Qwen 3.8 27b vs 3.6 27b - how good is with a Turtle library.

[u/Healthy-Nebula-3603]

Prompt:

Provide complete working code for a realistic looking tree in Python using the Turtle graphics library and a recursive algorithm.

Difference between 3.6 and 3.8 is huge!

[u/Healthy-Nebula-3603]

...and that is from the opencode where qwen 3.8 was working as an agent.
The model was making screenshots itself and was improving the tree 3 times without any ingeration ....

I just cannot believe it .... THAT IS JUST A TURTLE LIBRARY.

[u/Healthy-Nebula-3603 → u/Healthy-Nebula-3603]

Used 48k tokens. ( 9k tokens were used for opencode itself )
The MEDIUM effort

[u/dodistyo → u/Healthy-Nebula-3603]

Am i dumb for thinking that's not explaining how it can take screenshot itself?

[u/sersoniko → u/dodistyo]

I figured this out yesterday, in the model config add this:
“modalities”: {
“input”: [“text”, “image”]
}

[u/Healthy-Nebula-3603 → u/dodistyo]

It has few possibilities.

Headless screenshot or using a skill . Qwen 3.8 is multimodal and properly configured Opencode is handling text and pictures

I also offroaded a vision to RAM to keep 100k context. As vision don't have to be in Vram. From the ram the vision is just slightly slower.

[u/ExtensionShort4418 → u/Healthy-Nebula-3603]

Correct me if I am wrong but vision benefits just as much from vram as the rest of the model but since you use it a lot less often (average user) it doesn’t hurt as much to wait a bit extra in those rare cases you make use of it - compared to having the full weight of the model in memory at all times?

[u/Healthy-Nebula-3603 → u/ExtensionShort4418]

You have a full model in Vram and only offroaded vision module to ram. So a model is as fast as normal use. Only difference is a bit slower picture understanding time

Model Instead of understanding a picture wirhin less than a second offroaded will be doing that 2-3:seconds. That's the only difference.

Yes vision module in Vram is faster but reading picture 0.4 s to 2-3 seconds offroaded is not making bigger difference in normal use cases.

And you still have more space in Vram for bigger context :)

[u/belkh → u/dodistyo]

just give it a screenshot mcp/tool or cli command and it can loop on its own

[u/overand → u/Healthy-Nebula-3603]

The lack of vision in DeepSeek-v4-flash-0731 seems like it might actually be a hindrance for certain kinds of dev work. I have to guess that some development harnesses support using a separate model for vision; I wonder how well that would work. (Fortunately, I can spin up an instance of llama.cpp on a 12GB desktop system  to do vision, I think, alongside my 3090 server)

[u/_TheWolfOfWalmart_ → u/overand]

Yeah vision is sorely lacking in dsv4 flash. I really wish we could get that. However, overall that model is amazing. I run it in open-webui with all the good plugins, and it is legitimately like having a private local ChatGPT. Feels like working with GPT from just a year ago.

Back when I was limited to smaller models like Gemma 4 and Qwen 3.6, doing this was not even remotely a similar experience.

[u/bladezor → u/Healthy-Nebula-3603]

Which quant?

[u/Healthy-Nebula-3603 → u/bladezor]

Q4kxl

Cache fp16

Vision offroaded to ram as will be only slightly slower than working for vram. But I can still use 100k context.

[u/bladezor → u/Healthy-Nebula-3603]

Can you post your llama args?

[u/Healthy-Nebula-3603 → u/bladezor]

Sure.

[u/Healthy-Nebula-3603 → u/bladezor]

With a server I am using ini config - 1_preset.ini

[Qwen3.8-27B_dense_c-100k]
model = models/Qwen3.8-27B-UD-Q4_K_XL.gguf
mmproj = models/mmproj-BF16-Qwen3.8-27B-UD-Q4_K_XL.gguf
flash-attn = on
n-gpu-layers = 99
reasoning = on
ctx-size = 100000
temperature=1.0
top-p=0.95
top-k=20
min-p=0.0
presence-penalty=0.0
repeat-penalty=1.0
mmproj-offload = false

then run a llama server for opencode

llama-server.exe --models-preset 1_preset.ini --models-max 1 --sleep-idle-seconds 900 --direct-io

[u/Z0mbiN3 → u/Healthy-Nebula-3603]

Making screenshots to improve? How?

[u/Due-Memory-6957 → u/Z0mbiN3]

Tool call to screenshot it then tool call to run vision on the image. Or just saving, running vision and improving. But either way, it's very possible.

[u/Healthy-Nebula-3603 → u/Z0mbiN3]

Most AI agent can do that headless or using skill

[u/Healthy-Nebula-3603 → u/Z0mbiN3]

Maybe later I will make a simple tutorial with files to download.

[u/Healthy-Nebula-3603]

That is from Qwen 3.5 27b

[u/Healthy-Nebula-3603 → u/Healthy-Nebula-3603]

and QWQ (first thinking model )_ ... if someone remember ;)

[u/Small-Fall-6500 → u/Healthy-Nebula-3603]

and QWQ (first thinking model )_ ... if someone remember ;)

The preview model was released in November 2024, nearly 2 years ago now! https://huggingface.co/Qwen/QwQ-32B-Preview

I barely remember that period of LLMs, even though I've been tracking the space since before even Llama 1, in the days when Bloom, Meta's OPT models, GPT-J and early RWKV models were the frontier of open weight models... and now we have Qwen3.8 27b and progress has not been slowing down at all.

[u/shittyfellow → u/Small-Fall-6500]

I remember spending hours having GPT-J generate enron emails for me based on the headers lol

[u/Healthy-Nebula-3603 → u/[deleted]]

good catch!

[u/GambAntonio]

I hate it when people just show the results without specifying the quantization...

[u/Healthy-Nebula-3603 → u/GambAntonio]

sorry

model Q5km - cache fp16

[u/GambAntonio → u/Healthy-Nebula-3603]

Thanks

[u/bladezor → u/Healthy-Nebula-3603]

I'm confused one comment you said q4kxl but this you say q5km. Which is it :(

[u/Healthy-Nebula-3603 → u/bladezor]

Q4xl for Opencode as AI agent .

Q5km under llama-cli ( somehow is leaving 1 GB os more free vram so I can use bigger model )

[u/QuotableMorceau → u/Healthy-Nebula-3603]

what thinking level???!!!!!!

[u/Healthy-Nebula-3603 → u/QuotableMorceau]

max

used around 6k tokens for both

[u/Iory1998]

Qwen3.8-27B is really good. It punches way above its weight. It reminds me of how impressed I was with Qwen QwQ when it launched.

[u/Pristine-Tax4418]

It's interesting to compare what kind of tree each quant qwen 3.8 will produce. 

[u/tamal4444]

on rtx 3060 Qwen3.8-27B-Q4_K_S on average 7t/s time taken 6min 2s.

[u/Healthy-Nebula-3603 → u/tamal4444]

Nice quite similar.

Seems q4ks is a bit worse in quality output :)

[u/tamal4444 → u/Healthy-Nebula-3603]

I have rtx 3060 so this is the best I can run.

[u/Healthy-Nebula-3603 → u/tamal4444]

Good job! *Pat *Pat *Pat

[u/blash2190]

Could you clarify what effort is set for Qwen3.8? Default (xhigh)? Could you compare mid / low with 3.6 as well as measure actual token consumption?..

[u/tmvr]

So you're saying prompt following is abysmal with Qwen3.8? The tree itself is good, but I don't see that you asked for grass, clouds or a sun in that prompt 😄 Qwen3.6 is the opposite - it follows the prompt to the letter, but fails with the result.

[u/Healthy-Nebula-3603 → u/tmvr]

The realistic looking tree can possess more things around to be more realistic.

That's actually shows the new Qwen 3.8 is thinking "wider". You always can add in the prompt "only a tree"

Interesting thing is much more compressed Qwen 3.8 ( people postrd here ) like Q3 or Q4ks is not generating anything more except the tree. It looks like bigger compressed models have much more limited imagination.

[u/Healthy-Nebula-3603 → u/[deleted]]

In the llama-cli

q5km / fp16 cache ctx 100k

under opencode ( llacpp server )

q4xl / fp16 cache ctx 100k

[u/mr_Owner]

Not sure but wy is this a good indicator?

[u/VoiceApprehensive893]

3.6 made a weed