Qwen3.8-Max: A New Bar for Coding and Cowork
https://qwen.ai/blog?id=qwen3.8Particularly, I had one team member who was extremely sceptical of AIs/LLMs/harnesses and refused to use them. One day he said "Well, I have an RTX 5090 doing nothing... should I try to get something up on it?" and a few minutes later he had 3.6-35B loaded up, running OpenCode.
It continues to be a workhorse to this day, running on both my local Mac for various types of jobs, an AMD R9700 at the office, and said teammember still uses it on his 5090, although in practical terms we do a lot more with DS-V4-Flash-0731 these days.
Local: You need to invest $thousands into GPU and/or very-high-end CPU+Memory hardware.
Vendor: You can use any existing device, even a phone or tablet. A very low-end laptop is fine.
> takes literal minutes to get started
Local: Typical scenario is hours just to download the software, the model weights, and then faffing around with CUDA and matching your GPU drivers.
Vendor: Free-tier available instantly on a web URL. Even local agents have free tiers from multiple vendors. Install is a single command and/or download and "next,next,next,finish" wizard that takes ~1 minute.
> you can just `rm -fr` it and forget the whole thing existed.
I'm still cleaning up multi-GB model weights floating around in hidden subdirectories under my user profile from months ago when I was experimenting with local models!
Meanwhile I simply... stopped using Gemini. That was the entire process: I no longer actively use it. They stopped billing me for my token usage, because it is now zero. That's... it.
You have it totally backwards.
That's one of my main issues with AI anyways, the thought of having all my data go through some sketchy foreign (to me) entity with questionable motives and under a questionable regime.
Local AI solves for all of those.
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.
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 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?
Nice!
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.
> 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.
Surely Alibaba is leading by example here by reducing work hours per week while keeping pay the same right? Right?
Just like we have professors with specialisation wondering if AI models can also be so.
You can always try out this model for free on Qwen Chat. Alibaba Cloud has too much friction for me.
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?
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.
i might end up cancelling claude, anybody else thinking of the same ?
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
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.
Does this mean they distilled Claude? Sounds like what Claude Code will often do.