Qwen3.8-Max: A New Bar for Coding and Cowork

Particularly, 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.

I honestly think that with my electricity prices running qwen 36B myself is more expensive than hitting the cache rate at deepseek.

Not sure how Qwen3.8-Max is going to be licensed, hopefully it'll be Apache like the smaller ones.

IMHO this is a difficult question to answer. Part of the power of paid models comes from the software supporting it. With local models, you have tons of workflows that can severely influence the quality of the result.

In my personal experience, the SOTA models are way more consistent and can handle more complex questions. Part of that is (probably) because I don't let my local model access the internet, while paid models do use the internet to look at docs etc.

https://earendil.com/posts/session-portability/

“Just take the session thread to another provider” might not be feasible anymore soon-ish.

This is harder to do on other architectures that themselves aren't fully deterministic though.

Greedy decoding a single batch in most libraries will give you mostly deterministic outputs. Higher batch sizes can increase variance.

But all of this is down to CUDA and/or kernel implementation issues.

1: https://earendil.com/posts/session-portability

Note that the biggest GPU providers in the world are the hyper scalers and even they couldn’t allocate more if you pay for it. Because the rich companies and well funded ones are gobbling them up to the point where if tomorrow a 5T model that smokes every other model in the world is released you just can’t afford inference.

This isn't without it's challenges however.

1. This will increase costs drastically, since they would need to a run a sandbox per use to ensure data isolation.

2. Increased latency, and this directly limits how much of the harness can be moved to the cloud before the users notice sluggishness

It did a self update and it worked for a single request (me just saying hi). Pasted in the prompt to build the page and it 401'd.

However, that would just change the weights values and not their dimensions.

EDIT: This was obviously meant to be facetious with the intent to highlight the negative effects on the general population that datacenter buildout has in the US.

The need of the rich, you mean. In these kinds of situations it's never about the many, if it was about them different choices would be made.

There's Mistral. Although they've been surprisingly quiet for the last 6 months.

I agree, but you are underestimating EU regulatory incompetence

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.

3.8 Max Preview ran 90% discount on QwenCloud exactly as when Moonshot couldn't keep up with all the signups.

So just like in IPhone vs Android, you could end up with a situation where Chinese firms compete and get most of the revenue and usage with low profit margins but OpenAI and Anthropic capture a premium side of the market and still get a lot of profits.

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.

I have numerous cases where Sol failed and only Fable could solve a problem. For example yesterday I was merging a Q2 curved with a Bezier curved face in 3D using OpenSCAD. I tried for over 2 hours with Sol 5.6 high and x-high.

Fable two-shotted it in about 30 minutes.

In my experience open models (or GLM, DS and Kimi) are radically worse than either of Claude or ChatGPT at these tasks.

I think there is a huge "long tail" of tasks like this where the frontier labs are ahead, and I think this long tail is quite important.

Their marketing department :-) . I'm only half-joking; those guys are hard at work finding the best product-market fit for ChatGPT/Claude. "Product market fit" means "strongest revenue", which is not necessarily going to bring the best tool for you or me, but the one that can either get more consumers to shell off money, or more enterprises to cough money for licenses, and in both cases those consumer basis will be narrowed down to what legal and geopolitical circumstances allow OpenAI and Anthropic (and this is why they want to ban the competition!). It also means dark patterns and enshitification, of which I'm already seeing some both in the Codex interface (it was just renamed "ChatGPT"!!!) and in Claude Code (which also is just "Claude" now and can't '@' properly any longer). So in the medium run most people will be better off running an open source harness that can use any model.

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.

That's what Hollywood has been telling me my entire life!

> "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."

> “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...

Edit to answer my own question:

License file [2] states:

> If the Licensee or any of its affiliates operates a Model as a Service business, and the aggregate revenue of the Licensee and its affiliates exceeds 20 million US dollars (or the equivalent in other currencies) in total over any consecutive 12 months, the Licensee must enter into a separate agreement with Moonshot AI before using the Software or its derivative works for any commercial purpose.

[1] https://huggingface.co/moonshotai/Kimi-K3

[2] https://huggingface.co/moonshotai/Kimi-K3/blob/main/LICENSE

Ironically it's Electron BS, so actually, Sol could probably build itself...

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.

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?

It's infinite work, I just did more work while codex was doing it's thing in the background.

And then you were doing 100 x 10 minutes = 100 / 6 hours = ~16 hours & 40 minutes.

Intuitively it feels like focused models should be better models, but human programmers are ‘better’ knowing programming alongside general stuff. We’re not reduced by reading economics or Tolkien, removing such knowledge would be premature optimization.

I mostly just need it to mostly be a very advanced NLP model that is able to figure the set of tools that it needs to call with what argument. Then it will just follow a predefined decision tree.

https://www.hhgproject.org/entries/bistromathics.html

But also, control and consistency. A local model cannot be changed out under your feet like an API model can be.

For all we know, inference might be dirt cheap, they might just be hiking the API prices so high for us to think subscriptions are subsidized.