Qwen 3.5 397B is the best local coder I have used until now

A viral review from a developer testing numerous large language models has crowned Alibaba's Qwen 3.5 397B as the new benchmark for local coding assistance. The user, who tested models including GPT-OSS 120B, StepFun 3.5, MiniMax M2.5, and smaller Qwen variants, found the massive 397-billion-parameter model produced significantly more accurate and bug-free code on the first attempt. While acknowledging it's the slowest in raw token generation speed, the reviewer emphasized that this is offset by not needing multiple turns to fix errors or waiting for prolonged 'thinking' periods, a common issue with other models.

A key technical breakthrough enabling its local use is aggressive quantization. The user is running a version quantized to IQ2_XS by AesSedai, compressing the model down to just 123 gigabytes. This is a stark contrast to competitors like StepFun 3.5 and MiniMax M2.5, which require at least IQ4_XS quantization, or other Qwen siblings and rivals like Super Nemotron 120B that need the larger Q6_K method. This efficient compression makes the world-class model surprisingly accessible for developers with powerful local GPUs, challenging the notion that top-tier coding performance requires cloud-based APIs like GPT-4 or Claude.

The performance highlights a shift in the open-weight model landscape, where sheer scale (397B parameters), when paired with advanced quantization techniques, can create a locally-runnable model that competes on output quality. Its 'concise thinking' was specifically praised compared to its smaller siblings, suggesting better internal reasoning processes. This development is significant for developers prioritizing privacy, cost control, or offline access, providing a powerful alternative to cloud-based coding assistants.