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

qwen.ai

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

#381

Post by DoctorOetker »

aurareturn wrote: post #90
jofzar wrote: post #4

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?

Lmao I love their video with the idea that people will be able to do their hobbies while ai does their job. Anecdotally, I'm even more busy with AI than before AI. I'm expected to do a lot more even if doing one thing is faster. 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 pro…

So you were working 10 x 1 hour = 10 hours before.

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

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

#382

Post by dangoodmanUT »

me551ah wrote: post #257

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 trill…

The major labs don’t allow assistant prefill, so you have to “summarize”

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

#386

Post by Bayart »

sparkling wrote: post #252
apexalpha wrote: post #198

Earlier quoted context omitted.

It was for me too but the new deepseek pricing is too good to ignore for now. I honestly think that with my electricity prices running qwen 36B myself is more expensive than hitting the cache rate at deepseek.

Can you elaborate on DeepSeek (deepseek-v4-flash, i assume?). What does your typical usage pattern look like and what is your weekly/monthly spend? I gave it a try for a few days (pi + openrouter + deepseek-v4-flash via deepinfra) and ended up paying ~$18 for rather light usage. Yes it's still cheap, yes it's fast, but i feel i would still get a better deal with a Claude subscription plan.

Going directly though DeepSeek's API and hitting all day long with light/medium tasks I'm at $5/month. That's with pretty vanilla ohmypi.

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

#387

Post by pama »

user43928 wrote: post #258
pama wrote: post #250

Earlier quoted context omitted.

You can do whatever you want with the model within your own organization. If you use it commercially—either as a model-as-a-service business or in a very large-scale product—you should check the additional license terms, which go beyond MIT. My interpretation is that Moonshot cares about the exact inference behavior and accurate representation of their model or derivatives, and perhaps also about capturing some addit…

Considering that very few orgs are going to be able to host a 3T parameter model internally, chances are most deployments would be subject to these restrictions and require a separate license from Moonshot.

Any US organization can rent compute.

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

#388

Post by accrual »

gpugreg wrote: post #323
palata wrote: post #253

Earlier quoted context omitted.

> 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! Are you trying to say that local models are hard to use because... you're having issues handling files properly? I am not sure I get the argument. I get the rest of the comment: local models require an investment upfront, and it is less convenient. I…

> you're having issues handling files properly? I guess they were using ollama, which does not tell you where it puts the models it downloads.

Filelight / ncdu are my friends for finding random 30GB directories containing cached models.

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

#390

Post by Iolaum »

jpgvm wrote: post #375
jcoder wrote: post #305

Earlier quoted context omitted.

> All requests to an LLM are idempotent, for every API call you need to send it the entire conversation history A more appropriate term is “stateless”. LLM responses are certainly not idempotent, as they are not even deterministic.

They can be deterministic. We did this at Groq, if you sent a request with exactly the same input token, seed and temperature value you would get precisely the same result every time. This is harder to do on other architectures that themselves aren't fully deterministic though.

Was the temperature 0? Cause unless I don't understand it right, any non-zero temperature implies probabilistic next token prediction.

You did mention, seed, which I haven't seen available anywhere else (in my limited experience) and it can explain a reproducible inference result, but I feel like this matches the "letter of the law" meaning of deterministic rather than the "spirit of the law".

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