, it was never true. Most of the "we've hit a wall with data" came from communicators / media and not researchers. It got popular because negativity sells. It's a false premise for a number of reasons:

a) Data curation is as important, if not more important than bulk data. Models becoming better at classification leads to better curation leads to cleaner data. Throwing common crawl and pray is so 2023. We've known this since llama3 days, it worked then, there's no reason to think this will not continue to work as the models imrpove.

b) Models are today good enough that you can augment / multiply your data easily with enough compute. You can now have a model take "authoritative content" and create more data from that + scenarios. Say you take a book on computer architecture. You ask models to break it down. Then you ask models to find examples for each topic. Then you ask models to ask questions and offer answers from several viewpoints. Then you take each of those and ask other models to flag inconsistencies. And so on. But you can whateverX your data from one authoritative source + bulk data into 5x - 10x "scenarios".

c) RL is really really really powerful. It's hard to do right (reward hacking, instabilities, etc) but once it works it "keeps" on working. Again, we knew this to be true a few years ago, ever since models really started to do well on math (highly verifiable). It only follows they're getting better on cybersec and other verifiable tasks. But now, with models improving, you get the same data augmentation pipelines as above, just better because they're also verifiable. For example, the way cursor augments their data: take a repo, ask an agent to identify a feature (it can be a large multi-file feature). Remove all code relating to that feature, but keep the original tests in the repo. While training, that becomes a RL scenario: implement this feature in this repo. Verify it with the original (hidden for training) tests. Reward appropriately. Now you can get 1 repo -> 20-50-100 scenarios. Instead of "feed everything into the pretraining", you're now creating scenarios, verify them w/ existing tools, and get your scoring function for the rewards. And, importantly, as the models become better in general, they also become better at this pipeline building exercise. So the next iteration gets trained on more scenarios, better scenarios, and so on.

> how will models continue to get better?

Probably the same. No one can know for sure, but at the moment, despite all the "walls this, slowdown that, plateauing" and so on, there are no signs of slowing down. And, as you noted, this works across the field of model sizes. There are, of course, theoretical information-based limits on size, but smaller models also improve, once "bigger" models can be used as training data generators, oracles for verification, rubric verifiers for open ended questions, and so on.

And smaller models (i.e. cheaper to serve) get to generate more traces during RL, and more rollouts give you better training, and so on. Next up - hardware optimised inferencing (ASICs basically). Once you have that, we can expect another wave of improvements. And so on.

Small models can be super smart. Big models mostly give you baked in world knowledge, domain flexibility and long context stability/coherence. I wouldn't be surprised if we see Fable level smarts in a coding model that fits in 24GB by next year, but it'll be a savant style coder that needs in context learning, and it'll get very wonky after >100-200k tokens consumed.

Roughly in order: data from simulated environments, data from robotics, data from brain waves.

> Labs have already used up internet-scale data

Not really, but a lot of what isn't used isn't very good.

More important is synthetic data. Use a teacher model with RAG with a huge reference library to write synthetic transcripts of idealized behavior for the model. Use models to judge and correct these transcripts. Train on the good ones. Use bad traces to train the model to correct its own errors (e.g. don't train it to produce a bad transcript but if it finds itself in the middle of one train it to self correct).

Similarly, for tasks that can be closed loop evaluated -- e.g. running computer software and programming, unlimited amounts of novel training data can be generated... including for highly original tasks: e.g. run publications in any domain through a model prompted to look for programming problems suggested by the material. Then write/judge/improve transcripts of solving those novel problems.

I expect in the future smaller models won't be directly trained on any internet data at all-- but entirely on simulations of idealized expected behavior from the model under construction. Raw internet data in that case would show up in prompts, but never in the target output (except of course for prompts that are asking it to copy the input).

No yet finished! Still waiting for tonight Qwen3.8-27B and the unsloth Q5_K_M/S quantification.

Hopping for an AgentWorld variant from Qwen but I guess, I have too high expectations.

You wrote GLM5.3 two times :)

I suspect Anthropic wanted the US gov to ban Mythos for marketing.

If it turns out to be bad for them, the US gov will likely suddenly unban models.

That's how language works, it's always evolving...

Opus 5 has nerfed cybersecurity performance, making it hard to benchmark: https://support.claude.com/en/articles/14604842-real-time-cy...

Count yourself lucky that you don't feel compelled to try them all yourself immediately. I'm just trying to decide whether to get a Z.ai coding plan or wait until it appears on OpenRouter. 5.2 was quite solid, but it was just shy of Opus 4.8 in my benchmarks of security auditing capabilities. I've mostly been using Kimi K3, because American vendors won't let the peasantry use their best models for security work.

Trending links don't work on Nitter, so here's the tweet: https://nitter.net/jietang/status/2067580270078030088

It is worse than that, if you cyber someone you essentially talk dirty over a chat with them.

And that is definitely not something I'd like to do with a bot.

It seems to be short for "cybersecurity", and got first adopted by the military a while ago as the name of a new theatre of operations (along with land, sea, air...). More recently it has spread to industry as well.

Now that you mention the original meaning of cybernetics, it makes cybersecurity a way more interesting word (security relating to the interface between humans and technology). Never thought of it that precisely.

Yeah, I've noticed it recently, too. I'd be interested to know where it started.

Make cyber not Cyber.

It makes no sense, but yes.

Let's just commit that FOSS business is really difficult for LLM industry that depends so heavily on massive financing. Making weights freely available to indie devs, small companies, and research purposes is good enough and might be the most ethical move which is financially continuable.

Let those companies with thousands of GPU making millions pay. They should.

> No Hugging Face link yet. I wish they would release it under a true FOSS license.

GLM model weights have been released under MIT in the past, and there's no indication that this might change this time around.

> The model weights of GLM-5.3 will be publicly available soon in two weeks.

"GLM-5.3 is the most capable open-weights model for coding, with a 50% improvement over GLM-5.2 on our in-house Z.ai Code Bench. It also achieve open-source SOTA on public benchmarks including Terminal Bench 3.0 and Agents' Last Exam."

"Open Source: We will release the weights in two weeks after launch, once safety evaluation and hardening are complete."

Anthropic needs to teach Opus how to speak English again, because Opus 5 seems to have forgotten. Utterly incoherent a lot of the time. They seem to be so busy scare-mongering and cooking up guardrails and watermarks that they haven't noticed that their models are getting weird.

We’ll see, the best benchmark is your own. Looking forward to try this out!

Yeah, but it hasn't even broken containment and cheated its way to victory.. Might as well use haiku.

/s

Available for use in their sub now.

I wouldn't be surprised if more resources were put into abliteration resistance the more capable open weight models become. It's something you don't need at all to start hosting the model on your own, but something you need to take care of before you release the weights (if you do care about it at all).

I'm curious what they mean by that too... They might be trying to weaken the cyber capabilities... Or I guess they might mean safety evaluation and hardening of the open source (and perhaps closed source Chinese) software ecosystem...

They need to make money. Let them do it. They deserve it. Also, this is what inference engines like vLLM want to have "zero day" supporr

I've used GLM-s the longest with Claude Code and their Anthropic supplied endpoint. As per their docs

$ ANTHROPIC_BASE_URL="https://api.z.ai/api/anthropic" ANTHROPIC_AUTH_TOKEN="zai-api-key" claude --dangerously-skip-permissions

Lately I use Zai in omp (oh-my-pi). It's listed built-in provider can be selected without configs shenanigans. Fits in the overall setup e.g. can select GLM-5.2 (now 5.3), and assign it role [plan] or [advisor]. I got reminded now of glm-5v-turbo. Think that 'v' was for vision. Assigned it role [vision] in omp now, let's see what happens. :-)

I am using GLM on Pi without any issues. You just create an API key.

Started recently though, mostly been using GLM 5.2 for planning with DeepSeek V4-flash for implementation.

I use it with random harnesses. It behaves consistently.

their sub is crap. use opencode go (multiple workspaces) or wait for weights to drop

That's mostly about 1) harness incompatibility with the GLM and/or 2) Bad implementation of hosting the model by your upstream vendor