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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.

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...

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

they releasing Max.

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?

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.

It'll sell after 1-2 times, then the market will get jaded and ignore.

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.

Only the ones that beat expectations

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.

What can the US administration do about it?

real

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.

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.

[dead]

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

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.

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.

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.

That's the thing. Wny are companies like OpenAI/Anthropic/Alibaba/Kimi/Deepseek still hiring SWEs if their models have become so good?

> 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.

  Lmao I love their video with the idea that people will be able to do their hobbies while ai does their job.

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.

Not even humans can do that, you're literally asking for something beyond AGI

The imminent third-party benchmarks will cover that.

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.

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."