Qwen3.6-27B crushes Inkling 276B in 38s coding test
10x smaller model plans, writes, and self-reviews code flawlessly.
A Reddit user u/lilian_moraru ran a head-to-head test inspired by an Unsloth thread on Kimi K3 1-bit quantization. They compared Inkling-Small-276B-12B, a massive MoE quantized to UD-Q2_K_XL, running with reasoning effort "max" (above "xhigh"), against Alibaba's Qwen3.6-27B. Both models ran on NVIDIA's DGX Spark GB10 and received the exact same prompt. Inkling thought for 6 minutes before producing "lots of hacky code" and a short summary, whereas Qwen3.6-27B spent only 38 seconds thinking, designed an architecture plan, implemented classes with an update method, then methodically verified HTML tags, JavaScript brackets, and feature completeness. Qwen even caught a requested feature implemented under a different name, fixed issues, and printed a final report with file path and size. The comparison comes as quantization adoption grows, but shows severe quality loss when shrinking a 276B model to 2-bit precision.
- Qwen3.6-27B completed in 38 seconds vs 6 minutes for Inkling-Small-276B-12B
- Inkling used UD-Q2_K_XL (2-bit) quantization; Qwen ran dense at 27B
- Qwen self-checked syntax, features, and code structure before delivering
Why It Matters
Shows aggressive quantization cripples huge models—smaller dense models with strong reasoning can win on real tasks.