Local LLMs Show Strong Code Comprehension with Long‑Context Models

Local LLM enthusiasts have benchmarked several small open‑weight models for code comprehension. The author tested Qwen 3.6 35B A3B, Qwen 3.6 27B, Gemma 4 26B A4B, and Nemotron 3 Nano, all of which now support long‑context techniques such as gated delta nets, hybrid Mamba‑2, and sliding‑window attention. Compared to earlier models like Devstral Small 2, these four can ingest an entire academic paper and accompanying code, producing accurate explanations of the code’s purpose. Qwen 3.6 35B A3B emerged as the strongest performer, outperforming the others in both speed and depth of understanding. The author notes that Devstral Small 2 could not fit the long context even with 32 GB RAM, highlighting the hardware demands of newer architectures. The detailed findings are available on GitHub, offering a reproducible assessment framework. The post suggests that future small models, such as a potential Mistral release with gated delta nets, could further improve performance. Overall, the article demonstrates a clear shift toward more capable local LLMs for code‑centric tasks.