I ran a quantization shootout on Qwen3-Coder and the results are… interesting

I ran a quantization shootout on Qwen3-Coder-Next and the results are… interesting

Key Takeaways

• UD-Q5_K_M is significantly better in quality than other tested formats, even though it’s only slightly larger.
• For interactive coding tasks, UD-Q5_K_M performs as well or better in terms of speed compared to MXFP4.
• The trade-off between model size and performance is evident; smaller quantized models like Q4_K_M outperform larger ones like MXFP4 for certain tasks.

What quants are you guys running for code models? Are you finding the same quality cliff with aggressive compression?

“For me, I swapped my default from MXFP4 to UD-Q5_K_M. MXFP4 is still great for heavy prefill workloads but for daily code generation where you care about quality over speed, UD-Q5 is the clear winner.”

For those on Nvidia hardware, are you seeing different tradeoffs than RDNA?

Stay ahead of AI. Get the most important stories delivered to your inbox — no spam, no noise.

Please enable JavaScript in your browser to complete this form.

Email Name

Name *

First

Last

Email *

Submit