Qwen3.5 anchors 128GB local coding debate

The real story is not a single “best model” but how close local open-weight stacks have gotten to being usable daily drivers for coding-heavy workflows.

• –Qwen’s official Qwen3.5 release positions the family for long-context local serving with vLLM, SGLang, and agent tooling, which matches the thread’s homelab setup unusually well
• –Community sentiment around Qwen3.5 is strong for local coding, but separate discussion on Hacker News also pushes back on “Sonnet-level” hype and says real-world agentic work still exposes gaps
• –Alternatives like StepFun, GLM, Kimi, and MiniMax keep coming up in broader community comparisons, but they tend to trade off speed, tool-use reliability, cost, or practical local fit
• –For this kind of workload, harness quality matters a lot: multiple users report that prompt templates, reasoning settings, and tool-call behavior can swing results as much as model choice
• –The thread is a useful snapshot of where local AI stands in 2026: 128GB VRAM is enough for serious experimentation, but not enough to erase the gap between “best open-weight” and “best frontier API”