Unsloth AI presents performance benchmarks for Qwen3.6-35B-A3B GGUF quantizations, claiming state-of-the-art results in mean KL divergence across most model sizes. The discussion includes community analysis regarding SWE-bench Verified performance, where some users noted unexpected discrepancies between Qwen3.5 and Qwen3.6 quantization results during coding tasks.
Key points:
- Unsloth ranks first in 21 of 22 model sizes for mean KL divergence.
- Community debate over SWE-bench testing methodology and sample sizes.
- Reported performance variations between different quantization levels (Q4, Q5, Q6, Q8).
- Discussion on system prompt adherence and error rates in coding benchmarks.
Key points:
- Unsloth ranks first in 21 of 22 model sizes for mean KL divergence.
- Community debate over SWE-bench testing methodology and sample sizes.
- Reported performance variations between different quantization levels (Q4, Q5, Q6, Q8).
- Discussion on system prompt adherence and error rates in coding benchmarks.
This article details benchmarks for Unsloth Dynamic GGUFs of the Qwen3.5 model, including analysis of perplexity, KL divergence, and MXFP4. It covers performance across different bit widths and quant types, highlighting the impact of Imatrix and the limitations of certain quantization approaches. Full benchmark data is also provided.
