As someone who is searching for a new programming contract right now, reading all of the incredible abilities here is pretty intimidating. Especially since I get almost all of my projects from Upwork which is an outsourcing site.

I believe I am competing directly with these frontier models in some circumstances. Like there are a ton of programmers who previously would be outsourcing work to that site, but now they assign that same work to AI agents.

Ever since November 2022 when ChatGPT blew up, I have been focusing on agents in order to try to get ahead of the curve. But I haven't managed to get an agent business off the ground and have been doing poorly paid agentic projects from that site instead.

But now everyone is building agents, and this crazy list of accomplishments makes it look like we are close to the point where the agents are building agents.

In fact the next time I get an Upwork contract for another agent, I actually should run it through my agent and see how far it can get. What I'm seeing a lot of now is requests to automate as much of a business as possible.

Anyway the point is these models are just about capable of doing the entire job of analyzing a small business and building out all the agents and iterating on them with the business owner.

That's actually what I should build is a SaaS that does that. Which I would if I wasn't basically desperate to get another contract this week.

And I know Upwork is bad but I have not had much success with other options on short notice.

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.

This makes me wonder if AI companies even have a MOAT in the first place.

All requests to an LLM are idempotent, for every API call you need to send it the entire conversation history so that it can process it. LLMs do not learn or remember anything, which makes it super easy for users to switch LLMs on the fly. Most popular AI frameworks, make this a one-liner change these days.

And that makes me wonder if the trillion dollar valuations for OpenAI and Claude are even justified. Cause if that is justified, then Kimi, Qwen, Deepseek etc are also valued at a trillion dollars. Or all of them are worth a lot less. One of those statements is true.

Also this makes me wonder if the next iteration of LLMs would be based on fine-tuning, where LLMs actually learn from your past behaviour so that it would grant some amount of stickiness to the product. OpenAI used to offer fine tuning runs for GPT-3.5, but they don't seem to do that anymore.

The visual web development / perceptionbench scores are very promising for image->html flows. Here are some test results.

Original designs: https://image.non.io/257dc9cb-9e6b-4e00-8f12-23ea5e073649.we...

These are fairly rich, pattern-heavy, nuanced designs. I've asked each to create it as a SPA where the map flows behind it.

Opus 5 results: https://html.non.io/opusAcmeBooks

Qwen 3.8 max results: https://html.non.io/qwenAcmeBooks

Same prompt for both for the conversion. I used OpenCode for the qwen version, but I encountered a significant amount of errors / timeouts while it was running. Claude finished in around 16 min, but I spent close to 2 hours shepherding the Qwen build. For the implementation, there were signs it had good vision, but the timeouts make this very hard to use in a production setting.

I would achieve a lot too if I wasn't interrupted with "how is progressing meetings" And if with put those meetings in the model it would also be as bad as everyone

Whilst these coding models improve, they seem to cater for multiple languages, and for many, a trimmed-down LLM that supports just one language would be fantastic. This would be smaller, more able to run on the hardware people have at home, realistically (even on the CPU). Having one LLM that knows assembler, java, bain, C etc is neat, but when you only work and use one language at a time, it would be kinda neat to have those broken out into dedicated, smaller LLMs. After all Qwen3.8-Max handles over 90 programming languages - ask any programmer to name a list of computer languages and if they get over 20, they are doing well.

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.

> 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?

Once OpenAI and Anthropic are public, every such announcement will become a reliable sell signal

3.8 27b is the real news here

AWS Bedrock still lacks the support for the latest open weights models like GLM 5.2, DSV4 Flash 0731, Kimi K3 etc. Even they only support Qwen 3 which is a very old model. Any specific reasons they are reluctant to support open weights models?

> How Qwen Cloud handles your data during inference? > Qwen Cloud does not use your API inputs or outputs to train or improve models. > Learn more about how your data is handled during the inference process from Zero data retention.

If this is true, this is a big deal for me, but unfortunately I cannot find anything in their legal agreements, so this marketing sentence is worthless.

2.4 Trillion parameters with open weights releasing next week? The open-source community is going to need a collective GoFundMe just to buy enough VRAM to host this monster.

I like that Alibaba are emphasizing more challenging real-world "coding" tasks as well as just benchmarks.

The "Reproduce a research paper — then improve it" seems particularly impressive as well as actually useful - perhaps as close as we're going to get to "recursive self-improvement" given that these are data-driven not code-driven models.

I'm not sure how much real world use of AI is 100% hands off agentic coding, but I guess for objective evaluation purposes it needs to be something without a human in the loop.

It'd be interesting to see a comparison of each company's best models on a suite of real-world challenging tasks like this, but I guess difficult since each group of tasks would need to be one-time-use only to prevent subsequent benchmaxxing which makes comparisons useless.

Qwen3.6 35B-A3B Is my current backup model after Ornith—1.0, which has performed slightly better and faster at Python, db and some backend tasks. About 75 tokens/sec on a 5060ti 16gb depending on context size (usually 64k)

At $2/$6, it can be much more "approachable" than K3. Wondering what have they done differently to be able to afford this price (that K3 couldn't, apparently - most other providers offer similar prices to K3 itself).

I used the qwen 3.6 models and convinced myself to go ahead and get a gpu. I got the 7900 xt with 20 gb vram.

I have a 5080 Super RTX but its still not enough to run these big models. I use Gemma 4 right now on a Debian Linux with no GUI that I access remotely. Its works pretty well, but still not as good as any of the big models like Opus 5... Can't wait to be able to buy a personal home server that can run much bigger models. I heard some companies have started building AI PCs only used to have a personal AI model at home, have any of you tried one yet?

Just arrived on open router. Was waiting for this.

https://openrouter.ai/qwen/qwen3.8-max

> 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!

I noticed they are careful not show Apple products, what operating system are they using in the demo videos?

Waiting for Qwen3.8-27B :)

Their base models and architecture has quickly become the go-to for local inference and fine-tuning, even when they introduced some tricky things like GDN, so many people use it, that it was matter of days/weeks until lots of OSS frameworks adopted it.

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.

I used Qwen3.8 Max Preview for 2 weeks and can't tell if I truly miss Fable.

Qwen doesn't overuse em-dashes, weird sentence structures with bold text: normal text.

In my private GitHub Repo Qwen finished as much tasks as Fable did. Without hitting 5-hour session limits. Qwen's token-per-second performance fluctuated greatly. From 20 tps up to 80 tps.

Qwen failed on some "reading between the lines", but so did Fable.

The only noticeable difference is skill loading. Until "Must use" in a skills front matter, Qwen tends to be very lightly loading skills. Superpowers works, because of the same Must use wording. All my local skills use the same wording now. For installed Skills, it's more or less my responsibility to remind Qwen using a certain skill.

I used Qwen together with Qwen Code. I didn't compare Qwen+Claude Code.

With my Owl code scanner, Claude and Qwen produced similar results. Qwen repeatedly used the same output format, despite no format provided and disabling memory. Fable varied greatly. Both followed the file format output and produced valid files.

With Qwen 3.8 being now a visual understanding model, I don't really miss Fable/Opus/Sonnet for my private projects.

At work we are still exclusively using Anthropic models with Claude Code. No change incoming on that front.

Privately, I'm sold on Qwen and Qwen Token plan. No session limits, many open weights models available via a single API.

Since it's horrifying thinking about what US companies do with my data, it's no worse or better thinking about what Chinese companies do. It's a choice between the Plague and Cholera.

I've been waiting for a tiny model release! Kimi, Deepseek, and GLM were all monsters, the 27B 3.8 is filling a gap thats been open since Qwen 3.5

Also I love how literal Qwen 3.5 was, hopefully 3.8 is still extremely literal. The token explosions were actually helpful in debugging prompts.

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?

In a 30M+ LOC repo we blew out Qwen3.6’s context thinking about a code review. This was via direct call to model. Do harnesses facilitate better context management, or is there something else to accommodate its smaller context window?

Qwen3.8-35B-A3B would be really appreciated (-/\-)

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.

So the news here is that this model left the preview stage, and they are also releasing an open-weight version of the Max series for the first time.

You can always try out this model for free on Qwen Chat. Alibaba Cloud has too much friction for me.

unfortunately the alibaba cloud does not seem like it can keep up with the demand caused by this announcement, i'm just getting endless timeouts

I was super excited to read this but lost the plot when I go to

> Qwen3.8-Max was asked to create the oh-my-cli project from scratch and, over a 10+ day long-horizon autonomous coding run

10+ days of building what exactly? Is that a shell prompt customization toolkit? Or a coding harness??

Neither - well, sort of the second. The poorly named thing is a self evolving coding harness, the self evolving part makes it a big deal.

I wish they had picked a different example.

> The open weights will be released next week.

Has anyone know whatever they will release any video / image generation capabilities? Video input obviously suppose to be present.

Is "cowork" a general industry term now? Here I was just getting used to "coding" replacing "programming".

KimiK3-Thinking is not a matching model for comparsion?

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 so glad exciting releases like this are still going to have components with open weights like Qwen 3.8 27B dense

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

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?

What is the best LLM that I can use right now that is optimized for everything other than coding?

The last thing I want my personal agent to do is to write up code and run arbitrary commands. That is practically a legalized RCE.

is it the right time to perhaps switch to QwenCode ?

i might end up cancelling claude, anybody else thinking of the same ?

Have you retrained Qwen not to give Hoover Institution answers to geopolitical questions?

It is clearly distilled with an aggressive pro West bias to increase sales in the West.

Does the page actually load for anyone? I get stupid spa skeleton spinners.

they release new qwen model every two weeks?

AI is a commodity. This is proven now. And valuations will have to drop 90%.

There's a lot of AI models that each 'win' one week over the next. The pure definition of a commodity market and not a 'winner takes all market' as valuations would imply

Tokenpocalypse canceled

Does anyone know how token- and reasoning efficient it is? The charts don't show how many tokens were used in any benchmark.

Now, if only we could afford a setup decent enough to run 2/3 instances at the same time...

> 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.

ah so they distilled fable and sol, eh?

“self-evolves through feedback loops”

Does this mean they distilled Claude? Sounds like what Claude Code will often do.

Are these latest Qwen models still open weights or has Qwen moved away from that?