I've been using Qwen-3.6-35B-A3B on a spark for my local coding adventures. It's really quite good and getting faster (running the PR before it gets merged is already a 2x token gen speedup)

I cannot imaging spending the prices for Opus anymore, the little guys are getting good enough and in another year I expect to be even better.

Smaller models might not make the best agentic coding assistants, but I have a 128GB RAM headless machine serving llama.cpp with a number of local models that handles various tasks on a daily basis and works great.

- Qwen3-VL:30b > A file watcher on my NAS sends new images to it, which autocaptions and adds the text descriptions as a hidden EXIF layer into the image along with an entry into a Qdrant vector database for lossy searching and organization.

- Gemma3:27b > Used for personal translation work (mostly English and Chinese). Haven't had a chance to try out the Gemma4 models yet.

- Llama3.1:8b > Performs sentiment analysis on texts / comments / etc.

In my country we write longer and more detailed texts in primary school. That's blogpostmaxxing.

Comparing to Opus is a little unfair, a comparison against Haiku would be more fair. And for a really fast cloud model, I'd be interested to see how latency stacks up against GPT-5.3 Instant or Gemini 3.1 Flash Lite.

This whatever '-maxxxing' nonsense is really 'cringemaxxing'.

The people using it need to look at the origins of it first before using it everywhere.