They've also announced Qwen3.8-27B being released open-weight next week. Qwen3.6-27B is widely regarded as one of the best local models, especially since nothing else comes close to it, that isn't benchmaxxed, without being significantly larger. If 3.8 truly improves upon it that would be awesome.
Qwen3.6-35B is my daily driver for AI, and what convinced me to cancel my Claude subscription back in April. The Qwen3.6 line is easily the best local model I've tried, and I've tried a lot. I've got it diligently grinding away on my laptop right now, reviewing and fixing some bugs in my F# code.
35B MoE is certainly a good and fast local model. I find 27B dense to be quite a bit smarter, so I daily drive that. I wish there was a ~100B MoE with maybe 10B active. It would be super smart and fast!
I tried it for a bit, and It was not really worth its size. It got swept up in all the other AI news recently, but laguna s 2.1 I think is the best ~100B moe model right now
I didn't mention it above, but Laguna S is my other favorite model. I use Qwen a lot more, it's smaller and faster, but I like to switch to Laguna when I feel like I need a "heavy hitter" for certain huge or complex tasks.
What on earth hardwares do you guys have to be able to run 100gb models locally?! That's crazy! I'm here struggling to even get 27b models to run in somewhat usable way
Haha I'm on an Mac Studio with an M1 Ultra, 64gb ram. I bought it when it first came out, it just happens to be good for local LLMs. I have to use a smaller quant of Laguna S though (I think 4-bit? Not at my machine to check), as 8-bit and full size definitely don't fit in the 64gb I have.
Yeah, a good rule of thumb is that the weights take up ~100% of the size of the model, so 100B bytes (8-bit quant) would be, well, 100GB and a 4-bit quant would be half that.
Honestly the 27b dense one punches way above its weight in a lot of domains, especially coding in my testing, so I think you will probably be disappointed.
I would recommend looking into Ornith1.0 - it's using Qwen3.6 35B-A3B and excels in coding, at least for my coding needs, Python, web-dev, SQL scripting and some C#. Using Pi harness.
On an 8GB GPU and 32GB laptop: ~5 words/s while running in Qubes (I don't have an install at the moment that'll tell me tokens/s).
Tolerable and usable for some things, though thinking makes it take about a minute to reply in many cases. But getting this kind of thing to run on 8GB of VRAM is the main benefit of the mix-of-experts setup: it can do partial GPU loading and get a ton better throughput than a similarly-sized dense model.
I use it with a strix halo server. 35B runs stupidly fast. 27B is about 700 TPS prefill and 30 TPS token generation. Which interestedly is about what Kimi K3 gives me depending on provider.
On my SpacemiT K3 SBC with 32GB RAM (where models run on the eight A100 RISC-V cores with 1024 bit vectors) doing the same task I got 5, 5.8, 6.5 tok/s using gemma-4-26B-A4B-it-QAT-Q4_0.gguf, Qwen3.6-35B-A3B-Q4_K_M.gguf, Qwen3.5-35B-A3B-Q4_K_M.gguf. The corresponding dense models are more in the 2.5-3 tok/s range.
Kind of slow, but using only 14W of electricity so the Wh per task is twice as good as using my i9-13900 laptop with 4060 GPU.
It's pretty fast, faster than I could type anyway, but not as fast as Claude of course. My oMLX dashboard says I get about 45 tokens per second from the Qwen model I'm running (I host it on my M1 Mac Studio, not on my laptop).
It's just a MacBook Air with an M4, cheap and nothing special. I host Qwen on my Mac Studio, an M1 with 64gb ram. The model uses around 20-25gb ram depending on what it's doing.
Having invested in a machine with 128GB of RAM, I would love seeing something a bit larger than 27B / 35B, possibly a 54B dense model or 70B MoE would be much closer to the Qwen 3.8 Max experience.
This is what I've been waiting for. We are still using fine-tuned deployments of Qwen3.6-27B with a lot of success but could use a bump in intelligence. Here's hoping.
If they trained it well, and can do computer use, it will be a new era. Companies can keep PCs, put Qwen 3.8 27b on it and get rid of the employees, lol...
It was a matter of time for China to catch up with the US. In terms of infrastructure, manufacturing, and engineering workforce, China has the upperhand and I foresee them becoming the SOTA leaders. Maybe if the US wasn't so busy gatekeeping and keeping things proprietary, they would've had more trust from the open source community.
I think the window for a ban of open weight models is closing fast so let's hope US administration is going to miss it and we get Fable-level models (at least in some aspects) with open weights without infringing any newly introduced law as a long-term local baseline.
Even if the US does ban it, Europe due to its lack of European competitors, has no current similar protectionist incentives. Even if there is regulatory capture in the US, the rest of the world isn't going to follow suit until the current market leaders outright are replaced with other competitors that Europe would want to favor. The genie is out of the bottle in the West already.
You can send armed men to your house, or restrict companies in America from using these models.
They can also restrict US providers from serving these models. Looking at OpenRouter, there is currently 11 providers for 'DeepSeek V4 Flash 0731' with the only non-US one being Deepseek themselves, which aren't even the cheapest.
> Today, we are officially releasing Qwen 3.8-Max, the most capable model in the Qwen family to date. This also marks the first time we will open-source the weights of a Qwen-Max-class model — the open weights will be released next week.
> Qwen3.8 is launching and going open-weight soon! [...] You don't have to wait to test it. Just now, the Qwen3.8-Max-Preview made its debut on Alibaba’s Token Plan, Qoder, and QoderWork.
July 19th post mentions “Max-Preview” vs. today’s post dropping the “Preview”. Unclear what changed if anything though.. Maybe broader availability or it’s a slightly improved checkpoint
My understanding is that these "preview" models are usually earlier RL checkpoints, and that "official release" happens when they're happy with the training run?
I believe they mentioned around the preview announcement that they'd be releasing improvements to capability, which I assume means continued training.
K3 also forgot the wheels when I tried it, but it turned out to be a bug in llama-server's built-in SVG viewer. I know you have your own rasterizer but just FYI, sometimes it isn't the model's fault.
Agree, I don't necessarily see a strong argument favoring OpenAI or Anthropic here. In the interest of perspective, can anyone (perhaps playing devil's advocate) give one?
The open models are now good enough for what I want to do with them, let alone any future improvements. And factoring in efficiency gains, a model in the ~70b range starting to satisfy my needs would completely obviate the need to pay others for inference. This does not seem far-fetched to me, comparing with where open models were at this time last year. What am I missing?
Hardware and electrical costs including power usage and electric wiring/outlet costs of such machine.
Unless you are spending more than a max subscription (200 a month+) its cheaper to use the cloud.
But things are priced cheaper in the cloud now to lock you in and restrictions around hosted models are getting worse.
If you only have a $300 dollar laptop its probably not worth the upgrade.
I'm personally excited by local AI but the experience for the average isn't the same. I'm willing to get .5/s running on 10-15 years old machines but what I can do with it is limited.
you can't create a new drug by running a model on a laptop. You can't serve a customer support bot running on a laptop. You can't generate video in bulk for many users on a laptop. So there is still a case for paying others for inference.
Does it justify the valuations? No idea, but some major use cases are still there. That's why they are rushing to implement, OpenAI creating a "deployment company", Anthropic having some pharma rumors, etc.
Can they still go public ? MiniMax M3 Pro is also coming, then DeepSeek-v4-Pro GA, then GLM5.5. There will only be bad news for them in the coming few weeks/months.
It's not so simple, if such a headline can get them closer to the regulatory capture they want to lock in American businesses and forbid them from using Chinese AI.
US AI labs really rub me the wrong way, especially with the doom and scare tactics they use. Both Altman and Dario keep talking about how AI will replace workers and how we should regulate LLMs for national security, Dario’s main point.
LLMs are useful. We can all see that in agentic coding. But replacing everyone’s job? Hardly. And what’s with the scare tactic of trying to get the US government to ban foreign models?
LLMs are useful, and dare I say they’re on par with the internet. Making them cheaper and affordable is good for everyone. The fear mongering from Anthropic and OpenAI looks like an attempt to corner the US market into using only US models so they can keep the profits, especially since China has proven that LLMs are a commodity. US AI labs should work on making LLMs cheaper or better harness. Altman and Dario are not trustworthy.
> "Anthropic believes that they are the ones who should have final say over how Anthropic is used; given that they think only they should be developing leading edge AI, they by extension think that only they should have final say over AI generally. When you further combine this realization with the company’s pronouncements about AI’s ability to conduct all economic activity, you realize that Anthropic’s leadership effectively wants to have power over everything and everyone."
To be fair, we're simultaneously mocking anthropic for believing in safety so much and also for them thinking they're the only ones that care enough about it. It's true that no one else seems to care as much. Judging by reactions from everyone, all their safety talk is very bad PR.
The criticism isn't that they believe in safety too much. It's that they patently using safety as a red herring with the actual goal of regulating away competition that they know the cannot beat.
The people involved have been talking about safety long before they had any users or a company.
"Concrete Problems in AI Safety" by Dario and another founder was published 2016, anthropic was founded 2021. Among a bunch of other examples, including other founders.
If safety is seen as a joke now, it was really seen as a joke back then. The reason they keep shooting themselves in the foot with bad PR is because they truly believe in risks.
(so far they're also the only ones that have had their models banned, during a rare time they had a clear lead, and the only ones frustrating users with high safety controls. I can't see how this is maximising market position)
What they believe now or care about is fundamentally not very relevant: allowing them a monopoly would be a big mistake. Google was supposed to do no evil and it probably started that way but nothing guarantees the future other than having alternatives.
If there are genuine society risks in a tech I don't want to discourage CEOs from talking about them. I feel like we've spent decades talking about how evil chemical companies (etc.) were about covering up issues in the 20th century. But yes, that's different to being a reason to ban external models.
I agree re: Dario quietly pushing for government control. He also said LLMs would replace a lot of entry-level information jobs, doubling the unemployment rate from 4-5% to 10%.
but this crap may take forever to play out even if the outcome is well-known. Self-driving is "here", it's obvious that once it's cheap enough having a human behind a car wheel or a freight truck wheel is an absurd waste of human life (kinda like digging canals with bare hands instead of an excavator), yet truckers and uber drivers are still employed. But everyone knows the writing is on the wall for them.
Does this desideratum make any sense? The whole point is that you write to it in English or Urdu, and it writes the specified code. If you cut off everything but the code writing, you cut off everything.
no, apparently, otherwise we'd already have specialized models. every bit of meaningful human-generated data appears to improve the overall capability of the model.
I guess it can but it will be useless. After all the model superpower is awareness and ability to guess and infer some stuff. Right now a model saves you time not only by coding faster, but that it can figure out some stuff about the shape of the data and its purpose.
If you throw general purpose model at a codebase - it will look at the table and data logical connections beyond what is explicitly declared. It will figure out on its own that Salaries should be displayed on SalariesTable.php and it will "know" that your prices should include vat and so on.
A human knows that VAT and price go together and are related, full size LLM does too, stripped one - doesn't.
I'm trying and failing to find value running a potential Qwen 3.8 27b dense model on a 16 core, 128 GB of ram, 2080ti box. Yes, the GPU yells for help, but the problem is that no math works to upgrade this machine even when pouring $200 in rent every month into the large model providers...
How are you all justifying economical use of these local models right now? What's the cost efficient way to do this and do better (even with models evolving over time and losing now vs later) than the big labs?
You will simply not get more value out of running a local model vs paying for a subscription/API from the cloud in 2026. There is no math that will make local models come out ahead in $/intelligence/token.*
The point of local models is privacy, offline use, and maybe no guard rails.
* Not talking about enterprises that buy DGX racks and host Chinese models for internal use.
Points are starting to be made in favor of value, to the contrary of what you are affirming. Specifically because the new open weights models lower the TCO of hardware in an environment where new open weights were previously thought to be a thing of the past.
The only scenario is if you have enough work to do batch inference. Using a tiny fraction of GPU capacity to decode a single request at a time just doesn't make sense, as you say.
There's no point, except if you want privacy and independence. I am playing with a personal assistant that checks my emails, calendars, sends me an agenda and maintains my TODO list. I am not sending such data outside.
The models are good even by skeptics standard, it's just that evangelists are overselling the capabilities. If you understand the limits of LLMs not using them as a business is shooting yourself in the foot.
However, they are not at the point where they can effectively train themselves, nor did they are capable of researching their own method of learning. SWEs in mid-corps on my country are right now relegated to reviews and sanity check, basically babysitting the LLMs and making sure they're not spouting nonsense. If you think about it, that's basically QA and can also be delegated to another AI. If Bun's rust rewrite that they tout as fully LLM-led can pass the test of time in a year or so I think that's it.
I believe all that is now constrained by compute and capital, not tech.
There's infinite work to be done, so higher productivity makes people worth more. (Obviously this doesn't apply if AI can do everything but we're not there yet.)
I mean its pretty obvious right? This models are not flawless and sometimes reach stupid conclusions so there needs to be some one who watches it. Thought i must say u are right. Every one of them pretends that this new model is gonna finally take ur jobs lol
> I love their video with the idea that people will be able to do their hobbies while ai does their job...
Are you not already experiencing this? I think this is fairly common for people using AI now, though the time may not always go into hobbies or sports. It's common for me to setup Claude with an hour+ task while I catch up on housework, or while I'm getting ready in the morning.
In the last couple of weeks I've unfortunately had multiple family illnesses - it has been helpful to have Claude keep up with much of my product development programming work while I visit my mother in hospital and check on my father's recovery. I'm able to give more time to family without worrying that business progress isn't keeping up. The overnight Claude sessions while I'm asleep have been particularly helpful.
We are in the golden period where this IS possible. Once it becomes the norm to "do something else while your agent works", we'll be asked to do more WORK while the agent works, rather than do hobbies/housework/nap/etc.
The percentage of the population that needs to work will continue to go down because of aging and automation although you might not see it as a reduction of hours for a given individual employed person.
Already 38% of Americans aged 16 or older do not work and are not looking for work and yet are not dying from hunger or exposure. This would have seemed like a utopian dream to someone from the 1800s.
Being 16, 17 and 18 and not working is expected, given they are still at school. A good percentage of people aged 18 to 24 are studying full time.
This stat seems a bit misleading and doesn't support the "you can live and not work argument". How many of those people are retired? How many of those are under 18? How many of those simply cannot work? How many of those live at home or are on food stamp?
> In this case, Qwen3.8-Max was asked to create the oh-my-cli project from scratch and, over a 10+ day long-horizon autonomous coding run, build a self-evolving harness.
They don't explain how successful that went but it's a bit hilarious seen that an Anthropic dev explained that it's been 15 days Claude was hard at work --with nothing to show yet-- trying to rewrite itself in another language.
"You rewrite Claude Code, we rewrite oh-my-pi."
"You're nowhere after 15 days, we do it in 10."
Sure, it's apples to oranges and all that. But part of me thinks they know fully well what they did there.
It's meaningless. Models have always been able to do this and this capability is strengthened during RL since being able to explore the solution space to figure something out will give it a reward.
What is important is how long it can go without requiring human intervention. Not just that it's possible to run on its own for a time.
The second sentence of the page: "This also marks the first time we will open-source the weights of a Qwen-Max-class model — the open weights will be released next week."
They've also announced Qwen3.8-27B being released open-weight next week. Qwen3.6-27B is widely regarded as one of the best local models, especially since nothing else comes close to it, that isn't benchmaxxed, without being significantly larger. If 3.8 truly improves upon it that would be awesome.
Qwen3.6-35B is my daily driver for AI, and what convinced me to cancel my Claude subscription back in April. The Qwen3.6 line is easily the best local model I've tried, and I've tried a lot. I've got it diligently grinding away on my laptop right now, reviewing and fixing some bugs in my F# code.
I have been using Qwen3.6-35B-A3B as my daily driver as well and its been phenomenal when it comes to coding
How do you use a 72GB model as your daily driver locally?
At 4 bit it easily fits in 32GB. That's what most people use.
35B MoE is certainly a good and fast local model. I find 27B dense to be quite a bit smarter, so I daily drive that. I wish there was a ~100B MoE with maybe 10B active. It would be super smart and fast!
There was a 3.5 122B 10A release -
https://huggingface.co/Qwen/Qwen3.5-122B-A10B
I tried it for a bit, and It was not really worth its size. It got swept up in all the other AI news recently, but laguna s 2.1 I think is the best ~100B moe model right now
I didn't mention it above, but Laguna S is my other favorite model. I use Qwen a lot more, it's smaller and faster, but I like to switch to Laguna when I feel like I need a "heavy hitter" for certain huge or complex tasks.
What on earth hardwares do you guys have to be able to run 100gb models locally?! That's crazy! I'm here struggling to even get 27b models to run in somewhat usable way
Haha I'm on an Mac Studio with an M1 Ultra, 64gb ram. I bought it when it first came out, it just happens to be good for local LLMs. I have to use a smaller quant of Laguna S though (I think 4-bit? Not at my machine to check), as 8-bit and full size definitely don't fit in the 64gb I have.
Yeah, a good rule of thumb is that the weights take up ~100% of the size of the model, so 100B bytes (8-bit quant) would be, well, 100GB and a 4-bit quant would be half that.
I agree. 27b dense really did seem like the sweet spot.
I've heard 27B is smarter! I tried it some time ago but couldn't get it working with my oMLX. I need to try it again.
Honestly the 27b dense one punches way above its weight in a lot of domains, especially coding in my testing, so I think you will probably be disappointed.
In my case I would say they are comparable but moe models are looping and getting lost a lot more than dense models.
On the other hand having 90t/s with any local model is nice and Pi with loop police extension can prevent looping a lot.
I would recommend looking into Ornith1.0 - it's using Qwen3.6 35B-A3B and excels in coding, at least for my coding needs, Python, web-dev, SQL scripting and some C#. Using Pi harness.
compared to claude - how 'fast' is it in terms of throughput on your laptop?
On an 8GB GPU and 32GB laptop: ~5 words/s while running in Qubes (I don't have an install at the moment that'll tell me tokens/s).
Tolerable and usable for some things, though thinking makes it take about a minute to reply in many cases. But getting this kind of thing to run on 8GB of VRAM is the main benefit of the mix-of-experts setup: it can do partial GPU loading and get a ton better throughput than a similarly-sized dense model.
I use it with a strix halo server. 35B runs stupidly fast. 27B is about 700 TPS prefill and 30 TPS token generation. Which interestedly is about what Kimi K3 gives me depending on provider.
what hardware do you use or recommend for this? never heard of it until today.
Strix Halo is the unified memory platform from AMD. Similar to the DGX Spark from NVIDIA or the M series Macs.
I personally have the Framework Desktop, but there's also systems from other brands like Bosgame
On my SpacemiT K3 SBC with 32GB RAM (where models run on the eight A100 RISC-V cores with 1024 bit vectors) doing the same task I got 5, 5.8, 6.5 tok/s using gemma-4-26B-A4B-it-QAT-Q4_0.gguf, Qwen3.6-35B-A3B-Q4_K_M.gguf, Qwen3.5-35B-A3B-Q4_K_M.gguf. The corresponding dense models are more in the 2.5-3 tok/s range.
Kind of slow, but using only 14W of electricity so the Wh per task is twice as good as using my i9-13900 laptop with 4060 GPU.
It's pretty fast, faster than I could type anyway, but not as fast as Claude of course. My oMLX dashboard says I get about 45 tokens per second from the Qwen model I'm running (I host it on my M1 Mac Studio, not on my laptop).
What laptop?
It's just a MacBook Air with an M4, cheap and nothing special. I host Qwen on my Mac Studio, an M1 with 64gb ram. The model uses around 20-25gb ram depending on what it's doing.
Having invested in a machine with 128GB of RAM, I would love seeing something a bit larger than 27B / 35B, possibly a 54B dense model or 70B MoE would be much closer to the Qwen 3.8 Max experience.
This is what I've been waiting for. We are still using fine-tuned deployments of Qwen3.6-27B with a lot of success but could use a bump in intelligence. Here's hoping.
Qwen 3.6 27b has been the sweet spot for me in terms of local models. I've had good luck using it with Pi harness. Looking forward to this.
If they trained it well, and can do computer use, it will be a new era. Companies can keep PCs, put Qwen 3.8 27b on it and get rid of the employees, lol...
Yes let’s get rid of employees so no one is employed but somehow they can afford to buy my stuff
It was a matter of time for China to catch up with the US. In terms of infrastructure, manufacturing, and engineering workforce, China has the upperhand and I foresee them becoming the SOTA leaders. Maybe if the US wasn't so busy gatekeeping and keeping things proprietary, they would've had more trust from the open source community.
I think the window for a ban of open weight models is closing fast so let's hope US administration is going to miss it and we get Fable-level models (at least in some aspects) with open weights without infringing any newly introduced law as a long-term local baseline.
Even if the US does ban it, Europe due to its lack of European competitors, has no current similar protectionist incentives. Even if there is regulatory capture in the US, the rest of the world isn't going to follow suit until the current market leaders outright are replaced with other competitors that Europe would want to favor. The genie is out of the bottle in the West already.
> lack of European competitors
There's Mistral. Although they've been surprisingly quiet for the last 6 months.
I mean there is Mistral?
What can the US administration do about it?
Send you to jail.
Level 1: Anyone using unauthorized model weights in their business is prohibited from doing business with the government...
Level 2: ... or with anyone who does.
Level 3: Anyone caught with unauthorized model weights is subject to the same prosecution they could expect if they had been caught with CSAM.
What they always do. Send in armed men with guns? Export Controls. Import Controls. National Security Laws.
Good luck doing that in China?
You can send armed men to your house, or restrict companies in America from using these models.
They can also restrict US providers from serving these models. Looking at OpenRouter, there is currently 11 providers for 'DeepSeek V4 Flash 0731' with the only non-US one being Deepseek themselves, which aren't even the cheapest.
>Good luck doing that in China.
Crazy how all the 5 eyes nations banned Huawei within like 18 months of each other.
Import control would only affect US? Also good luck sending armed men to China.
> Today, we are officially releasing Qwen 3.8-Max, the most capable model in the Qwen family to date. This also marks the first time we will open-source the weights of a Qwen-Max-class model — the open weights will be released next week.
I don't understand. That's dated today, but:
https://twitter.com/alibaba_qwen/status/2078759124914098291
> Qwen3.8 is launching and going open-weight soon! [...] You don't have to wait to test it. Just now, the Qwen3.8-Max-Preview made its debut on Alibaba’s Token Plan, Qoder, and QoderWork.
That was on July 19th. I used it to draw this pelican: https://simonwillison.net/2026/Jul/20/afraid-of-chinese-mode...
So what are they releasing today?
July 19th post mentions “Max-Preview” vs. today’s post dropping the “Preview”. Unclear what changed if anything though.. Maybe broader availability or it’s a slightly improved checkpoint
My understanding is that these "preview" models are usually earlier RL checkpoints, and that "official release" happens when they're happy with the training run?
I believe they mentioned around the preview announcement that they'd be releasing improvements to capability, which I assume means continued training.
... other comments were right, this is the full qwen3.8-max model, two weeks ago was the qwen3.8-max-preview release.
Here's a pelican I just got out of the new model. It took 11 minutes and forgot the wheels! https://tools.simonwillison.net/markdown-svg-renderer#url=ht... (scroll to bottom)
The reasoning trace is pretty great:
> More additions: basket with fish in it? Cute detail — a fish poking out of a basket on the handlebars! This adds charm and pelican context.
If the price is $2/$6 that cost me 17 cents: https://www.llm-prices.com/#it=90&ot=29734&ic=2&oc=6
It gave the svg for the wheels in the reasoning trace then forgot to include them in its final answer. Lol.
It has a "definition" for wheel in SVG inside <defs>, but did not use it in the picture.
K3 also forgot the wheels when I tried it, but it turned out to be a bug in llama-server's built-in SVG viewer. I know you have your own rasterizer but just FYI, sometimes it isn't the model's fault.
Wow, bike geometry is really good! Except for the missing wheels lol
Do pelican bikes need wheels? They've got wings, after all... I think Qwen is on to something here.
It's because of the basket with the fish. like ET, fishy fishy sail home.
they releasing Max.
Once OpenAI and Anthropic are public, every such announcement will become a reliable sell signal
Agree, I don't necessarily see a strong argument favoring OpenAI or Anthropic here. In the interest of perspective, can anyone (perhaps playing devil's advocate) give one?
The open models are now good enough for what I want to do with them, let alone any future improvements. And factoring in efficiency gains, a model in the ~70b range starting to satisfy my needs would completely obviate the need to pay others for inference. This does not seem far-fetched to me, comparing with where open models were at this time last year. What am I missing?
Hardware and electrical costs including power usage and electric wiring/outlet costs of such machine.
Unless you are spending more than a max subscription (200 a month+) its cheaper to use the cloud.
But things are priced cheaper in the cloud now to lock you in and restrictions around hosted models are getting worse.
If you only have a $300 dollar laptop its probably not worth the upgrade.
I'm personally excited by local AI but the experience for the average isn't the same. I'm willing to get .5/s running on 10-15 years old machines but what I can do with it is limited.
coding on a laptop is only one use case
you can't create a new drug by running a model on a laptop. You can't serve a customer support bot running on a laptop. You can't generate video in bulk for many users on a laptop. So there is still a case for paying others for inference.
Does it justify the valuations? No idea, but some major use cases are still there. That's why they are rushing to implement, OpenAI creating a "deployment company", Anthropic having some pharma rumors, etc.
Surely all those use cases can be done right now without a closed model?
I think GP was asking specifically about the use case for closed models.
Can they still go public ? MiniMax M3 Pro is also coming, then DeepSeek-v4-Pro GA, then GLM5.5. There will only be bad news for them in the coming few weeks/months.
Fable 5.1 is coming, then GPT-6...
It's not so simple, if such a headline can get them closer to the regulatory capture they want to lock in American businesses and forbid them from using Chinese AI.
But the US are the country of freedom!
That's what Hollywood has been telling me my entire life!
US AI labs really rub me the wrong way, especially with the doom and scare tactics they use. Both Altman and Dario keep talking about how AI will replace workers and how we should regulate LLMs for national security, Dario’s main point.
LLMs are useful. We can all see that in agentic coding. But replacing everyone’s job? Hardly. And what’s with the scare tactic of trying to get the US government to ban foreign models?
LLMs are useful, and dare I say they’re on par with the internet. Making them cheaper and affordable is good for everyone. The fear mongering from Anthropic and OpenAI looks like an attempt to corner the US market into using only US models so they can keep the profits, especially since China has proven that LLMs are a commodity. US AI labs should work on making LLMs cheaper or better harness. Altman and Dario are not trustworthy.
You are right to feel that way about the frontier labs, especially Anthropic. From https://stratechery.com/2026/anthropics-safety-superpower/
> "Anthropic believes that they are the ones who should have final say over how Anthropic is used; given that they think only they should be developing leading edge AI, they by extension think that only they should have final say over AI generally. When you further combine this realization with the company’s pronouncements about AI’s ability to conduct all economic activity, you realize that Anthropic’s leadership effectively wants to have power over everything and everyone."
To be fair, we're simultaneously mocking anthropic for believing in safety so much and also for them thinking they're the only ones that care enough about it. It's true that no one else seems to care as much. Judging by reactions from everyone, all their safety talk is very bad PR.
The criticism isn't that they believe in safety too much. It's that they patently using safety as a red herring with the actual goal of regulating away competition that they know the cannot beat.
The people involved have been talking about safety long before they had any users or a company.
"Concrete Problems in AI Safety" by Dario and another founder was published 2016, anthropic was founded 2021. Among a bunch of other examples, including other founders.
If safety is seen as a joke now, it was really seen as a joke back then. The reason they keep shooting themselves in the foot with bad PR is because they truly believe in risks.
(so far they're also the only ones that have had their models banned, during a rare time they had a clear lead, and the only ones frustrating users with high safety controls. I can't see how this is maximising market position)
What they believe now or care about is fundamentally not very relevant: allowing them a monopoly would be a big mistake. Google was supposed to do no evil and it probably started that way but nothing guarantees the future other than having alternatives.
If there are genuine society risks in a tech I don't want to discourage CEOs from talking about them. I feel like we've spent decades talking about how evil chemical companies (etc.) were about covering up issues in the 20th century. But yes, that's different to being a reason to ban external models.
Sam drank the "superintelligence" kool aid early on and said 30-40% of jobs could be impacted by AI, but recently admitted he was wrong
> “My scorecard, at the highest level, would be we’ve been roughly right on technological predictions and pretty wrong on the social and economic implications” https://www.cxtoday.com/ai-automation-in-cx/sam-altman-softe...
I agree re: Dario quietly pushing for government control. He also said LLMs would replace a lot of entry-level information jobs, doubling the unemployment rate from 4-5% to 10%.
Yale did a study recently showing little impact on employment in high-AI exposed jobs https://budgetlab.yale.edu/research/ai-probably-not-yet-reas...
I imagine it will be a long tail. Most companies won’t fire people for AI but probably won’t immediately replace a person that leaves, if at all.
Or won't hire that extra person when they need to increase output.
but this crap may take forever to play out even if the outcome is well-known. Self-driving is "here", it's obvious that once it's cheap enough having a human behind a car wheel or a freight truck wheel is an absurd waste of human life (kinda like digging canals with bare hands instead of an excavator), yet truckers and uber drivers are still employed. But everyone knows the writing is on the wall for them.
It'll sell after 1-2 times, then the market will get jaded and ignore.
Only the ones that beat expectations
3.8 27b is the real news here
real
Can a model be stripped off anything not relevant to coding and get a lot lighter? Or is that impossible?
Just like we have professors with specialisation wondering if AI models can also be so.
You can... but the trick is to do so without killing performance. Turns out a lot of random things help make coding performance good.
Does this desideratum make any sense? The whole point is that you write to it in English or Urdu, and it writes the specified code. If you cut off everything but the code writing, you cut off everything.
Yes, look up REAP.
no, apparently, otherwise we'd already have specialized models. every bit of meaningful human-generated data appears to improve the overall capability of the model.
we do though and it can be stripped
I guess it can but it will be useless. After all the model superpower is awareness and ability to guess and infer some stuff. Right now a model saves you time not only by coding faster, but that it can figure out some stuff about the shape of the data and its purpose.
If you throw general purpose model at a codebase - it will look at the table and data logical connections beyond what is explicitly declared. It will figure out on its own that Salaries should be displayed on SalariesTable.php and it will "know" that your prices should include vat and so on.
A human knows that VAT and price go together and are related, full size LLM does too, stripped one - doesn't.
It seems this is the only mention of cost?
> Qwen3.8-Max comes with the official support for reasoning_effort, which can be used to adjust reasoning depth and control cost:
> xhigh (default): for complex tasks demanding thorough analysis
> medium: balancing accuracy and speed
> low: efficient reasoning optimizing for speed and cost
I hope this is significantly cheaper. I've been loving Deepseek for it's nearly free usage costs, hard to justify switching from cents per day.
They mention it explicitly in the Twitter post [0].
> Pricing: Input: $2.0 / M tokens Output: $6.0 / M tokens Implicit Caching: $0.25 / M tokens
[0]: https://x.com/Alibaba_Qwen/status/2084100707423289643
The linked qwencloud page has pricing; it's $2/6.
https://www.qwencloud.com/models/qwen3.8-max
Thanks, totally missed that!
> This also marks the first time we will open-source the weights of a Qwen-Max-class model — the open weights will be released next week.
Nice!
Is "cowork" a general industry term now? Here I was just getting used to "coding" replacing "programming".
Has anyone tried Qwen with the Fusion 360 MCP server? I feel like drawing with python is close enough but I'm curious
I'm trying and failing to find value running a potential Qwen 3.8 27b dense model on a 16 core, 128 GB of ram, 2080ti box. Yes, the GPU yells for help, but the problem is that no math works to upgrade this machine even when pouring $200 in rent every month into the large model providers...
How are you all justifying economical use of these local models right now? What's the cost efficient way to do this and do better (even with models evolving over time and losing now vs later) than the big labs?
You will simply not get more value out of running a local model vs paying for a subscription/API from the cloud in 2026. There is no math that will make local models come out ahead in $/intelligence/token.*
The point of local models is privacy, offline use, and maybe no guard rails.
* Not talking about enterprises that buy DGX racks and host Chinese models for internal use.
Points are starting to be made in favor of value, to the contrary of what you are affirming. Specifically because the new open weights models lower the TCO of hardware in an environment where new open weights were previously thought to be a thing of the past.
The only scenario is if you have enough work to do batch inference. Using a tiny fraction of GPU capacity to decode a single request at a time just doesn't make sense, as you say.
There's no point, except if you want privacy and independence. I am playing with a personal assistant that checks my emails, calendars, sends me an agenda and maintains my TODO list. I am not sending such data outside.
If you can do inference on the CPU, drop the GPU : it should be faster.
Lmao I love their video with the idea that people will be able to do their hobbies while ai does their job.
Surely Alibaba is leading by example here by reducing work hours per week while keeping pay the same right? Right?
That's the thing. Wny are companies like OpenAI/Anthropic/Alibaba/Kimi/Deepseek still hiring SWEs if their models have become so good?
OpenAI can't even build Codex app (not cli) for Linux... I thought code was solved?!
Ironically it's Electron BS, so actually, Sol could probably build itself...
https://en.wikipedia.org/wiki/Jevons_paradox
Otherwise known as the "no shit Sherlock" principle
The models are good even by skeptics standard, it's just that evangelists are overselling the capabilities. If you understand the limits of LLMs not using them as a business is shooting yourself in the foot.
However, they are not at the point where they can effectively train themselves, nor did they are capable of researching their own method of learning. SWEs in mid-corps on my country are right now relegated to reviews and sanity check, basically babysitting the LLMs and making sure they're not spouting nonsense. If you think about it, that's basically QA and can also be delegated to another AI. If Bun's rust rewrite that they tout as fully LLM-led can pass the test of time in a year or so I think that's it.
I believe all that is now constrained by compute and capital, not tech.
There's infinite work to be done, so higher productivity makes people worth more. (Obviously this doesn't apply if AI can do everything but we're not there yet.)
The world never runs out of problem. There is so much work to do.
I mean its pretty obvious right? This models are not flawless and sometimes reach stupid conclusions so there needs to be some one who watches it. Thought i must say u are right. Every one of them pretends that this new model is gonna finally take ur jobs lol
> I love their video with the idea that people will be able to do their hobbies while ai does their job...
Are you not already experiencing this? I think this is fairly common for people using AI now, though the time may not always go into hobbies or sports. It's common for me to setup Claude with an hour+ task while I catch up on housework, or while I'm getting ready in the morning.
In the last couple of weeks I've unfortunately had multiple family illnesses - it has been helpful to have Claude keep up with much of my product development programming work while I visit my mother in hospital and check on my father's recovery. I'm able to give more time to family without worrying that business progress isn't keeping up. The overnight Claude sessions while I'm asleep have been particularly helpful.
No I haven't had time to spend my afternoon rock climbing while ai generates documentation.
It's infinite work, I just did more work while codex was doing it's thing in the background.
We are in the golden period where this IS possible. Once it becomes the norm to "do something else while your agent works", we'll be asked to do more WORK while the agent works, rather than do hobbies/housework/nap/etc.
The percentage of the population that needs to work will continue to go down because of aging and automation although you might not see it as a reduction of hours for a given individual employed person.
Already 38% of Americans aged 16 or older do not work and are not looking for work and yet are not dying from hunger or exposure. This would have seemed like a utopian dream to someone from the 1800s.
Source?
Being 16, 17 and 18 and not working is expected, given they are still at school. A good percentage of people aged 18 to 24 are studying full time.
This stat seems a bit misleading and doesn't support the "you can live and not work argument". How many of those people are retired? How many of those are under 18? How many of those simply cannot work? How many of those live at home or are on food stamp?
Before AI:
I have 10 tasks that take 1 hour each to do.
After AI:
I have 100 tasks that take 10 minutes each to do.
Same amount of time spent working, maybe even more stressful, just more productive.
We will eventually need a self evolution benchmark to see where these large models can create recursive solutions that improve
the benchmark I trust most is whether the model can explain its own pricing page without getting confused
Not even humans can do that, you're literally asking for something beyond AGI
Bistromathics
https://www.hhgproject.org/entries/bistromathics.html
Tokenpocalypse canceled
> In this case, Qwen3.8-Max was asked to create the oh-my-cli project from scratch and, over a 10+ day long-horizon autonomous coding run, build a self-evolving harness.
They don't explain how successful that went but it's a bit hilarious seen that an Anthropic dev explained that it's been 15 days Claude was hard at work --with nothing to show yet-- trying to rewrite itself in another language.
"You rewrite Claude Code, we rewrite oh-my-pi."
"You're nowhere after 15 days, we do it in 10."
Sure, it's apples to oranges and all that. But part of me thinks they know fully well what they did there.
is it the right time to perhaps switch to QwenCode ?
i might end up cancelling claude, anybody else thinking of the same ?
Does the page actually load for anyone? I get stupid spa skeleton spinners.
ah so they distilled fable and sol, eh?
Does anyone know how token- and reasoning efficient it is? The charts don't show how many tokens were used in any benchmark.
The imminent third-party benchmarks will cover that.
“self-evolves through feedback loops”
Does this mean they distilled Claude? Sounds like what Claude Code will often do.
It's meaningless. Models have always been able to do this and this capability is strengthened during RL since being able to explore the solution space to figure something out will give it a reward.
What is important is how long it can go without requiring human intervention. Not just that it's possible to run on its own for a time.
Of course they did.
Are these latest Qwen models still open weights or has Qwen moved away from that?
The second sentence of the page: "This also marks the first time we will open-source the weights of a Qwen-Max-class model — the open weights will be released next week."
Page won’t load for me it’s just grey bars fading back and forth forever.
You can always wait until the page loads before posting your thoughts on the Internet