[!IMPORTANT] This quantization was uploaded on 2026-05-20 and replaces a previous version. If you downloaded this model before this date, please re-download for the updated weights.

Qwen3.6-35B-A3B-oQ5-fp16-mtp

Quantization details

• Model type: qwen3_5_moe
• Bits: 5
• Group size: 64
• Format: MLX safetensors

Community Benchmark Results

Benchmarks run independently on local hardware using the oMLX inference server (Apple Silicon, MLX backend), all models with think = yes. Timing data is omitted — inference was subject to swap memory pressure during these runs and is not a reliable proxy for reasoning depth or token budget. Accuracy is the signal. Note: official accuracy benchmarks for the Claude-distilled base model were still marked as pending on the model card at time of writing. These runs represent some of the first independently verified results.

Models

Accuracy

Notes

• oQ6e distillation is worth it for math and reasoning. M1 leads on 7 of 13 benchmarks, with the clearest gains on GSM8K (+10pp), MathQA (+9pp), and BBQ (+8pp). MMLU and TruthfulQA are the exceptions, where the base oQ6 edges ahead.
• oQ4 distillation is not recommended. M5 scores lower than the base oQ4 on every benchmark without exception — an average drop of −10.8pp across all 13 tasks. The regression is not limited to coding; WinoGrande (commonsense reasoning) drops the most at −20.7pp. The distilled weights do not appear to survive 4-bit compression intact. The only near-parity is BBQ (−1.3pp), suggesting safety-adjacent behaviour is the most compression-resilient aspect of the distillation.
• Coding is the weakest area for the distilled model at any bit level. Both M1 and M5 underperform on MBPP and LiveCodeBench relative to base models at the same or lower bit level. This is consistent with a known limitation noted by the base model author: the distillation used attention-only LoRA (0.01% of parameters), leaving the MoE expert FFNs unchanged — "distillation transfers how to reason, not new facts."

Citation