A much more effective way to use models is to have them drive a deterministic tool like a calendar instead. Also, Qwen 3.8 is complete overkill for a task like that, you're just wasting energy for no reason here. A much smaller model trained on agentic tool use would both work more reliably and use way less power.

I did make it use getTime every time but when I tell it to make a calendar entry for the "next Tuesday" for example it would be off by at least a week. Sometimes even making the entry in the past.

I'd agree with you on it being overkill, but I wrote SK many scripts and automations involving LLM tasks that rely on a capable model to determine the flow of a task that I heavily prefer the 27ban models

currently running gemma4 and for my use case it performs much better than qwen3.8

I find what the model was RL trained on is really important. It looks like Qwen 3.8 is mainly focused on agentic coding, so it does really well there. But once you throw it at tasks outside the training then things start to fall apart fast.

im not bothered by it.
but also.. i wouldn't use it as my sole model to perform every task.
[waves hand toward huggingface.] ( a gazillion bajillion models, specialized for all types of tasks).

edit: i checked. and there aren't as many models as i expected, for this task(didnt test them). and.. small multimodal model failed badly.
it's a strange benchmark. but it seems reasonable to expect LLMs to be able to do this.

What was your prompt for the iguana?

i dont want to share it. but it was easy to add a little more description for basic criteria in the request. although, i did not detail a step-by-step process for drawing.
i had this idea to feed it an actual image, to base the drawing on. but i wasnt able to. llama.cpp seems to be messed up[esp vulkan pkg](and also i probably dont know the proper way to utilize features(if they even exist for a mode))

Here's the result by Qwen3.8-27b Q8 for "Create an SVG of an Iguana.":

For your image with "Create an SVG of an Iguana based on the provided image.":

(Using llama.cpp with Opencode)

You can run it on smaller hardware too, people managed to get 3.6 to run with 16 gb ram with stuff like quants and MTP. But the fact that you can run a model that performs as well as a frontier model from half a year ago on any laptop is frankly mind blowing.

I've got it running on 12gb at lowest quant at a decent speed, like 30toks