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I am surprised. I've been using DS4 Flash (0731) for weeks now and it works perfectly fine as a replacement for Claude in a large variety of cases. It requires a few more iterations, sure, but it's useful enough to not need a Claude subscription anymore. Among the things I do I've been reverse engineering, writing complex C++ code...

Qwen 3.8 27B is around Opus 4.8 level of capability on the Agentic Intelligence Index (52 vs 57). In my testing the locally hosted Qwen is good enough that looking at a given piece of work output I couldn't tell you which model was behind it.

https://artificialanalysis.ai/models/qwen3-8-27b?models=gpt-...

I will give it a try, but from the benchmarks it never exceeds the DS4 flash benchmarks by significant margin and And I feel that the throughput that you will get on those machines or what I'm getting with my local hosted flash will be so much worse that it's not worth it.

Sparks don't have enough memory bandwidth, for the same 20k you're better off buying RTX or Apple M5 Ultra machines.

> get myself four sparks at a decent price

Wow, if you don't mind me asking. How and where?

> I would love to see an Opus-4.8-level local model but TBH I just haven't got there yet. The models I've tried so far _are_ good but they aren't able to solve tough technical challenges, regardless of harness/prompting/etc.

Agree. It doesn’t even have to be local, using models in this size class through OpenRouter will reveal their limits if you work side by side with Opus level models regularly.

There are a lot of social media posts about people cancelling their Anthropic or ChatGPT subscriptions after installing a local LLM. I’ve used local LLMs a lot and I spend a lot of time with frontier models and the difference is still huge. As far as I can tell, the social media posts about local LLMs replacing frontier models are either wishful thinking, engagement bait, or people who must be working on much simpler projects with a much higher tolerance for slop than I have.

Hopefully you also bought a switch

How much did you drop on these 4 sparks?

If you used the bare API pricing, 1M tokens @ 30% input/70% output/50% cached, you'd pay $0.05805. Even with four discounted sparks, how much are you paying for the same tokens/distribution?

And don't forget the coolest part, DeepSeek, Qwen, Z.ai and Moonshot have almost caught up while being open about their research and their model weights. We can mostly speculate about OAI and Anthropic models, nothing else, how fun huh?

There is a massive price war going on. All of these Chinese companies are publicly listed and exist outside the hype bubble required to ship Dario's dogshit paper onto the pauper's pension fund.

except besides benchmarks, most of these models don't meet reliability of Sol/Opus in coding work. Opus unfortunately talks very weirdly so not a great out of the box experience

Maybe others have found otherwise, but I find the benchmarks drastically different to real world "feel" of a model, even within the same harness. I'm not sure if this just reflects personal interaction styles, or if it is indicative of benchmaxxing or unrealistic automated benchmarking methodology.

Opus 5 consistently comes at or near the top, but outputs constant unreadable jibberish. Meanwhile GPT 5.6 Luna medium tends to be rated pretty poor on agentic tasks compared to the Chinese lab open models, but I find the latter much more likely to lose track of their own behaviour during a long-horizon task or get stuck in a doom loop.

(This isn't a comment on GLM-5.3 Flash as I've not used it!)

I don't know how anyone can actually use Luna max on ANY real workload. I've had Sol orchestrate a bunch of Luna agents, these agents were explicitly given small chunks of larger objectives and they still filled their entire context windows with just reasoning tokens, until compaction hit, and then reasoning again.

I've probably wasted a good 40% of my weekly usage on Luna Max agents just thinking and not writing a single line of code.

It's also better than Sol (at whatever effort) at designing pretty UIs. I have a Codex sub and I've been using this model for UI stuff.

Only 73K output tokens too. Anthropic should really be embarrassed with their Sonnet 5 price/performance.

Opus 5 is better than Fable in this benchmark?

> They should've just lead with real, up to date data, because it's good, not the silly old tactics like comparing to Opus 4.8 when 5.0 is out in many of their charts

It's what people know. Opus is just the common target.

> Smarter and cheaper than Luna xhigh, not as smart but less expensive than Luna max. Smashes deepseek v4 flash

The problem with this and DeepSWE is it goes for a very specific profile. I'm not convinced DeepSWE is any accurate in actual work. It's surely a different signal (compared to some that allow cheating) but it has its own issues, e.g. weak harness.

Luna is great at following instructions but bad instructions or anything not covered = death.

Deepseek is more analytical. Good for bug tracking.

GLM is a better all rounder in some ways. Better at creativity.

Isn't this practically every TOS though?

Nearly every TOS I've ever read has a "We can ban you for any reason, or no reason, are under no obligation to disclose any reason." line somewhere in it.

HN's for example

> We reserve the right, at our sole discretion, to change or modify portions of these Terms of Use at any time.

> You acknowledge that Y Combinator may establish general practices and limits concerning use of the Site,

> You further acknowledge that Y Combinator reserves the right to change these general practices and limits at any time, in its sole discretion, with or without notice.

> Y Combinator reserves the right to investigate and take appropriate legal action against anyone who, in Y Combinator’s sole discretion, violates this provision, including without limitation, removing the offending content from the Site, suspending or terminating the account of such violators and reporting you to the law enforcement authorities.

> Can ban you if you, in the "sole and absolute opinion" of Z.ai, have violated these broad terms.

OpenAI revoked my Cyber verification, along with many others, asked to reverify (i.e. give my biometric information to Persona), had me do it 8 times, just to find out several days later that they silently implemented a nationality whitelist, and my nationality didn't make it (and no, it's not a sanctioned country).

Their support says they can't look into anything or do anything, and their public spokespersons on X deny everything.

I get tons of cyber refusals now (lots of reverse engineering), so it's only matter of time when my account is going to get banned.

At least Z.AI is being honest here. And no provider other than OAI/ANT had me submit my biometric information just to use Ghidra.

I get all that.

Then alternatives are:

- Grok - where I absolutely have 0 trust in X.ai's interst in "pushing humanity forward".

- OpenAI and Anthropic - which seem to try to be building the biggest moat they can by pushing to ban open models. And at the same time want to be an Arbiter of what level of intelligence I can use.

- Google and Meta - I don't need to talk about the practices of these companies.

Yes, the terms of service aren't great. But the alternatives aren't great either. I don't believe that a future which OpenAI and Anthropic are pushing for has my best interest in mind.

The model is MIT-licensed, so run it on any of the non-Chinese inference providers that will host it in a few days.

It's China. It's a given that they use your data for training. At least they're nice enough to be honest about it.

The model weights are MIT licensed.

Yes, the terms are dubious. But they are also reasonably lenient with enforcement. They also don't require persona id verification, witch is wat turned me away from openai.

I blocked Z.ai as soon as they were loading 10 different external providers including Alibaba who was just proven to execute silent sound fingerprinting mechanisms.

Give it a couple days, and there will be plenty of other inference companies hosting it. Don't like z.ai's TOS? Use the model on a provider with TOS that you agree with.

Are their TOS significantly more vague or restrictive than OpenAI or Anthropic’s?

In any case what matters is what is enforced in practice. It will be a mild inconvenience to switch providers on Openrouter.

If Anthropic or OpenAI decide to apply those same arbitrary terms, you are SOL.

As long as the weights are open, who cares? You can rely on someone else for inference.

How is that vastly different from any other non-enterprise facing provider? I do believe Anthropic bans accounts without even a human in the loop with no recourse left to those banned.

None of that applies if you run it at home. Also 3rd party providers will start serving this pretty soon under different terms.

You can download the weights, and run in your own hardware and avoid all that.

you can abliterate any open model like this. This is pretty standard stuff in a TOS. I'd be surprised if you couldn't find the same in OAI or Anthropic's

>Broad and perpetual license over inputs and outputs, and even your name and profile picture.

>Vague prohibitions on whatever may harm Z.ai’s "interests" or even the "national interests" of any country.

[...] may cause harm to Anthropic, our users, or third parties, we reserve the right to remove or take down some or all of such Third-Party Content using, where appropriate, algorithmic and human review.

You may not export or provide access to the Services into any U.S. embargoed countries or to anyone on (i) the U.S. Treasury Department’s list of Specially Designated Nationals, (ii) any other restricted party lists identified by the Office of Foreign Asset Control, (iii) the U.S. Department of Commerce Denied Persons List or Entity List, or (iv) any other restricted party lists

>Vague prohibitions on "disturbing" or "inappropriate" content, whatever that is.

we will use Materials for model training when [...] your Materials are flagged for safety review to improve our ability to detect harmful content, enforce our policies, or advance our safety research.

>Vague prohibitions on discussing Z.ai, even my posting this comment violates it.

>Can ban you if you, in the "sole and absolute opinion" of Z.ai, have violated these broad terms, and if you paid for the discounted yearly plan kiss your money goodbye.

To engage in any other conduct that restricts or inhibits any person from using or enjoying our Services, or that we reasonably consider exposes us—or any of our users, affiliates, or any other third party—to any liability, damages, or detriment of any type, including reputational harms.

Mind you, that's Anthropic's Terms of Use in Europe. I have zero doubts the TOS applied to the US is even worse and that merely mentioning your first born in a chat entitles them to a part of its soul.

Chinese laws are not valid in the EU

They all do that, some are just more honest to tell you upfront than others.

TOS is and will ever be just a "pretty please".

Who the hell cares when I can run it myself?

> Vague prohibitions on "disturbing" or "inappropriate" content, whatever that is.

I have prompted out a lot of disturbing and inappropriate content with GLM-5.2, that would have left other American models blanched in the face or clutch their pearls. I think this is mostly a reference to Anti-CCP stuff.

In fact, I don't think I've ever even had a prompt refused.

For openrouter in pi:

{ "providers": { "openrouter": { "models": [ { "id": "z-ai/glm-5.3-flash", "name": "Z.ai: GLM 5.3 Flash", "reasoning": true, "thinkingLevelMap": { "off": null, "minimal": null, "low": "low", "medium": null, "high": "high", "xhigh": null, "max": "max" }, "input": ["text", "image"], "cost": { "input": 0.075, "output": 0.25, "cacheRead": 0.015, "cacheWrite": 0 }, "contextWindow": 1048576, "maxTokens": 131072 } ] } } }

Weren't they giving free access? Not exacty a meaningful heuristic if so

This is the takeaway here: That's how they have been serving it at scale as Ox-Alpha. This is a definitional moment.-

Further quote:

"Compared with our initial baseline on the same hardware, we achieved a 3× improvement in end-to-end serving performance, reaching hardware efficiency and per-token cost comparable to mainstream NVIDIA GPUs. This demonstrates that Chinese chips can support frontier-model inference efficiently and economically at scale."

https://z.ai/blog/glm-5.3-flash

Another self-inflicted own courtesy of US government policy.

While I think China would always get to hardware self-sufficiency eventually, all export controls have done is (1) accelerate China's development, and (2) divert revenue that would've otherwise gone to NVIDIA/AMD/etc instead.

I don't see a situation where subscription payers move outside American LLMs (chatgpt, claude, gemini)

And I don't see a situation where serious API payers are OK with handing the Chinese state all their data. Like manufactures of decades past did and learned a hard, even existential, lesson for it. The state mantra has been "Collect and Copy" for a long time now, tech just hasn't had that moment to experience it yet.

So that leaves local hosting/leasing, but one of those has totally non-practical economics and the other doesn't have enough compute to meet any kind of real demand.

I also have yet to meet a single person who isn't neck-deep in the tech space mention a Chinese LLM. It's 100% the big American three.

If anything it's custom chips from the labs that threatens Nvidia.

Ox Alpha is a smaller model and it was running very slowly. Chinese AI accelerators are coming along, but nVidia’s lead is huge.

Most US companies that have anything to do with government, finance, medical, etc. already have contractual or regulatory obligations which prevent them from using Chinese hardware or services, even before the AI boom. That's a huge market.

Nvidia will do just fine. (Disclaimer: not a shareholder. At least, not directly.)

God I wish I could’ve shorted NVIDIA right now

yay, I called it! :) (in the other thread)

Not really a brag: it ran like shit. Very slow (~20tps, VERY high latency) and it would timeout all the time.

I'm sure the chips are fine, but they clearly didn't have enough capacity for the demand they had (that 100T/day claim was asbolute bs)

This is no surprise [0] [1].

>> "They are already there on open weight models and Jensen knows that it is only a matter of time until China catches up with GPUs or other AI accelerators."

It is also why Nvidia becoming a bank for other AI companies who are unable to find VCs to fund them isn't really a good thing and that is bearish.

[0] https://news.ycombinator.com/item?id=49397204

[1] https://news.ycombinator.com/item?id=49431231

Not really. Chinese AI companies were never using NVidia AI chips.

This announcement doesn't really mean anything at all. It means the very few people who are already using Z.ai's API will continue to do so, but the vast majority of money going to Nvidia is through the massive amount of business going to Anthropic, OpenAI, and other western cloud providers and inference providers, who are mostly using NVidia chips for inference.

Also, NVidia chips are still sold out and supply constrained.

You aren't going to get nearly as much token usage locally from DGX Sparks or even M5 Ultra (though it might be close, unsure would need to get my mittens on it to clarify).

You will get around 2-4 concurrent streams of aggregate tokens at best for such a model and around 0.5B output tokens per month assuming you use loops and run it when you are sleeping. That's 500 (per mill) * 0.5$ = 250$ only at most.

Then there is maintanence and efficiency costs due to electricity usage and such, any down time, etc.

You will be lucky if you can squeeze more than 200$ of value out of it in a month.

I don't think people should buy local hardware for money reasons, by the time you will pay off a 10K USD machine, 2-3K USD machine will catch up and beat it by a significant margin.

Unless your expectation is that we will be in hardware winter for the next 10+ years. At 200$ per month it will take around 200 * 50 = 10k, that is, 50 months, so around 4-5 years.

Again assuming you are making the most of your hardware somehow, very hard to do in practice.

I don't recommend people to use compute as investment or payoff thing, but if you have the money to burn and can afford it why not, maybe with some software optimizations it will be cheaper but then again Z.ai is currently offering 50% discount and providers will offer cheaper rates for sure.

But either way you will never be able to burn more than 200$ worth of token on a cheap hardware device, because inference becomes more profitable the more you scale it up, you have separate prefill and decode engines/systems, and a lot of nuance, but assume for every 10x increase in infra you increase margins by 5-10%.

So from 10K to 100K to 1M to 10M to 100M.. I don't think this curve continues beyond 100M but I have no idea about that scale unless some AI lab is interested in hiring me lol.

So a 100M infra will have ~30% better margins than you at 10K, then there is software optimizations but that's cheap enough, though some of it is only viable at scale.

Either way assume 10K is the price of privacy if you really want to buy it. Don't worry about making the most out of the usage, you will always be in a net loss but I would assume for you 10K doesn't matter.

The lack of measurement causes existence of such claims or discussion. Last Friday I built this Model fingerprint calculator and I tested between OxAlpha with all other claimed models, the only match was GLM. It generates, or measures regardless of the model-weights or its training data. No need guessing when one can measure it. I tested with Gemini family too, far different. Here is the link to my experiment https://github.com/unclecode/modelprint

On Twitter they mentioned that it was unfortunate timing as the 3.7 flash release collided with ox alpha.

Trolling. GLM is heavily distilled from Gemini.

That's 160GB-ish for Q4...how is 256 insufficient?

Looks like the M5 Ultra Studio wait times are going to increase again. Already at 10-12 weeks, I wonder how long it'll go?

Speaking as someone who isn't really well versed in this, does 18B active parameters mean that you could potentially hold only the 18B parameters in RAM and stream the rest from a fast NVMe SSD for acceptable performance similar to how Colibri works?

https://github.com/JustVugg/colibri

We need to figure out what the real pricing is for a going concern. Right now, everyone is subsidizing and discounting to grow (or maintain) market share. The big question is whether the steady state, market derived inference pricing is above or below what we’re seeing today. I honestly don’t know. Anthropic had said that inference is profitable, but they’re clearly not yet profitable overall with training and buildouts still happening.

> I don't see how NVIDIA can keep their spot as belle of the ball.

FWIW, people were saying "ASICs will kill CUDA demand!" since the crypto mining boom. Then a few months later, CUDA found another niche application in LLM applications.

With the mounting demand for robotics, surveillance and autonomous weapons, I don't see how Nvidia couldn't keep their spot. They have their pick of the litter with hundreds of market segments, and unlike the rest of FAANG they're not afraid to branch out.

One term of art that's emerged for this "feel" is "big model smell", first coined by @aidan_mclau. [0]

To my surprise I couldn't find any proper explainers of the term in a quick search, despite grokking it after seeing it in various contexts on Twitter, but Fable 5 offered a useful analogy: "A student who memorized worked solutions and one who understands the subject score the same on the test; you can only tell them apart by asking a question the test didn't. Real-world use is nothing but those questions, which is why a single AA number feels right and wrong at the same time."

In other words, big model smell is related to the underlying ability to "understand" when tasks are underspecified or out-of-distribution. This ability can be mimicked to parity by smaller, distilled models according to the density of the training data for particular tasks, but neural scaling laws still hold for generalized reasoning ability.

More recently with these smaller models, there's a separate but related "RL-fried" phenomenon, where they rely on CoT to "grind toward a checkable answer even in contexts (open dialogue, taste, ambiguity) where there is no checkable answer, and you get the tell: over-hedged, over-structured, relentlessly on-task, deaf to the subtext."

There are some other insights and caveats in the (short) conversation that I feel you may appreciate reading. [1]

[0] https://x.com/aidan_mclau/status/1807843014104211855 [1] https://claude.ai/share/d511a348-7c36-432f-a6d5-9deab2802615

I agree. They are definitely good - no issues with instruction following for example - but they miss the "intelligence" larger models have.

For implementation tasks, where I have the problem already defined and researched, or just simple task, I'd definitely use something like Luna xhigh or max. If the task is vague, or involves planning, I'd rather use Sol medium, even though it's theoretically worse on benchmarks.

The recent and slightly smaller DSv4 Flash is also GLM 5.2 equivalent (or close enough)

> From a biased source, but would be big if true. I've had great results with GLM 5.2.

It's at least close (even if not better) from the Ox Alpha runs. For the price it's definitely great.

They even give this over the API now:

│ https://openrouter.ai/api/v1/chat/completions model: stealth/ox-alpha auth: OPENROUTER_API_KEY status: 404 Not Found response: {"error":{"message":"Thank you for participating in the Stealth Ox Alpha testing period. This model was ZAI's GLM-5.3 Flash.

│ Use it now: https://openrouter.ai/z-ai/glm-5.3-flash","code":404},"user_...":"}

Yeah, made me suspicious of how well the Ox Alpha was performing that it wasn't some 'new group' making the model.

> "yeah, this is the model/harness that I now run on my machine and don't mess with it"

* me raises hand.-

Their list of allowed tools is extensive so just use whatever you want within that list

Think z code gives a token bonus though

I think my inexperience using Claude Code or Codex makes a difference but what would you expect to be different here as opposed to using pi or opencode? Pi is my main driver so switching between all these models is a no brainer. No matter what the model is, my harness stays the same: same workflow, same skills, etc.

I use my own harness: https://github.com/computerex/z

Have been using it as my primary harness for personal work for I'd say 6 months. I recommend everyone create their own harness at least to learn. There are a lot of practical benefits.

Both can be true though. I had the max coding plan since january and I kept using with Pi since then, even though it wasn’t as good as opus until glm 5.3. It definitely can be a daily driver if you don’t want to use Anthropic or OpenAI. It’s going to be even better with native vision now available. And i’m not messing with my setup either.

You can use OpenRouter directly in Claude Code as well, it's quite nice!

I think anthropic is behind but Luna on a Jalapeno seems profitable

They're obviously in a pickle, nobody is going to continue to pay $15-50 a mm tokens here soon. There's a reason OpenAI stopped training large models last week, and it's not because of "saftey" or "alignment" they know these gigantic models are not worth the squeeze.

This is a bad model. Worse than Luna in every way; slower, dumber.

they also conspicuously omitted GPT 5.6 Luna from comparison. It scores lower, but is also cheaper. MiMo 2.5 is not a valid comp at this point

edit: nevermind. it is there in the artifical analysis scatter plot, but is greyed-out.

MUCH more interesting is that in that chart, their cost is WAY off. The actual chart shows GLM 5.3 Flash at $0.09, but their chart shows $0.045...

Probably apples to apples would be to compare z.ai subscription plans vs API pricing

Is that cheaper than DS4 flash?

This is exactly what Jenson said in all of his interviews. Banning it in the short term would have long term consequences.

It's feeling good on non-pelican workloads too. Less verbose than 5.3, cheaper/higher usage limits, vision capability and some good web design one-shots even with vision disabled.

With GLM 5.1 and 5.2, the big problem was tool calling and long-horizon coherency. 5.3 was more trustworthy at the cost of longer thinking traces, and now Flash seems to improve on it once again with a more concise, smaller model. As long as there aren't any noticeable regressions, I could see myself defaulting to this for >90% of my day-to-day coding work.

I only see a mostly blank page with a "Paste" button, a "URL" button, and and a "Preview" label.

It's not possible to keep shrinking down parameters and keep "frontier" performance, it's like saying it's possible to take a 3 hour movie and compress it down to 3 megabytes, there are information theoretic limits on the amount of bits of information that can be compressed.

What I'm saying is, if you're expecting a model that can be run on a 16GB or 32GB machine with the intelligence/knowledge of Mythos or Sol, it will never happen. It cannot happen, just like you cannot watch the Odyssey saved as a 16MB file.

Smaller models can get faster and smarter, but by definition they can never compress all of the knowledge of a frontier model and they will approach a limit by which they cannot get better.

Can't come soon enough!

If it gets more efficient it'll be more enticing to expand use case. Personally I'm hoping to do a lot at home but I'm not counting the datacenter building as a bad move at this moment. It may and up that way.

It seems like it has come to mean "fast, small, cheap" models these days, and seems well enough understood as such that different AI labs are adopting it.

Starting? This was obvious way back in 2019, when the US decided to give China a little push developing their own silicon industry.

> It's too big, bright and resourceful of a country to choose confrontation instead of collaboration.

It's not like we didn't try it. China first have to learn to make deals where both party benefits.

This has been clearly stated as what would happen going back several decades at least.

> I'm starting to think

That's good. Keep going.

Well the big problem with china is that they do not respect international law when it comes to technology theft. But that argument is very weak when it appears that a lot of what they do is out in the open for anyone to replicate.

I really wish GLM models had vision capabilities. I've worked around that in the past to use a vision MCP in my harness that GLM can call. It is not the same, but it allows the model to query images.

I've just used it for a fairly complex refactoring of the UI in a SwiftUI / AppKit app. It managed the refactoring in blazing colors, and the resulting UI looked really good. It was also quite fast. I'm impressed.

50%

Chinese universities are really a huge advantage, even in the US many of the top staff in model development are Chinese. Another big thing is the hardware costs required to train models. Between those two factors it really looks like this will remain a US-China competition for the foreseeable future, although there are some other players like Mistral from France.

Well, it will be interesting to see where Qwen3.8-Flash-Next ends up landing, also released today. These are exciting times!

I find GLM's idea of fast/flash is not really competitive with the speed DS4 Flash has, and it's hard to see them as being in the same segment for that reason.

Even vision? Thought k3 might have an edge there

Captive audience.

Yes; if you try to use Ox Alpha it will give an error saying it waa trial period, and that it is GLM 5.3 flash.

Its displayed in the html...

Right now there's at least two: https://openrouter.ai/z-ai/glm-5.3-flash

Give it a day or two. More will pop up.

I don't see anyone other than ZAI yet but GLM 5.2 is available on many providers worldwide so I'd expect we'll see the same on this one soon.

It was certainly almost RL-fried to overfit the benchmarks, at the expense of actual usability. See Opus 5.

is it a benchmarkmaxxing model?!