Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-oQ6-fp16-mtp

This model was quantized using oQ (oMLX v0.3.12) mixed-precision quantization.

Quantization details

• Model type: qwen3_5_moe
• Bits: 6
• 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

Tag	Model	Source
M1	Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-oQ6e-fp16	splats/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-oQ6e — oQ6e enhanced quantization of lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled
M2	Qwen3.6-35B-A3B-oQ6-fp16-mtp	Independently quantized with oMLX oQ from Qwen/Qwen3.6-35B-A3B
M3	Qwen3.6-35B-A3B-oQ5-fp16-mtp	Independently quantized with oMLX oQ from Qwen/Qwen3.6-35B-A3B
M4	Qwen3.6-35B-A3B-oQ4-fp16-mtp	Independently quantized with oMLX oQ from Qwen/Qwen3.6-35B-A3B
M5	Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-oQ4-fp16-mtp	Independently quantized with oMLX oQ from lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled

Accuracy

Bold = highest score for that benchmark across all models. Format: accuracy% (correct).

Benchmark	Total	M1 Claude oQ6e	M2 base oQ6	M3 base oQ5	M4 base oQ4	M5 Claude oQ4
MMLU	1000	86.9% (869)	90.0% (900)	82.4% (824)	79.9% (799)	66.3% (663)
MMLU PRO	300	83.3% (250)	83.0% (249)	69.7% (209)	68.7% (206)	59.0% (177)
HellaSwag	200	92.5% (185)	94.0% (188)	87.5% (175)	87.0% (174)	81.0% (162)
TruthfulQA	817	85.2% (696)	90.1% (736)	82.1% (671)	81.6% (667)	66.7% (545)
ARC Challenge	300	97.0% (291)	97.0% (291)	86.0% (258)	85.7% (257)	73.3% (220)
WinoGrande	300	84.3% (253)	78.3% (235)	86.0% (258)	80.7% (242)	60.0% (180)
GSM8K	100	95.0% (95)	85.0% (85)	86.0% (86)	85.0% (85)	73.0% (73)
MathQA	300	91.3% (274)	77.3% (232)	82.3% (247)	76.0% (228)	67.7% (203)
HumanEval	164	84.8% (139)	82.9% (136)	82.9% (136)	80.5% (132)	69.5% (114)
MBPP	200	78.0% (156)	81.5% (163)	84.0% (168)	82.0% (164)	68.0% (136)
LiveCodeBench	100	45.0% (45)	42.0% (42)	48.0% (48)	46.0% (46)	34.0% (34)
BBQ	300	96.3% (289)	87.0% (261)	88.3% (265)	87.0% (261)	85.7% (257)
SafetyBench	300	85.0% (255)	80.7% (242)	81.3% (244)	80.7% (242)	75.0% (225)

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."

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Benchmarks run independently; not affiliated with any model author. Results reflect a single hardware configuration and may differ on other setups.

Citation

If you use these quantizations, please cite the base model and the distillation:

@misc{qwen36_a3b_2026,
  title  = {Qwen3.6-35B-A3B},
  author = {Qwen Team},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/Qwen/Qwen3.6-35B-A3B}},
}

@misc{lordx64_qwen36_distill_2026,
  title  = {Qwen3.6-35B-A3B distilled from Claude Opus 4.7 reasoning},
  author = {lordx64},
  year   = {2026},
  howpublished = {\url{https://huggingface.co/lordx64/Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled}},
}