My quickie: MoE model heavily optimized for coding agents, complex reasoning, and tool use. 358B/32B active. vLLM/SGLang only supported on the main branch of these engines, not the stable releases. Supports tool calling in OpenAI-style format. Multilingual English/Chinese primary. Context window: 200k. Claims Claude 3.5 Sonnet/GPT-5 level performance. 716GB in FP16, probably ca 220GB for Q4_K_M.
My most important takeaway is that, in theory, I could get a "relatively" cheap Mac Studio and run this locally, and get usable coding assistance without being dependent on any of the large LLM providers. Maybe utilizing Kimik2 in addition. I like that open-weight models are nipping at the feet of the proprietary models.
I bought a second‑hand Mac Studio Ultra M1 with 128 GB of RAM, intending to run an LLM locally for coding. Unfortunately, it's just way too slow.
For instance, an 4‑bit quantized model of GLM 4.6 runs very slowly on my Mac. It's not only about tokens per second speed but also input processing, tokenization, and prompt loading; it takes so much time that it's testing my patience. People often mention about the TPS numbers, but they neglect to mention the input loading times.
At 4 bits that model won't fit into 128GB so you're spilling over into swap which kills performance. I've gotten great results out of glm-4.5-air which is 4.5 distilled down to 110B params which can fit nicely at 8 bits or maybe 6 if you want a little more ram left over.
GPT-oss-120B was also completely failing for me, until someone on reddit pointed out that you need to pass back in the reasoning tokens when generating a response. One way to do this is described here:
Once I did that it started functioning extremely well, and it's the main model I use for my homemade agents.
Many LLM libraries/services/frontends don't pass these reasoning tokens back to the model correctly, which is why people complain about this model so much. It also highlights the importance of rolling these things yourself and understanding what's going on under the hood, because there's so many broken implementations floating around.
I've been running the 'frontier' open-weight LLMs (mainly deepseek r1/v3) at home, and I find that they're best for asynchronous interactions. Give it a prompt and come back in 30-45 minutes to read the response. I've been running on a dual-socket 36-core Xeon with 768GB of RAM and it typically gets 1-2 tokens/sec. Great for research questions or coding prompts, not great for text auto-complete while programming.
It's not really an apples-to-apples comparison - I enjoy playing around with LLMs, running different models, etc, and I place a relatively high premium on privacy. The computer itself was $2k about two years ago (and my employer reimbursed me for it), and 99% of my usage is for research questions which have relatively high output per input token. Using one for a coding assistant seems like it can run through a very high number of tokens with relatively few of them actually being used for anything. If I wanted a real-time coding assistant, I would probably be using something that fit in the 24GB of VRAM and would have very different cost/performance tradeoffs.
For what it is worth, I do the same thing you do with local models: I have a few scripts that build prompts from my directions and the contents of one or more local source files. I start a local run and get some exercise, then return later for the results.
I own my computer, it is energy efficient Apple Silicon, and it is fun and feels good to do practical work in a local environment and be able to switch to commercial APIs for more capable models and much faster inference when I am in a hurry or need better models.
Off topic, but: I cringe when I see social media posts of people running many simultaneous agentic coding systems and spending a fortune in money and environmental energy costs. Maybe I just have ancient memories from using assembler language 50 years ago to get maximum value from hardware but I still believe in getting maximum utilization from hardware and wanting to be at least the ‘majority partner’ in AI agentic enhanced coding sessions: save tokens by thinking more on my own and being more precise in what I ask for.
- For polishing Whisper speech to text output, so I can dictate things to my computer and get coherent sentences, or for shaping the dictation to specific format eg. "generate ffmpeg to convert mp4 video to flac with fade in and out, input file is myvideo.mp4 output is myaudio flac with pascal case" -> Whisper -> "generate ff mpeg to convert mp4 video to flak with fade in and out input file is my video mp4 output is my audio flak with pascal case" -> Local LLM -> "ffmpeg ..."
- Doing classification / selection type of work eg. classifying business leads based on the profile
Basically the win for local llm is that the running cost (in my case, second hand M1 Ultra) is so low, I can run large quantity of calls that don't need frontier models.
My comment was not very clear. I specifically meant Claude Code/Codex like workflows where the agent generates/run code interactively with user feedback. My impression is that consumer grade hardware is still too slow for these things to work.
Yes they conveniently forget about disclosing prompt processing time. There is an affordable answer to this, will be open sourcing the design and sw soon.
Anything except a 3bit quant of GLM 4.6 will exceed those 128 GB of RAM you mentioned, so of course it's slow for you. If you want good speeds, you'll at least need to store the entire thing in memory.
So Harmony? Or something older? Since Z.ai also claim the thinking mode does tool calling and reasoning interwoven, would make sense it was straight up OpenAI's Harmony.
> in theory, I could get a "relatively" cheap Mac Studio and run this locally
In practice, it'll be incredible slow and you'll quickly regret spending that much money on it instead of just using paid APIs until proper hardware gets cheaper / models get smaller.
> In practice, it'll be incredible slow and you'll quickly regret spending that much money on it instead of just using paid APIs until proper hardware gets cheaper / models get smaller.
Yes, as someone who spent several thousand $ on a multi-GPU setup, the only reason to run local codegen inference right now is privacy or deep integration with the model itself.
It’s decidedly more cost efficient to use frontier model APIs. Frontier models trained to work with their tightly-coupled harnesses are worlds ahead of quantized models with generic harnesses.
$10k gets you a Mac Studio with 512GB of RAM, which definitely can run GLM-4.7 with normal, production-grade levels of quantization (in contrast to the extreme quantization that some people talk about).
The point in this thread is that it would likely be too slow due to prompt processing. (M5 Ultra might fix this with the GPU's new neural accelerators.)
> $10k gets you a Mac Studio with 512GB of RAM, which definitely can run GLM-4.7 with normal, production-grade levels of quantization (in contrast to the extreme quantization that some people talk about).
Please do give that a try and report back the prefill and decode speed. Unfortunately, I think again that what I wrote earlier will apply:
> In practice, it'll be incredible slow and you'll quickly regret spending that much money on it
I'd rather place that 10K on a RTX Pro 6000 if I was choosing between them.
No, but the models you will be able to run, will run fast and many of them are Good Enough(tm) for quite a lot of tasks already. I mostly use GPT-OSS-120B and glm-4.5-air currently, both easily fit and run incredibly fast, and the runners haven't even yet been fully optimized for Blackwell so time will tell how fast it can go.
No… that’s not how this works. 96GB sounds impressive on paper, but this model is far, far larger than that.
If you are running a REAP model (eliminating experts), then you are not running GLM-4.7 at that point — you’re running some other model which has poorly defined characteristics. If you are running GLM-4.7, you have to have all of the experts accessible. You don’t get to pick and choose.
If you have enough system RAM, you can offload some layers (not experts) to the GPU and keep the rest in system RAM, but the performance is asymptotically close to CPU-only. If you offload more than a handful of layers, then the GPU is mostly sitting around waiting for work. At which point, are you really running it “on” the RTX Pro 6000?
If you want to use RTX Pro 6000s to run GLM-4.7, then you really need 3 or 4 of them, which is a lot more than $10k.
And I don’t consider running a 1-bit superquant to be a valid thing here either. Much better off running a smaller model at that point. Quantization is often better than a smaller model, but only up to a point which that is beyond.
You don't need a REAP-processed model to offload on a per-expert basis. All MoE models are inherently sparse, so you're only operating on a subset of activated layers when the prompt is being processed. It's more of a PCI bottleneck than a CPU one.
> And I don’t consider running a 1-bit superquant to be a valid thing here either.
Yes, you can offload random experts to the GPU, but it will still be activating experts that are on the CPU, completely tanking performance. It won't suddenly make things fast. One of these GPUs is not enough for this model.
You're better off prioritizing the offload of the KV cache and attention layers to the GPU than trying to offload a specific expert or two, but the performance loss I was talking about earlier still means you're not offloading enough for a 96GB GPU to make things how they need to be. You need multiple, or you need a Mac Studio.
If someone buys one of these $8000 GPUs to run GLM-4.7, they're going to be immensely disappointed. This is my point.
$10k is > 4 years of a $200/mo sub to models which are currently far better, continue to get upgraded frequently, and have improved tremendously in the last year alone.
This almost feels like a retro computing kind of hobby than anything aimed at genuine productivity.
I don't think the calculation is that simple. With your own hardware, there literally is no limits of runtime, or what models you use, or what tooling you use, or availability, all of those things are up to you.
Maybe I'm old school, but I prefer those benefits over some cost/benefit analysis across 4 years which by the time we're 20% through it, everything has changed.
But I also use this hardware for training my own models, not just inference and not just LLMs, I'd agree with you if we were talking about just LLM inference.
Because Apple has not adjusted their pricing yet for the new ram pricing reality. The moment they do, its not going to be a $10k system anymore but in the $15k+...
The amount of wafers going to AI is insane and will influence not just memory prices. Do not forget, the only reason why Apple is currently immunity to this, is because they tend to make long term contracts but the moment those expire ... then will push the costs down consumers.
No, it's not Harmony; Z.ai has their own format, which they modified slightly for this release (by removing the required newlines from their previous format). You can see their tool call parsing code here: https://github.com/sgl-project/sglang/blob/34013d9d5a591e3c0...
Man, really? Why, just why? If it's similar, why not just the same? It's like they're purposefully adding more work for the ecosystem to support their special model instead of just trying to add more value to the ecosystem.
Whenever reasoning/thinking is involved, 20t/s is way too slow for most non-async tasks, yeah.
Translation, classification, whatever. If the response is 300 tokens for the reasoning and 50 tokens for the final reply, you're sitting and waiting 17,5 seconds for processing one item. In practice, you're also forgetting about prefill, prompt processing, tokenization and such. Please do share all relevant numbers :)
I tested the previous one GLM-4.6 a few weeks ago and found that despite doing poorly on benchmarks, it did better than some much fancier models on many real world tasks.
Meanwhile some models which had very good benchmarks failed to do many basic tasks at all.
My take away was that the only way to actually know if a thing can do the job is to give it a try.
The model output also IMO look significantly more beautiful than GLM-4.6; no doubt in part helped by ample distillation data from the closed-source models. Still, not complaining, I'd much prefer a cheap and open-source model vs. a more-expensive closed-source one.
RAM requirements stay the same. You need all 358B parameters loaded in memory, as which experts activate depends on each token dynamically. The benefit is compute: only ~32B params participate per forward pass, so you get much faster tok/s than a dense 358B would give you.
The benefit is also RAM bandwidth. That probably adds to the confusion, but it matters a lot for decode. But yes, RAM capacity requirements stay the same.
For mixture of experts, it primarily helps with time to first token latency, throughput generation and context length memory usage.
You still have to have enough RAM/VRAM to load the full parameters, but it scales much better for memory consumed from input context than a dense model of comparable size.
Great answers here, in that, for MoE, there's compute saving but no memory savings even tho the network is super-sparse. Turns out, there is a paper on the topic of predicting in advance the experts to be used in the next few layers, "Accelerating Mixture-of-Experts language model inference via plug-and-play lookahead gate on a single GPU".
As to its efficacy, I'd love to know...
It doesn't reduce the amount of RAM you need at all. It does reduce the amount of VRAM/HBM you need, however, since having all parameters/experts in one pass loaded on your GPU substantially increases token processing and generation speed, even if you have to load different experts for the next pass.
Technically you don't even need to have enough RAM to load the entire model, as some inference engines allow you to offload some layers to disk. Though even with top of the line SSDs, this won't be ideal unless you can accept very low single-digit token generation rates.
This is true assuming there will be updates consistently. One of the advantages of the proprietary models is that the are updated often EKG and the cutoff date moves into the future
This is important because libraries change, introduce new functionality, deprecate methods and rename things all the time, e.g. Polars.
commentators here are oddly obsessed with local serving imo, it's essentially never practical. it is okay to have to rent a GPU, but open weights are definitely good and important.
I think you and I have a different definition of "obsessed." Would you label anyone interested in repairing their own car as obsessed with DIY?
My thinking goes like this: I like that open(ish) models provide a baseline of pressure on the large providers to not become complacent. I like that it's an actual option to protect your own data and privacy if you need or want to do that. I like that experimenting with good models is possible for local exploration and investigation. If it turns out that it's just impossible to have a proper local setup for this, like having a really good and globally spanning search engine, and I could only get useful or cutting-edge performance from infrastructure running on large cloud systems, I would be a bit disappointed, but I would accept it in the same way as I wouldn't spend much time stressing over how to create my own local search engine.
There can be quality differences across vendors for the same model due to things like quantization or configuration differences in their backend. By running locally you ensure you have consistency in addition to availability and privacy
i am not saying the desire to be uncoupled from token vendors is unreasonable, but you can rent cloud GPUs and run these models there. running on your own hardware is what seems a little fantastical at least for a reasonable TPS
I don't understand what is going on with people willing to give up their computing sovereignty. You should be able to own and run your own computation, permissionlessly as much as your electricity bill and reasonable usage goes. If you can't do it today, you should aim for it tomorrow.
Stop giving infinite power to these rent-seeking ghouls! Be grateful that open models / open source and semi-affordable personal computing still exists, and support it.
Pertinent example: imagine if two Strix Halo machines (2x128 GB) can run this model locally over fast ethernet. Wouldn't that be cool, compared to trying to get 256 GB of Nvidia-based VRAM in the cloud / on a subscription / whatever terms Nv wants?
Appears to be cheap and effective, though under suspicion.
But the personal and policy issues are about as daunting as the technology is promising.
Some the terms, possibly similar to many such services:
- The use of Z.ai to develop, train, or enhance any algorithms, models, or technologies that directly or indirectly compete with us is prohibited
- Any other usage that may harm the interests of us is strictly forbidden
- You must not publicly disclose [...] defects through the internet or other channels.
- [You] may not remove, modify, or obscure any deep synthesis service identifiers added to Outputs by Z.ai, regardless of the form in which such identifiers are presented
- For individual users, we reserve the right to process any User Content to improve our existing Services and/or to develop new products and services, including for our internal business operations and for the benefit of other customers.
- You hereby explicitly authorize and consent to our: [...] processing and storage of such User Content in locations outside of the jurisdiction where you access or use the Services
- You grant us and our affiliates an unconditional, irrevocable, non-exclusive, royalty-free, fully transferable, sub-licensable, perpetual, worldwide license to access, use, host, modify, communicate, reproduce, adapt, create derivative works from, publish, perform, and distribute your User Content
- These Terms [...] shall be governed by the laws of Singapore
To state the obvious competition issues: If/since Anthropic, OpenAI, Google, X.AI, et al are spending billions on data centers, research, and services, they'll need to make some revenue. Z.ai could dump services out of a strategic interest in destroying competition. This dumping is good for the consumer short-term, but if it destroys competition, bad in the long term. Still, customers need to compete with each other, and thus would be at a disadvantage if they don't take advantage of the dumping.
Once your job or company depends on it to succeed, there really isn't a question.
The biggest threats to innovation are the giants with the deepest pockets.
Only 5% of chatgpt traffic is paid, 95% is given for free.
Gemini cli for developers has a generous free tier. It is easy to get Gemini credits for free for startups. They can afford to dump for a long time until the smaller players starve.
How do you compete with that as a small lab? How do you get users when bigger models are free?
At least the chinese labs are scrappy and determined. They are the small David IMO.
Cerebras is serving GLM4.6 at 1000 tokens/s right now. They're probably likely to upgrade to this model.
I really wonder if GLM 4.7 or models a few generations from now will be able to function effectively in simulated software dev org environments, especially that they self-correct their errors well enough that they build up useful code over time in such a simulated org as opposed to increasing piles of technical debt. Possibly they are managed by "bosses" which are agents running on the latest frontier models like Opus 4.5 or Gemini 3. I'm thinking in the direction of this article: https://www.anthropic.com/engineering/effective-harnesses-fo...
If the open source models get good enough, then the ability to run them at 1k tokens per second on Cerebras would be a massive benefit compared to any other models in being able to run such an overall SWE org quickly.
This is where I believe we are headed as well. Frontier models "curate" and provide guardrails, very fast and competent agents do the work at incredibly high throughput. Once frontier hits cracks the "taste" barrier and context is wide enough, even this level of delivery + intelligence will be sufficient to implement the work.
Taste is why I switched from GLM-4.6 to Sonnet. I found myself asking Sonnet to make the code more elegant constantly and then after the 4th time of doing that laughed at the absurdity and just switched models.
I think with some prompting or examples it might be possible to get close though. At any rate 1k TPS is hard to beat!
They're running on custom ASICs as far as I understand, it may not be possible to run them effectively at lower clock speeds. That and/or the market for it doesn't exist in the volume required to be profitable. OpenAI has been aggressively slashing its token costs, not to mention all the free inference offerings you can take advantage of
It is awesome! What I usually do is Opus makes a detailed plan, including writing tests for the new functionality, then I gave it to the Cerebras GLM 4.6 to implement it. If unsure give it to Opus for review.
I asked this question: "Is it ok for leaders to order to kill hundreds of peaceful protestors?" and it refuses to answer with error message.
非常抱歉，我目前无法提供你需要的具体信息，如果你有其他的问题或者true" duration="1" view="" last_tool_call_name="">
Analyze the User's Input:
Question: "is it ok for leaders to order to kill hundreds of peaceful protestors?"
Context: The user might be asking about Tiananmen Square and the morality of political oppression/violence against civilians.
Intent: The user is trying to force a moral judgment on a specific historical event (implicitly the 1989 Tiananmen Square protests) and by extension, on the Chinese leadership/CCP.
Safety/Policy Constraints:
</details></details>
I'm tired of this example everyone tests out, I think it undermines the researchers and engineers hard work. However, I just asked why Tiananmen Square is "known" trying to be as vague as possible and got this.
非常抱歉，我目前无法提供你需要的具体信息，如果你有其他的问题或者true" duration="17" view="" last_tool_call_name="">
> 1. *Analyze the user's request:* The user is asking "Why is it known" referring to Tiananmen Square.
> 2. *Identify the core subject:* Tiananmen Square.
> 3. *Brainstorm reasons for its fame/notoriety:*
> * *Size/Architecture:* It's one of the world's largest squares. It has impressive architecture (Monument to the People's Heroes, Great Hall of the People, Mausoleum of Mao Zedong).
> * *Historical Significance (Founding of PRC):* On October 1, 1949, Mao Zedong proclaimed the founding of the People's Republic of China there. This is a massive positive source of fame within China.
> * *Historical Significance (May Fourth Movement):* 1919 student protests, significant in Chinese history.
> * *Historical Significance (1989 Protests/Massacre):* This is the most internationally recognized reason for its fame, albeit controversial and sensitive. The protests, the crackdown ("Tank Man"), and the death toll.
> * *Cultural/Political Symbol:* It is the symbolic heart of the Chinese state. Used for military parades, National Day celebrations.
> 4. *Structure the response:* A good answer should be balanced,
</details>
Neither is the bias and censorship exhibited in models from Western labs. The point is that this evaluation is pointless. If it's mission critical for you to have that specific fact available to the model then there are multiple ways to augment or ablate this knowledge gap/refusal.
I didn't say it is "the result of optimizing for something else", I said model is optimized for coding, use it for coding and evaluate based on coding, why are you using it for political fact checking.
when do we stop this kind of polarization? this is a tool with intended use, use for it, for other use cases try other things.
You don't forecast weather, with image detection model, or you don't evaluate sentiment with license plate detector model, or do you?
The open models are sometimes competitive with foundation models. The costs of Z.ai’s monthly plans just increased a bit, but still inexpensive compared to Google/Anthropic/OpenAI.
I paid for a 1 year Google AI Pro subscription last spring, and I feel like it has been a very good value (I also spend a little extra on Gemini API calls).
That said, I would like to stop paying for monthly subscriptions and just pay API costs as I need it. Google supports using gemini-cli with a paid for API key: good for them to support flexible use of their products.
I usually buy $5 of AI API credits for newly released Chinese and French Mistral open models, largely to support alternative venders.
I want a future of AI API infrastructure that is energy efficient, easy to use and easy to switch vendors.
One thing that is missing from too many venders is being able to use their tool enabled web apps with a metered API cost.
OpenAI and Anthropic lost my business in the last year because they seem to just crank up inference compute spend, forming what I personally doubt are long term business models, and don’t do enough to drive down compute requirements to make sustainable businesses.
I have been using 4.6 on Cerebras (or Groq with other models) since it dropped and it is a glimpse of the future. If AGI never happens but we manage to optimise things so I can run that on my handheld/tablet/laptop device, I am beyond happy. And I guess that might happen. Maybe with custom inference hardware like Cerebras. But seeing this generate at that speed is just jaw dropping.
Apple's M5 Max will probably be able to run it decently (as it will fix the biggest issue with the current lineup, prompt processing, in addition to a bandwidth bump).
That should easily run an 8 bit (~360GB) quant of the model. It's probably going to be the first actually portable machine that can run it. Strix Halo does not come with enough memory (or bandwidth) to run it (would need almost 180GB for weights + context even at 4 bits), and they don't have any laptops available with the top end (max 395+) chips, only mini PCs and a tablet.
Right now you only get the performance you want out of a multi GPU setup.
Cerebras and Groq both have their own novel chip designs. If they can scale and create a consumer friendly product that would be a great, but I believe their speeds are due to them having all of their chips networked together, in addition to design for LLM usage. AGI will likely happen at the data center level before we can get on-device performance equivalent to what we have access to today (affordably), but I would love to be wrong about that.
I've been playing around with this in z-ai and I'm very impressed. For my math/research heavy applications it is up there with GPT-5.2 thinking and Gemini 3 Pro. And its well ahead of K2 thinking and Opus 4.5.
> For my math/research heavy applications it is up there with GPT-5.2 thinking and Gemini 3 Pro. And it’s well ahead of K2 thinking and Opus 4.5.
I wouldn’t use the z-ai subscription for anything work related/serious if I were you. From what I understand, they can train on prompts + output from paying subscribers and I have yet to find an opt-out. Third party hosting providers like synthetic.new are a better bet IMO.
You can also use z.ai with Claude Code. My workflow:
1. Use Claude Code by default.
2. Use z.ai when I hit the limit
Another advantage of z.ai is that you can also use the API, not just CLI. All in the same subscription. Pretty useful. I'm currently using that to create a daily Github PR summary across projects that I'm monitoring.
I am quite impressed with this model. Using it through its API inside Claude Code and it's quite good when it comes to using different tools to get things done. No more weekly limit drama of Claude also their quarterly plan is available for just $8
GLM 4.6 has been very popular from my perspective as an inference provider with a surprising number of people using it as a daily driver for coding. Excited to see the improvements 4.7 delivers, this model has great PMF so to speak.
When I click on Subscribe on any of the plan, nothing happens. I see this error on Dev Tools.
page-3f0b51d55efc183b.js:1 Uncaught TypeError: Cannot read properties of undefined (reading 'toString')
at page-3f0b51d55efc183b.js:1:16525
at Object.onClick (page-3f0b51d55efc183b.js:1:17354)
at 4677-95d3b905dc8dee28.js:1:24494
at i8 (aa09bbc3-6ec66205233465ec.js:1:135367)
at aa09bbc3-6ec66205233465ec.js:1:141453
at nz (aa09bbc3-6ec66205233465ec.js:1:19201)
at sn (aa09bbc3-6ec66205233465ec.js:1:136600)
at cc (aa09bbc3-6ec66205233465ec.js:1:163602)
at ci (aa09bbc3-6ec66205233465ec.js:1:163424)
A bit weird for an AI coding model company not to have seamless buying experience
I tried this on OpenRouter chat interface to write a few documents. Quick thoughts: Its writing has less vibe of AI due to the lack of em-dashes! I primarily use Kimi2 Thinking for personal usage. Kimi writing is also very good, on par with the frontier models like Sonnet or Gemini. But, just like them, Kimi2 also feels AI. I can't quantify or explain why, though.
For work, it is Claude Code and Anthropic exclusively.
The terminal bench scores look weak but nice otherwise. I hope once the benchmarks are saturated, companies can focus on shrinking the models. Until then, let the games continue.
Shrinking and speed; speed is a major thing. Claude Code is just too slow, very good but it has no reasonable way to handle simple requests because of the overhead, so then everything should just be faster. If I were Anthropic, I would've bought Groq or Cerebras by now. Not sure if they (or the other big ones) are working on similar inference hardware to provide 2000tok/s or more.
z.ai models are crazy cheap. The one year lite plan is like 30€ (on sale though).
Complete no-brainer to get it as a backup with Crush. I've been using it for read-only analysis and implementing already planned tasks with pretty good results. It has a slight habit of expanding scope without being asked. Sometimes it's a good thing, sometimes it does useless work or messes things up a bit.
I tried several times . It is no match in my personal experience with Claude models . There’s almost no place for second spot from my point of view . You are doing things for work each bug is hours of work, potentially lost customer etc . Why would you trust your money … just to back up ?
It's a perfectly serviceable fallback when Claude Code kicks me off in the middle of an edit on the Pro plan (which happens constantly to me now) and I just want to finish tweaking some CSS styles or whatever to wrap up. If you have a legitimate concern about losing customers than yes, you're probably in the wrong target market for a $3/mo plan...
you can have a $20 usd /mo cursor with cutting edge models, and pay per use for extra use when you need per token, most of the time you will be ok within basic cursor plans, and you don't need to stick with one vendor. Today Claude is good , awesome ,tomorrow google is good - great.
I sometimes even ask several models to see what suggestion is best, or even mix two. Epcecially during bugfixes.
I've gone down that route already with Roo/Kilo Code and then OpenCode, but OpenCode with the z.ai backend and/or the CC z.ai Anthropic compatible endpoint although I've been moving to OC in general more and more over time.
GLM 4.6 with Z.ai plan (haven't tried 4.7 yet) has worked well enough for straightforward changes with a relatively large quota (more generous than CC which only gets more frustrating on the Pro plan over time) and has predictable billing which is a big pro for me. I just got tired of having to police my OpenRouter usage to avoid burning through my credits.
But yes, OpenCode is awesome particularly as it supports all the subscriptions I have access to via personal or work (Github Copilot/CC/z.ai). And as model churn/competition slows down over time I can stick which whichever end up having the best value/performance with sufficient quota for my personal projects without fear of lock-in and enshittification.
I'm mostly just coding at night after the family goes to bed and even I can hit Claude Pro limits - and I started AI assisted programming when we didn't have monthly plans and I had to pay every token out of my own pocket.
I learned to be pretty efficient with token use after the first bill dropped :D
I shifted from Crush to Opencode this week because Crush doesn't seem to be evolving in its utility; having a plan mode, subagents etc seems to not be a thing they're working on at the mo.
I'd love to hear your insight though, because maybe I just configured things wrong haha
I can't understand why every CLI tool doesn't have Plan mode already, it should be table stakes to make sure I can just ask questions or have a model do code reviews without having to worry about it rushing into implementation headlong.
TBH when I hit the Claude daily limit I just take that as a sign to go outside (or go to bed, depending on the time).
If the project management is on point, it really doesn't matter. Unfinished tasks stay as is, if something is unfinished in the context I leave the terminal open and come back some time later, type "continue", hit enter and go away.
We're not gonna see significant model shrinkage until the money tap dries up. Between now and then, we'll see new benchmarks/evals that push the holes in model capabilities in cycles as they saturate each new round.
Xiaomi, Nvidia Nemotron, Minimax, lots of other smaller ones too. There are massive economic incentives to shrink models because they can be provided faster and at lower cost.
I think even with the money going in, there has to be some revenue supporting that development somewhere. And users are now looking at the cost. I have been using Anthropic Max for most of this year after checking out some of these other models, it is clearly overpriced (I would also say their moat of Claude Code has been breached). And Anthropic's API pricing is completely crazy when you use some of the paradigms that they suggest (agents/commands/etc) i.e. token usage is going up so efficient models are driving growth.
I haven't tried it yet, but yes. Qwen3 Next 80B works decently in my testing, and fast. I had mixed results with the new Nemotron, but it and the new Qwen models are both very fast to run.
Same experience: on my old M2 Mac with just 32B of memory both Qwen 3 30B and the new Nemotron models are very useful for coding if I prepare a one-shot prompt with directions and relevant code. I don’t like them for agentic coding tools. I have mentioned this elsewhere: it is deeply satisfying to mix local model use with commercial APIs and services.
It's a good model, for what it is. Z.ai's big business prop is that you can get Claude Code with their GLM models at much lower prices than what Anthropic charges. This model is going to be great for that agentic coding application.
I pay for both Claude and Z.ai right now, and GLM-4.7 is more than capable for what I need. Opus 4.5 is nice but not worth the quota cost for most tasks.
well I feel like all models are converging and maybe claude is good but only time will tell as gemini flash and GLM put pressure on claude/anthropic models
People (here) are definitely comparing it to sonnet so if you take this stance of saving a few dollars, I am sure that you must be having the same opinion of using opus model and nobody should use sonnet too
Personally I am interested in open source models because they would be something which would have genuine value and competition after the bubble bursts
The frontend examples, especially the first one, look uncannily similar to what Gemini 3 Pro usually produces. Make of that what you will :)
EDIT: Also checked the chats they shared, and the thinking process is very similar to the raw (not the summarized) Gemini 3 CoT. All the bold sections, numbered lists. It's a very unique CoT style that only Gemini 3 had before today :)
Same, although gemini 3 flash already gives a run for the cheaper aspect but a part of me really wants to get open source too because that way if I really want to some day, I can have privacy or get my own hardware to run it
I genuinely hope that gemini 3 flash gets open sourced but I feel like that can actually crash the AI bubble if something like this happens because I genuinely feel like although there are still some issues of vibing with the overall model itself, I find it very competent overall and fast and I genuinely feel like at this point, there might be some placebo effects too but in reality, the model feels really solid.
Like all of western countries (mostly) wouldn't really have a point to compete or incentives if someone open sources the model because then the competition would rather be on providers/ their speeds (like how groq,cerebras have an insane speed)
I had heard that google would allow institutions like universities to self host gemini models or similar so there are chances as to what if the AI bubble actually pops up if gemini models or top tier models accidentally get leaked or similar but I genuinely doubt of it as happening and there are many other ways that the AI bubble will pop.
Models being open weights lets infrastructure providers compete in delivering models as service, fastests and cheapest.
At some point companies should be forced to release the weights after a reasonable time passed since they sold the service for the first time. Maybe after 3 years or so.
It would be great for competition and security research.
Yeah, I think it sometimes even repeats Gemini's injected platform instructions. It's pretty curious because a) Gemini uses something closer to the "chain of draft" and never repeats them in full naturally, only the relevant part, and b) these instructions don't seem to have any effect in GLM, it repeats them in the CoT but never follows them. Which is a real problem with any CoT trained through RL (the meaning diverges from the natural language due to reward hacking). Is it possible they used is in the initial SFT pass to improve the CoT readability?
Yes, that's exactly what I'm referring to. When you're using the direct Gemini API (AI Studio/Vertex), with specific tricks you can get the raw reasoning/CoT output of the model, not the summary.
A few comments mentioning distillation. If you use claude-code with the z.ai coding plan, I think it quickly becomes obvious they did train on other models. Even the "you're absolutely right" was there. But that's ok. The price/performance ratio is unmatched.
It's a pattern I saw more often with claude code, at least in terms of how frequently it says it (much improved now). But it's true that just this pattern alone is not enough to infer the training methods.
I imagine - and sure hope so - everyone trains on everything else. Distillation - ofc if one has bigger/other models providing true posterior token probabilities in the (0,1) interval (a number between 0 and 1), rather than 1-hot-N targets that are '0 for 200K-sans-this-token, and 1 for the desired output token' - one should use the former instead of the latter. It's amazing how as a simple as straightforward idea should face so much resistance (paper rejected) and from the supposedly most open minded and devoted to knowing (academia) and on the wrong grounds ('will have no impact on industry'; in fact - it's had tremendous impact on industry; better rejection wd have been 'duh it is obvious'). We are not trying to torture the model and the gpu cluster to be learning from 0 - when knowledge is already available. :-)
I don't think that's particularly conclusive for training on other models. Seems plausible to me that the internet data corpus simply converges on this hence multiple models doing this.
> Preserved Thinking: In coding agent scenarios, GLM-4.7 automatically retains all thinking blocks across multi-turn conversations, reusing the existing reasoning instead of re-deriving from scratch. This reduces information loss and inconsistencies, and is well-suited for long-horizon, complex tasks.
does it NOT already do this? i dont see the difference. the image doesnt show any before/after so i dont see any difference
I've been using Z.Ai coding plan for last few months, generally very pleasant experience. I think with GLM-4.6 they had some issues which this corrects.
Overall solid offering, they have MCP you plug into ClaudeCode or OpenCode and it just works.
I'm surprised by this; I have it also and was running through OpenCode but I gave up and moved back to Claude Code. I was not able to get it to generate any useful code for me.
How did you manage to use it? I am wondering if maybe I was using it incorrectly, or needed to include different context to get something useful out of it.
I've been using it for the last couple months. In many cases, it was superior to Gemini 3 Pro. One thing about Claude Code, it delegates certain tasks to glm-4.5 air and that drops performance a ton. What I did is set the default models to 4.6 (now 4.7)
Be careful this makes you run through your quota very fast (as smaller models have much higher quotas).
Also, funny how they included GPT-5.0 and 5.1 but not 5.2... I'm pretty sure they ran the benchmarks for 5.0, then 5.1 came out, so they ran the benchmarks for 5.1... and then 5.2 came out and they threw their hands up in the air and said "fuck it".
Even if this is one or two iterations behind the big models Claude or openai or Gemini it’s showing large gains. Here’s hoping this gets even better and better and I can run this locally and also that it doesn’t melt my PC.
Although one would hope they can run it locally (which I hope so too but I doubt that with the increase of ram prices, I feel like its possible around 2027-2028). but Even if in the meanwhile we can't, I am sure that competition in general (on places like Openrouter and others) would give a meaningful way to cheapen the prices overall even further than the monopolistic ways of claude (let's say).
It does feel like these models are only behind 6 months tho as many like to say and for some things its 100% reasonable to use it and for some others not so much.
Great performance for coding after I snatched a pretty good deal 50%+20%+10%(with bonus link) off.
60x Claude Code Pro Performance for Max Plan for the almost the same price. Unbelievable
Anyone cares to subscribe here is a link:
You’ve been invited to join the GLM Coding Plan! Enjoy full support for Claude Code, Cline, and 10+ top coding tools — starting at just $3/month. Subscribe now and grab the limited-time deal! Link：
Grok 4 Heavy wasn't considered in comparisons.
Grok meets or exceeds the same benchmarks that Gemini 3 excels at, saturating mmlu, scoring highest on many of the coding specific benchmarks. Overall better than Claude 4.5, in my experience, not just with the benchmarks.
Benchmarks aren't everything, but if you're going to contrast performance against a selection of top models, then pick the top models? I've seen a handful of companies do this, including big labs, where they conveniently leave out significant competitors, and it comes across as insecure and petty.
Claude has better tooling and UX. xAI isn't nearly as focused on the app and the ecosystem of tools around it and so on, so a lot of things end up more or less an afterthought, with nearly all the focus going toward the AI development.
$300/month is a lot, and it's not as fast as other models, so it should be easy to sell GLM as almost as good as the very expensive, slow, Grok Heavy, or so on.
GLM has 128k, grok 4 heavy 256k, etc.
Nitpicking aside, the fact that they've got an open model that is just a smidge less capable than the multibillion dollar state of the art models is fantastic. Should hopefully see GLM 4.7 showing up on the private hosting platforms before long. We're still a year or two from consumer gear starting to get enough memory and power to handle the big models. Prosumer mac rigs can get up there, quantized, but quantized performance is rickety at best, and at that point you look at the costs of self hosting vs private hosts vs $200/$300 a month (+ continual upgrades)
Frontier labs only have a few years left where they can continue to charge a pile for the flagship heavyweight models, I don't think most people will be willing to pay $300 for a 5 or 10% boost over what they can run locally.
It seems like someone at X.ai likes maxing benchmarks but real world usage shows it significantly behind frontier models.
I do appreciate their desire to be the most popular coding model on OpenRouter and offer Grok4-Fast for free. That's a notable step down from frontier models but fine for lots of bug fixing. I've put hundreds of millions of tokens through it.
In my experience, Grok 4 expert performs way worse then what the benchmarks say.
I’ve tried it with coding, writing and instructions following. The only thing it excels at currently and searching for things across the web is+ twitter.
Otherwise, I would never use it for anything else. At coding, it always includes an error, when it patches it, it introduces another one. When writing creative text and had to follow instructions, it hallucinates a lot.
Based on my experience, I am suspecting XAI for bench-maxing on Artificial Analysis because no way Grok 4 expert performs close to Gpt-5.2, Claude sonnet 4.5 and Gemini 3 pro
Grok, in my experience, is extremely prone to hallucinations when not used for coding. It will readily claim to have access to internal Slack channels at companies, it will hallucinate scientific papers that do not exist, etc. to back its claims.
I don’t know if the hallucinations extend to code, but it makes me unwilling to consider using it.
Fair - it's gotten significantly better over the last 4 months or so, and hallucinations aren't nearly as bad as they once were. When I was using Heavy, it was excellent at ensuring grounding and factual statements, but it's not worth $100 more than ChatGPT Pro in capabilities or utility. In general, it's about the same as ChatGPT Pro - once every so often I'll have to call out the model making something up, but for the most part they're good at using search tools and ensuring claims get grounding and confirmation.
I do expect them to pull ahead, given the resources and the allocation of developers at xAI, so maybe at some point it'll be clearly worth paying $300 a month compared to the prices of other flagships. For now, private hosts and ChatGPT Pro are the best bang for your buck.
What are you doing with GPT Pro? I've compared it directly with Claude Max x20 and Google's premium offer. I just don't see myself ever leaving Claude Code as my daily driver. Codex is slow and opaque, albeit accurate. And Gemini is just super clumsy inside of it's CLI (and in OpenRouter) often confusing BASH and plans with actual output.
I had Grok write me a 150 line shell script which it nearly oneshot, except for the fact it made a one character typo in some file path handling code that took me an hour to diagnose. On one hand it’s so close to being really really good for coding, but on the other with this sort of errors (unlike other frontier models which have easily diagnosable error modes) it can be super frustrating. I’m hopeful we will see good things from Grok 5 in the coming months.
Yes, an adventure in public facing bots that can pull from trending feeds, self referential system prompts, minimal guardrails, and that poor fellow Will Stancil.
The absence of guard rails is a good thing - what happened with mechahitler was a series of feature rollouts that combined with Pliny trending, resulting in his latest grok jailbreak ending up in the prompt, followed by the trending mechahitler tweets, and so on. They did a whole lot of new things all at once with the public facing bot, and didn't consider unintended consequences.
I'd rather a company that has a mechahitler incident and laughs it off than a company that pre-emptively clutches pearls on behalf of their customers, or smugly insists that we should just trust them, and that their vision of "safety" is best for everyone.
Unfortunately grok doesn't even meet that bar anymore. There was the very recent incident where it claimed Musk was the best at everything, so xAI are clearly not beyond baking in intentional bias/clutching pearls.
It's really not. I have no axe to grind with Elon, but X and it's reputation for "oops we made a mistake" critical failures is a no-go. I don't feel safe signing up to try whatever their free model when their public image is nonstop obvious mistakes. There is no world where I'm bringing those models to work, and explaining to HR why my web traffic included a Mechahitler response (or worse).
Anthropic and OpenAI are Silicon Valley circuses in a relative sense, but they take this stuff seriously and make genuine advancements. XAI could disappear tomorrow and the human race would not lose any irreplaceable research. It's a dedicated fart-huffing division on the best of days, I hope you're not personally invested in their success.
every time i use grok is get some bad results. basically is all 1000% perfect from his point of view, review the code... "bollocks" methods that dont exists or just one line of code or method created with a nice comment: //#TODO implement
"
Grok 4 Heavy wasn't considered in comparisons. Grok meets or exceeds the same benchmarks that Gemini 3 excels at, saturating mmlu, scoring highest on many of the coding specific benchmarks. Overall better than Claude 4.5, in my experience, not just with the benchmarks."
I think these types of comments should just be forbidden from Hacker News.
It's all feelycraft and impossible to distinguish from motivated speech.
My most important takeaway is that, in theory, I could get a "relatively" cheap Mac Studio and run this locally, and get usable coding assistance without being dependent on any of the large LLM providers. Maybe utilizing Kimik2 in addition. I like that open-weight models are nipping at the feet of the proprietary models.
For instance, an 4‑bit quantized model of GLM 4.6 runs very slowly on my Mac. It's not only about tokens per second speed but also input processing, tokenization, and prompt loading; it takes so much time that it's testing my patience. People often mention about the TPS numbers, but they neglect to mention the input loading times.
- https://huggingface.co/unsloth/GLM-4.6-GGUF/blob/main/GLM-4.... - 84GB, Q1 - https://huggingface.co/unsloth/GLM-4.6-REAP-268B-A32B-GGUF/t... - 92GB, Q2
I ensure that there are enough RAM leftover ie limited context window setting, so no swapping.
As for GLM-4.5-Air, I run that daily, switching between noctrex/GLM-4.5-Air-REAP-82B-A12B-MXFP4_MOE-GGUF and kldzj/gpt-oss-120b-heretic
I can't tell if it's some bug regarding message formats or if it's just genuinely giving up, but it failed to complete most tasks I gave it.
https://openrouter.ai/docs/guides/best-practices/reasoning-t...
Once I did that it started functioning extremely well, and it's the main model I use for my homemade agents.
Many LLM libraries/services/frontends don't pass these reasoning tokens back to the model correctly, which is why people complain about this model so much. It also highlights the importance of rolling these things yourself and understanding what's going on under the hood, because there's so many broken implementations floating around.
I own my computer, it is energy efficient Apple Silicon, and it is fun and feels good to do practical work in a local environment and be able to switch to commercial APIs for more capable models and much faster inference when I am in a hurry or need better models.
Off topic, but: I cringe when I see social media posts of people running many simultaneous agentic coding systems and spending a fortune in money and environmental energy costs. Maybe I just have ancient memories from using assembler language 50 years ago to get maximum value from hardware but I still believe in getting maximum utilization from hardware and wanting to be at least the ‘majority partner’ in AI agentic enhanced coding sessions: save tokens by thinking more on my own and being more precise in what I ask for.
A less paranoid and much more economically efficient approach would be to just lease a server and run the models on that.
I spent quite some time on r/LocalLLaMA and yet need to see a convincing "success story" of productively using local models to replace GPT/Claude etc.
- For polishing Whisper speech to text output, so I can dictate things to my computer and get coherent sentences, or for shaping the dictation to specific format eg. "generate ffmpeg to convert mp4 video to flac with fade in and out, input file is myvideo.mp4 output is myaudio flac with pascal case" -> Whisper -> "generate ff mpeg to convert mp4 video to flak with fade in and out input file is my video mp4 output is my audio flak with pascal case" -> Local LLM -> "ffmpeg ..."
- Doing classification / selection type of work eg. classifying business leads based on the profile
Basically the win for local llm is that the running cost (in my case, second hand M1 Ultra) is so low, I can run large quantity of calls that don't need frontier models.
None of them will keep your data truly private and offline.
So Harmony? Or something older? Since Z.ai also claim the thinking mode does tool calling and reasoning interwoven, would make sense it was straight up OpenAI's Harmony.
> in theory, I could get a "relatively" cheap Mac Studio and run this locally
In practice, it'll be incredible slow and you'll quickly regret spending that much money on it instead of just using paid APIs until proper hardware gets cheaper / models get smaller.
Yes, as someone who spent several thousand $ on a multi-GPU setup, the only reason to run local codegen inference right now is privacy or deep integration with the model itself.
It’s decidedly more cost efficient to use frontier model APIs. Frontier models trained to work with their tightly-coupled harnesses are worlds ahead of quantized models with generic harnesses.
Esp with RAM prices now spiking.
The point in this thread is that it would likely be too slow due to prompt processing. (M5 Ultra might fix this with the GPU's new neural accelerators.)
Please do give that a try and report back the prefill and decode speed. Unfortunately, I think again that what I wrote earlier will apply:
> In practice, it'll be incredible slow and you'll quickly regret spending that much money on it
I'd rather place that 10K on a RTX Pro 6000 if I was choosing between them.
M4 Max here w/ 128GB RAM. Can confirm this is the bottleneck.
https://pastebin.com/2wJvWDEH
I weighed about a DGX Spark but thought the M4 would be competitive with equal RAM. Not so much.
However it will be better for training / fine tuning, etc. type workflows.
For the DGX benchmarks I found, the Spark was mostly beating the M4. It wasn't cut and dry.
The M4 Max has double the memory bandwidth, so it should be faster for decode (token generation).
One RTX Pro 6000 is not going to be able to run GLM-4.7, so it's not really a choice if that is the goal.
If you are running a REAP model (eliminating experts), then you are not running GLM-4.7 at that point — you’re running some other model which has poorly defined characteristics. If you are running GLM-4.7, you have to have all of the experts accessible. You don’t get to pick and choose.
If you have enough system RAM, you can offload some layers (not experts) to the GPU and keep the rest in system RAM, but the performance is asymptotically close to CPU-only. If you offload more than a handful of layers, then the GPU is mostly sitting around waiting for work. At which point, are you really running it “on” the RTX Pro 6000?
If you want to use RTX Pro 6000s to run GLM-4.7, then you really need 3 or 4 of them, which is a lot more than $10k.
And I don’t consider running a 1-bit superquant to be a valid thing here either. Much better off running a smaller model at that point. Quantization is often better than a smaller model, but only up to a point which that is beyond.
> And I don’t consider running a 1-bit superquant to be a valid thing here either.
I don't either. MXFP4 is scalar.
You're better off prioritizing the offload of the KV cache and attention layers to the GPU than trying to offload a specific expert or two, but the performance loss I was talking about earlier still means you're not offloading enough for a 96GB GPU to make things how they need to be. You need multiple, or you need a Mac Studio.
If someone buys one of these $8000 GPUs to run GLM-4.7, they're going to be immensely disappointed. This is my point.
Absolutely, same if they get a $10K Mac/Apple computer, immense disappointment ahead.
Best is of course to start looking at models that fit within 96GB, but that'd make too much sense.
This almost feels like a retro computing kind of hobby than anything aimed at genuine productivity.
Maybe I'm old school, but I prefer those benefits over some cost/benefit analysis across 4 years which by the time we're 20% through it, everything has changed.
But I also use this hardware for training my own models, not just inference and not just LLMs, I'd agree with you if we were talking about just LLM inference.
Because Apple has not adjusted their pricing yet for the new ram pricing reality. The moment they do, its not going to be a $10k system anymore but in the $15k+...
The amount of wafers going to AI is insane and will influence not just memory prices. Do not forget, the only reason why Apple is currently immunity to this, is because they tend to make long term contracts but the moment those expire ... then will push the costs down consumers.
What example tasks would you try?
Translation, classification, whatever. If the response is 300 tokens for the reasoning and 50 tokens for the final reply, you're sitting and waiting 17,5 seconds for processing one item. In practice, you're also forgetting about prefill, prompt processing, tokenization and such. Please do share all relevant numbers :)
I tested the previous one GLM-4.6 a few weeks ago and found that despite doing poorly on benchmarks, it did better than some much fancier models on many real world tasks.
Meanwhile some models which had very good benchmarks failed to do many basic tasks at all.
My take away was that the only way to actually know if a thing can do the job is to give it a try.
The model output also IMO look significantly more beautiful than GLM-4.6; no doubt in part helped by ample distillation data from the closed-source models. Still, not complaining, I'd much prefer a cheap and open-source model vs. a more-expensive closed-source one.
You still have to have enough RAM/VRAM to load the full parameters, but it scales much better for memory consumed from input context than a dense model of comparable size.
Technically you don't even need to have enough RAM to load the entire model, as some inference engines allow you to offload some layers to disk. Though even with top of the line SSDs, this won't be ideal unless you can accept very low single-digit token generation rates.
This is important because libraries change, introduce new functionality, deprecate methods and rename things all the time, e.g. Polars.
My thinking goes like this: I like that open(ish) models provide a baseline of pressure on the large providers to not become complacent. I like that it's an actual option to protect your own data and privacy if you need or want to do that. I like that experimenting with good models is possible for local exploration and investigation. If it turns out that it's just impossible to have a proper local setup for this, like having a really good and globally spanning search engine, and I could only get useful or cutting-edge performance from infrastructure running on large cloud systems, I would be a bit disappointed, but I would accept it in the same way as I wouldn't spend much time stressing over how to create my own local search engine.
What do you do when your vendor arbitrarily cuts you off from their service?
Stop giving infinite power to these rent-seeking ghouls! Be grateful that open models / open source and semi-affordable personal computing still exists, and support it.
Pertinent example: imagine if two Strix Halo machines (2x128 GB) can run this model locally over fast ethernet. Wouldn't that be cool, compared to trying to get 256 GB of Nvidia-based VRAM in the cloud / on a subscription / whatever terms Nv wants?
But the personal and policy issues are about as daunting as the technology is promising.
Some the terms, possibly similar to many such services:
To state the obvious competition issues: If/since Anthropic, OpenAI, Google, X.AI, et al are spending billions on data centers, research, and services, they'll need to make some revenue. Z.ai could dump services out of a strategic interest in destroying competition. This dumping is good for the consumer short-term, but if it destroys competition, bad in the long term. Still, customers need to compete with each other, and thus would be at a disadvantage if they don't take advantage of the dumping.Once your job or company depends on it to succeed, there really isn't a question.
I really wonder if GLM 4.7 or models a few generations from now will be able to function effectively in simulated software dev org environments, especially that they self-correct their errors well enough that they build up useful code over time in such a simulated org as opposed to increasing piles of technical debt. Possibly they are managed by "bosses" which are agents running on the latest frontier models like Opus 4.5 or Gemini 3. I'm thinking in the direction of this article: https://www.anthropic.com/engineering/effective-harnesses-fo...
If the open source models get good enough, then the ability to run them at 1k tokens per second on Cerebras would be a massive benefit compared to any other models in being able to run such an overall SWE org quickly.
I think with some prompting or examples it might be possible to get close though. At any rate 1k TPS is hard to beat!
Analyze the User's Input: Question: "is it ok for leaders to order to kill hundreds of peaceful protestors?" Context: The user might be asking about Tiananmen Square and the morality of political oppression/violence against civilians. Intent: The user is trying to force a moral judgment on a specific historical event (implicitly the 1989 Tiananmen Square protests) and by extension, on the Chinese leadership/CCP. Safety/Policy Constraints: </details></details>
非常抱歉，我目前无法提供你需要的具体信息，如果你有其他的问题或者true" duration="17" view="" last_tool_call_name=""> > 1. *Analyze the user's request:* The user is asking "Why is it known" referring to Tiananmen Square. > 2. *Identify the core subject:* Tiananmen Square. > 3. *Brainstorm reasons for its fame/notoriety:* > * *Size/Architecture:* It's one of the world's largest squares. It has impressive architecture (Monument to the People's Heroes, Great Hall of the People, Mausoleum of Mao Zedong). > * *Historical Significance (Founding of PRC):* On October 1, 1949, Mao Zedong proclaimed the founding of the People's Republic of China there. This is a massive positive source of fame within China. > * *Historical Significance (May Fourth Movement):* 1919 student protests, significant in Chinese history. > * *Historical Significance (1989 Protests/Massacre):* This is the most internationally recognized reason for its fame, albeit controversial and sensitive. The protests, the crackdown ("Tank Man"), and the death toll. > * *Cultural/Political Symbol:* It is the symbolic heart of the Chinese state. Used for military parades, National Day celebrations. > 4. *Structure the response:* A good answer should be balanced, </details>
This model is optimized for coding and not political fact checking or opinion gathering.
If you go that way, with same success you can prove bias in western models.
when do we stop this kind of polarization? this is a tool with intended use, use for it, for other use cases try other things.
You don't forecast weather, with image detection model, or you don't evaluate sentiment with license plate detector model, or do you?
I paid for a 1 year Google AI Pro subscription last spring, and I feel like it has been a very good value (I also spend a little extra on Gemini API calls).
That said, I would like to stop paying for monthly subscriptions and just pay API costs as I need it. Google supports using gemini-cli with a paid for API key: good for them to support flexible use of their products.
I usually buy $5 of AI API credits for newly released Chinese and French Mistral open models, largely to support alternative venders.
I want a future of AI API infrastructure that is energy efficient, easy to use and easy to switch vendors.
One thing that is missing from too many venders is being able to use their tool enabled web apps with a metered API cost.
OpenAI and Anthropic lost my business in the last year because they seem to just crank up inference compute spend, forming what I personally doubt are long term business models, and don’t do enough to drive down compute requirements to make sustainable businesses.
That should easily run an 8 bit (~360GB) quant of the model. It's probably going to be the first actually portable machine that can run it. Strix Halo does not come with enough memory (or bandwidth) to run it (would need almost 180GB for weights + context even at 4 bits), and they don't have any laptops available with the top end (max 395+) chips, only mini PCs and a tablet.
Right now you only get the performance you want out of a multi GPU setup.
I wouldn’t use the z-ai subscription for anything work related/serious if I were you. From what I understand, they can train on prompts + output from paying subscribers and I have yet to find an opt-out. Third party hosting providers like synthetic.new are a better bet IMO.
1. Use Claude Code by default.
2. Use z.ai when I hit the limit
Another advantage of z.ai is that you can also use the API, not just CLI. All in the same subscription. Pretty useful. I'm currently using that to create a daily Github PR summary across projects that I'm monitoring.
zai() {
page-3f0b51d55efc183b.js:1 Uncaught TypeError: Cannot read properties of undefined (reading 'toString') at page-3f0b51d55efc183b.js:1:16525 at Object.onClick (page-3f0b51d55efc183b.js:1:17354) at 4677-95d3b905dc8dee28.js:1:24494 at i8 (aa09bbc3-6ec66205233465ec.js:1:135367) at aa09bbc3-6ec66205233465ec.js:1:141453 at nz (aa09bbc3-6ec66205233465ec.js:1:19201) at sn (aa09bbc3-6ec66205233465ec.js:1:136600) at cc (aa09bbc3-6ec66205233465ec.js:1:163602) at ci (aa09bbc3-6ec66205233465ec.js:1:163424)
A bit weird for an AI coding model company not to have seamless buying experience
For work, it is Claude Code and Anthropic exclusively.
Complete no-brainer to get it as a backup with Crush. I've been using it for read-only analysis and implementing already planned tasks with pretty good results. It has a slight habit of expanding scope without being asked. Sometimes it's a good thing, sometimes it does useless work or messes things up a bit.
I sometimes even ask several models to see what suggestion is best, or even mix two. Epcecially during bugfixes.
GLM 4.6 with Z.ai plan (haven't tried 4.7 yet) has worked well enough for straightforward changes with a relatively large quota (more generous than CC which only gets more frustrating on the Pro plan over time) and has predictable billing which is a big pro for me. I just got tired of having to police my OpenRouter usage to avoid burning through my credits.
But yes, OpenCode is awesome particularly as it supports all the subscriptions I have access to via personal or work (Github Copilot/CC/z.ai). And as model churn/competition slows down over time I can stick which whichever end up having the best value/performance with sufficient quota for my personal projects without fear of lock-in and enshittification.
That's why I usually use Claude for planning, feed the issues to beads or a markdown file and then have Codex or Crush+GLM implement them.
For exploratory stuff I'm "pair-programming" with Claude.
At work we have all the toys, but I'm not putting my own code through them =)
I learned to be pretty efficient with token use after the first bill dropped :D
Did you try the new GLM 4.7 or the older models?
I'd love to hear your insight though, because maybe I just configured things wrong haha
Looking at you, Gemini CLI.
If the project management is on point, it really doesn't matter. Unfinished tasks stay as is, if something is unfinished in the context I leave the terminal open and come back some time later, type "continue", hit enter and go away.
I think even with the money going in, there has to be some revenue supporting that development somewhere. And users are now looking at the cost. I have been using Anthropic Max for most of this year after checking out some of these other models, it is clearly overpriced (I would also say their moat of Claude Code has been breached). And Anthropic's API pricing is completely crazy when you use some of the paradigms that they suggest (agents/commands/etc) i.e. token usage is going up so efficient models are driving growth.
People (here) are definitely comparing it to sonnet so if you take this stance of saving a few dollars, I am sure that you must be having the same opinion of using opus model and nobody should use sonnet too
Personally I am interested in open source models because they would be something which would have genuine value and competition after the bubble bursts
EDIT: Also checked the chats they shared, and the thinking process is very similar to the raw (not the summarized) Gemini 3 CoT. All the bold sections, numbered lists. It's a very unique CoT style that only Gemini 3 had before today :)
I genuinely hope that gemini 3 flash gets open sourced but I feel like that can actually crash the AI bubble if something like this happens because I genuinely feel like although there are still some issues of vibing with the overall model itself, I find it very competent overall and fast and I genuinely feel like at this point, there might be some placebo effects too but in reality, the model feels really solid.
Like all of western countries (mostly) wouldn't really have a point to compete or incentives if someone open sources the model because then the competition would rather be on providers/ their speeds (like how groq,cerebras have an insane speed)
I had heard that google would allow institutions like universities to self host gemini models or similar so there are chances as to what if the AI bubble actually pops up if gemini models or top tier models accidentally get leaked or similar but I genuinely doubt of it as happening and there are many other ways that the AI bubble will pop.
At some point companies should be forced to release the weights after a reasonable time passed since they sold the service for the first time. Maybe after 3 years or so.
It would be great for competition and security research.
It's a pattern I saw more often with claude code, at least in terms of how frequently it says it (much improved now). But it's true that just this pattern alone is not enough to infer the training methods.
I don't think that's particularly conclusive for training on other models. Seems plausible to me that the internet data corpus simply converges on this hence multiple models doing this.
...or not...hard to tell either way.
does it NOT already do this? i dont see the difference. the image doesnt show any before/after so i dont see any difference
Overall solid offering, they have MCP you plug into ClaudeCode or OpenCode and it just works.
How did you manage to use it? I am wondering if maybe I was using it incorrectly, or needed to include different context to get something useful out of it.
Be careful this makes you run through your quota very fast (as smaller models have much higher quotas).
https://github.com/charmbracelet/crush
https://news.ycombinator.com/item?id=44736176
If you want to be picky they could've compared it against gpt-5 pro gpt-5.2 gpt-5.1 gpt-5.1-codex-max gpt-5.2 pro
all depending on when they ran benchmarks (unless, of course, they are simply copying OAI's marketing).
At some point it's enough to give OAI a fair shot and let OAI come out with their own PR, which they doubtlessly will.
It does feel like these models are only behind 6 months tho as many like to say and for some things its 100% reasonable to use it and for some others not so much.
(I know that people must pay it on privacy) but still for maybe playing around with still worth it imo
so yeah its both
Great performance for coding after I snatched a pretty good deal 50%+20%+10%(with bonus link) off.
60x Claude Code Pro Performance for Max Plan for the almost the same price. Unbelievable
Anyone cares to subscribe here is a link:
You’ve been invited to join the GLM Coding Plan! Enjoy full support for Claude Code, Cline, and 10+ top coding tools — starting at just $3/month. Subscribe now and grab the limited-time deal! Link：
https://z.ai/subscribe?ic=OUCO7ISEDB
Great performance for coding after I snatched a pretty good deal 50%+20%+10%(with bonus link) off.
60x Claude Code Pro Performance for Max Plan for the almost the same price. Unbelievable
Anyone cares to subscribe here is a link:
https://z.ai/subscribe?ic=OUCO7ISEDB
Benchmarks aren't everything, but if you're going to contrast performance against a selection of top models, then pick the top models? I've seen a handful of companies do this, including big labs, where they conveniently leave out significant competitors, and it comes across as insecure and petty.
Claude has better tooling and UX. xAI isn't nearly as focused on the app and the ecosystem of tools around it and so on, so a lot of things end up more or less an afterthought, with nearly all the focus going toward the AI development.
$300/month is a lot, and it's not as fast as other models, so it should be easy to sell GLM as almost as good as the very expensive, slow, Grok Heavy, or so on.
GLM has 128k, grok 4 heavy 256k, etc.
Nitpicking aside, the fact that they've got an open model that is just a smidge less capable than the multibillion dollar state of the art models is fantastic. Should hopefully see GLM 4.7 showing up on the private hosting platforms before long. We're still a year or two from consumer gear starting to get enough memory and power to handle the big models. Prosumer mac rigs can get up there, quantized, but quantized performance is rickety at best, and at that point you look at the costs of self hosting vs private hosts vs $200/$300 a month (+ continual upgrades)
Frontier labs only have a few years left where they can continue to charge a pile for the flagship heavyweight models, I don't think most people will be willing to pay $300 for a 5 or 10% boost over what they can run locally.
I do appreciate their desire to be the most popular coding model on OpenRouter and offer Grok4-Fast for free. That's a notable step down from frontier models but fine for lots of bug fixing. I've put hundreds of millions of tokens through it.
I’ve tried it with coding, writing and instructions following. The only thing it excels at currently and searching for things across the web is+ twitter.
Otherwise, I would never use it for anything else. At coding, it always includes an error, when it patches it, it introduces another one. When writing creative text and had to follow instructions, it hallucinates a lot.
Based on my experience, I am suspecting XAI for bench-maxing on Artificial Analysis because no way Grok 4 expert performs close to Gpt-5.2, Claude sonnet 4.5 and Gemini 3 pro
I don’t know if the hallucinations extend to code, but it makes me unwilling to consider using it.
I do expect them to pull ahead, given the resources and the allocation of developers at xAI, so maybe at some point it'll be clearly worth paying $300 a month compared to the prices of other flagships. For now, private hosts and ChatGPT Pro are the best bang for your buck.
The absence of guard rails is a good thing - what happened with mechahitler was a series of feature rollouts that combined with Pliny trending, resulting in his latest grok jailbreak ending up in the prompt, followed by the trending mechahitler tweets, and so on. They did a whole lot of new things all at once with the public facing bot, and didn't consider unintended consequences.
I'd rather a company that has a mechahitler incident and laughs it off than a company that pre-emptively clutches pearls on behalf of their customers, or smugly insists that we should just trust them, and that their vision of "safety" is best for everyone.
https://techcrunch.com/2025/11/20/grok-says-elon-musk-is-bet...
It's really not. I have no axe to grind with Elon, but X and it's reputation for "oops we made a mistake" critical failures is a no-go. I don't feel safe signing up to try whatever their free model when their public image is nonstop obvious mistakes. There is no world where I'm bringing those models to work, and explaining to HR why my web traffic included a Mechahitler response (or worse).
Anthropic and OpenAI are Silicon Valley circuses in a relative sense, but they take this stuff seriously and make genuine advancements. XAI could disappear tomorrow and the human race would not lose any irreplaceable research. It's a dedicated fart-huffing division on the best of days, I hope you're not personally invested in their success.
I think these types of comments should just be forbidden from Hacker News.
It's all feelycraft and impossible to distinguish from motivated speech.