The Qwen 3.6 35B A3B hype is real!!!

My personal test for small local LLM intelligence is to check whether a model has any ability to understand the code that I write for my own academic research. My research is on some pretty niche topics and I doubt that anything like it is substantively present in the training sets for LLMs. A few months ago, small local models' ability to understand my code was nominal at best with [Devstral Small 2 being the top performer](https://www.reddit.com/r/LocalLLaMA/comments/1ry93gz/devstral_small_2_24b_severely_underrated/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button). However, several small open weight models now have methods of accommodating fairly **long contexts** (gated delta net, hybrid Mamba2, sliding window attention) which makes them ***extremely*** **smarter**. I can now feed a model an entire academic paper along with accompanying code and ask it to use the paper to work out what the code is doing. I just spent a couple days experimenting with: * Qwen 3.6 35B A3B * Qwen 3.6 27B * Gemma 4 26B A4B * Nemotron 3 Nano **All** of them were able to comprehend my code significantly better than what any *small* local model could do a few months ago. I did try Devstral Small 2 since I recently went from a single 16GB graphics card to two; however, I simply couldn't fit the long context in 32GB of ram. I hope Mistral releases a new small model with a gated delta net, because I think it could take the throne. [These are my detailed findings](https://github.com/nathanlgabriel/paper_code_mapping_assessment/blob/main/README.md) from asking local models to explain how my code maps to the research paper it corresponds to. TLDR: All four models listed above are incredibly capable local models, with Qwen 3.6 35B A3B standing out as the best. I'm also inclined to think that an intelligent human with *any* of these four models is more capable than something like Opus 4.7 on its own (see the detailed findings). Please let me know your thoughts!