It drew a better pelican riding a bicycle than Opus 4.7 did! https://simonwillison.net/2026/Apr/16/qwen-beats-opus/

[1] https://news.ycombinator.com/item?id=47246746 [2] https://news.ycombinator.com/item?id=47249343

https://huggingface.co/unsloth/Qwen3.6-35B-A3B-GGUF

Small openweight coding models are, imho, the way to go for custom agents tailored to the specific needs of dev shops that are restricted from accessing public models.

I'm thinking about banking and healthcare sector development agencies, for example.

It's a shame this remains a market largely overlooked by Western players, Mistral being the only one moving in that direction.

"Qwen's base models live in a very exam-heavy basin - distinct from other base models like llama/gemma. Shown below are the embeddings from randomly sampled rollouts from ambiguous initial words like "The" and "A":"

https://xcancel.com/N8Programs/status/2044408755790508113

Mudler APEX-I-Quality. then later I tried Byteshape Q3_K_S-3.40bpw

Both made claims that seemed too good to be true, but I couldn't find any traces of lobotomization doing long agent coding loops. with the byteshape quant I am up to 40+ t/s which is a speed that makes agents much more pleasant. On an rtx 3060 12GB and 32GB of system ram, I went from slamming all my available memory to having like 14GB to spare.

Balancing KV Cache and Context eating VRam super fast.

https://huggingface.co/Qwen/Qwen3.6-35B-A3B

My current is a used M1 MBP Pro with 16GB of ram.

I thought this was all I was ever going to need, but wanting to run really nice models locally has me thinking about upgrading.

Although, part of me wants to see how far I could get with my trusty laptop.

  #include <stdio.h>
  int m

  #include <stdio.h>
  int m</fim_prefix>

I asked it to give me instruction on how to create SSH key and it tried to do it instead of just answering.

https://internetexception.com/2026/04/16/testing-qwen-3-6/

As I am using mostly the non-open models, I have no idea what these numbers mean.

Should I use brew to install llma.ccp or the zypper to install the tumbleweed package?

give me the training data?

At the time of writing, all deepseek or qwen models are de facto prohibited in govcon, including local machine deployments via Ollama or similar. Although no legislative or executive mandate yet exists [1], it's perceived as a gap [2], and contracts are already including language for prohibition not just in the product but any part of the software environment.

The attack surface for a (non-agentic) model running in local ollama is basically non-existent . . but, eh . . I do get it, at some level. While they're not l33t haXX0ring your base, the models are still largely black boxes, can move your attention away from things, or towards things, with no one being the wiser. "Landing Craft? I see no landing craft". This would boil out in test, ideally, but hey, now you know how much time your typical defense subcon spends in meaningful software testing[3].

[1] See also OMB Memorandum M-25-22 (preference for AI developed and produced in the United States), NIST CAISI assessment of PRC-origin AI models as "adversary AI" (September 2025), and House Select Committee on the CCP Report (April 16, 2025), "DeepSeek Unmasked".

[2] Overall, rather than blacklist, I'd recommend a "whitelist" of permitted models, maintained dynamically. This would operate the same way you would manage libraries via SSCG/SSCM (software supply chain governance/management) . . but few if any defense subcons have enough onboard savvy to manage SSCG let alone spooling a parallel construct for models :(. Soooo . . ollama regex scrubbing it is.

[3] i.e. none at all, we barely have the ability to MAKE anything like software, given the combination of underwhelming pay scales and the fact defense companies always seem to have a requirement for on-site 100% in some random crappy town in the middle of BFE. If it wasn't for the downturn in tech we wouldn't have anyone useful at all, but we snagged some silcon refugees.

It's better than 27b?

benchmarks dont really help me so much

You want to wash your car. Car wash is 50m away. Should you walk or go by car?

> Walk. At 50 meters, the round trip is roughly 100 meters, taking about two minutes on foot. Driving would require starting the engine, navigating, parking, and dealing with unnecessary wear for a negligible distance. Walk to the car wash, and if the bay requires the vehicle inside, have it moved there or return on foot. Walking is faster and more efficient.

Classic response. It was really hard to one shot this with Qwen3.5 Q4_K_M.

Qwen3.6 UD-IQ4_XS also failed the first time, then I added this to the system prompt:

> Double check your logic for errors

Then I created a new dialog and asked the puzzle and it responded:

> Drive it. The car needs to be present to be washed. 50 meters is roughly a 1-minute walk or a 10-second drive. Walking leaves the car behind, making the wash impossible. Driving it the short distance is the only option that achieves the goal.

Now 3.6 gets it right every time. So not as great as a super model, but definitely an improvement.

I'll give this a try, but I would be surprised if it outperforms Qwen3.5-27B.

I want to reduce AI to zero. Granted, this is an impossible to win fight, but I feel like Don Quichotte here. Rather than windmill-dragons, it is some skynet 6.0 blob.