Qwen3.6-Max-Preview: Smarter, Sharper, Still Evolving

Some people seem to agree and some don't, but I think that indicates we're just down to your specific domain and usage patterns rather than the SOTA models being objectively better like they clearly used to be.

Every time I try to build something with it, the output is worse than other models I use (Gemini, Claude), it takes longer to reach an answer and plenty of times it gets stuck in a loop.

I was very impressed.

FAANGS love to give away money to get people addicted to their platforms, and even they, the richest companies in the world, are throttling or reducing Opus usage for paying members, because even the money we pay them doesn't cover it.

Meanwhile, these are usable on local deployments! (and that's with the limited allowance our AI overlords afford us when it comes to choices for graphics cards too!)

Could you please share more about this

As so many things these days: It's a cult.

I've used Claude for many months now. Since February I see a stark decline in the work I do with it.

I've also tried to use it for GPU programming where it absolutely sucks at, with Sonnet, Opus 4.5 and 4.6

But if you share that sentiment, it's always a "You're just holding it wrong" or "The next model will surely solve this"

For me it's just a tool, so I shrug.

The idea that every new foundation model needs to be pretrained from scratch, using warehouses of GPUs to crunch the same 50 terabytes of data from the same original dumps of Common Crawl and various Russian pirate sites, is hard to justify on an intuitive basis. I think the hard work has already been done. We just don't know how to leverage it properly yet.

People think that Chinese AI labs are just super cool bros that love sharing for free.

The don't understand it's just a state sponsored venture meant to further entrench China in global supply and logistics. China's VCs are Chinese banks and a sprinkle of "private" money. Private in quotes because technically it still belongs to the state anyway.

China doesn't have companies and government like the US. It just has government, and a thin veil of "company" that readily fool westerners.

It would be a great day for humanity if people would stopping glazing text autocomplete as revolutionary.

I find even the SOTA models to be far away from trustworthy for anything beyond throwaway tasks. Supervising a less-than-SOTA model to save $10 to $100 per month is not attractive to me in the least.

I have been experimenting with self hosted models for smaller throwaway tasks a lot. It’s fun, but I’m not going to waste my time with it for the real work.

But, if for some reason everything stopped at Opus 4.5 level and we never got a better model (and 4.6/4.7 are better, if only marginally so and mostly expanding the kind of work it can do rather than making it better at making web apps), we could still do a lot of real work real fast with Opus 4.5, and software development would never go back to everyone handwriting most of the code.

A model as good as Opus 4.5 (or slightly better according to the mostly easily gamed benchmarks) at a 10th the price is probably a worthwhile proposition for a lot of people. $100 a month, or more, to get Opus 4.7 is well worth it for a western developer...the time the lower-end models waste is far more expensive than the cost of using the most expensive models. For the foreseeable future, I'll keep paying a premium for the models that waste less of my time and produce better results with less prodding.

But, also, it's wild how fast things move. Open models you can run on relatively modest hardware are competitive with frontier models of two years ago. I mean, you can run Qwen 3.6 MoE 35B A3B or the larger Gemma 4 models on normal hardware, like a beefy Macbook or a Strix Halo or any recentish 24GB/32GB GPU...not much more expensive than the average developer laptop of pre-AI times. And, it can write code. It can write decent prose (Qwen is maybe better at code, Gemma definitely has better prose), they can use tools, they have a big enough context window for real work. They aren't as good as Opus 4.5, yet.

Anyway, I use several models at this point, for security and code reviews, even if Claude Code with Opus is still obviously the best option for most software development tasks. I'll give Qwen a try, too. I like their small models, which punch well above their weight, I'll probably like the big one, too.

Even many people on a Claude subscription aren't choosing or able to choose Opus 4.7 because of those cost/usage pressures. Often using Sonnet or an older opus, because of the value Vs. quality curve.

If even cheaper models start reaching that level (GLM 5.1 is also close enough that I'm using it at lot), that's a big deal, and a totally valid reason to compare against Opus 4.5

In any case a benchmark provided by the provider is always biased, they will pick the frameworks where their model fares well. Omit the others.

Independent benchmarks are the go to.

Anthropic's "Best Practices" doc[0] for Claude Code states, "A clean session with a better prompt almost always outperforms a long session with accumulated corrections."

[0] https://code.claude.com/docs/en/best-practices

I use llama-server that comes with llama.cpp instead of using ollama. Here are the exact settings I use.

llama-server -ngl 99 -c 192072 -fa on --cache-type-k q4_0 --cache-type-v q4_0 --host 0.0.0.0 --sleep-idle-seconds 300 -m Qwen3.5-27B-Q4_K_M.gguf

You can keep the KV cache on GPU which means it's pretty damn fast and you should be able to hold a reasonable context window size (on your GPU).

I've had really impressive results locally with this.

I'd strongly recommend cloning llama.cpp locally btw (in wsl2) and asking a frontier model in eg Claude code to set it up for you and tweak it. In my experience the apps that sit on top of llama.cpp don't expose all the options and flags and one wrong flag can mean terrible performance (eg context windows not being cached). If you compile it from source with a coding agent it can look up the actual code when things go wrong.

You should be able to get at least 20-40tok/s on that machine on Gemma 4 which is very usable, probabaly faster on qwen3.6 since it's only 3b active params.

Grab a recent win-vulkan-x64 build of llama.cpp here: https://github.com/ggml-org/llama.cpp/releases - llama.cpp is the engine used by Ollama and common wisdom is to just use it directly. You can try CUDA as well for a speedup but in my experience Vulkan is most likely to "just work" and is not too far behind in speed.

For best quality, download the biggest version of Qwen 3.5 27B you can fit on your 4090 while still leaving room for context and overhead: https://huggingface.co/unsloth/Qwen3.5-27B-GGUF - I would try the UD-Q5_K_XL but you might have to drop down to Q5_K_S. For best speed, you could use Qwen 3.6 35B-A3B (bigger model but fewer parameters are active per token): https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF - probably the UD-Q4_K_S for this one.

Now you need to make sure the whole model is fitting in VRAM on the 4090 - if anything gets offloaded to system memory it's going to slow way down. You'll want to read the docs here: https://github.com/ggml-org/llama.cpp/tree/master/tools/serv... (and probably random github issues and posts on r/localllama as well), but to get started:

  llama-server -m /path/to/above/model/here.gguf --no-mmap --fit on --fit-ctx 20000 --parallel 1

Go to the printed link (localhost:8080 by default) and check that the model seems to be working normally in the default chat interface. Then, you're going to want more context space than 20k tokens, so look at your available VRAM (I think the regular Windows task manager resource monitor will show this) and incrementally increase the fit-ctx target until it's almost full. 100k context is enough for basic coding, but more like 200k would be better. Qwen's max native context length is 262,144. If you want to push this to the limit you can use `--fit-target <amount of memory in MB>` to reduce the free VRAM target to less than the default 1024 - this may slow down the rest of your system though.

Finally, start hooking up coding harnesses (llama-server is providing an OpenAI-compatible API at localhost:8080/v1/ with no password/token). Opencode seems to work pretty reliably, although there's been some controversy about telemetry and such. Zed has a nice GUI but Qwen sometimes has trouble with its tools. Frankly I haven't found an open harness I'm really happy with.

How is that different from American?

Oh wait, it doesn't apply to those…

If you overuse LLMs or get excited about them at all, you're ngmi and a complete idiot.

In my own experience, even with web app of medium scale (think Odoo kind of ERP), they are next to useless in understanding and modling domain correctly with very detailed written specs fed in (whole directory with index.md and sub sections and more detailed sections/chapters in separate markdown files with pointers in index.md) and I am not talking open weight models here - I am talking SOTA Claude Opus 4.6 and Gemini 3.1 Pro etc.

But that narrative isn't popular. I see the parallels here with the Crypto and NFT era. That was surely the future and at least my firm pays me in cypto whereas NFTs are used for rewarding bonusess.

it puts a massive backstop at the margins they can possibly extract from users

It's not crushing Opus 4.5 in real-life use for me, but it's close enough to be near interchangeable with Sonnet for me for a lot of tasks, though some of the "savings" are eaten up by seemingly using more tokens for similar complexity tasks (I don't have enough data yet, but I've pushed ~500m tokens through it so far.

They have difficulty supplying their users with capacity, but in an email they pointed out that they are aware of it. During peak hours, I experience degraded performance. But I am on their lowest tier subscription, so I understand if my demand is not prioritized during those hours.

https://arena.ai/leaderboard/text?viewBy=plot&license=open-s...

I did give it one task which was more complex and I was quite impressed by. I had a local setup with Tiltdev, K3S and a pnpm monorepo which was failing to run the web application dev server; GLM correctly figured out that it was a container image build cache issue after inspecting the containers etc and corrected the Tiltfile and build setup.