Is WizardCoder-Python-34B-V1.0 or Qwen3-30B-A3B-Thinking-2507 smaller?

Qwen3-30B-A3B-Thinking-2507 is smaller at Q4_K_M: 17.28 GiB against 18.83 GiB.

Which uses less memory at long context?

Qwen3-30B-A3B-Thinking-2507 — its KV cache at 32K context is 2.0x smaller, because of how its attention layers cache.

Model comparison

WizardCoder-Python-34B-V1.0 vs Qwen3-30B-A3B-Thinking-2507

At Q4_K_M, Qwen3-30B-A3B-Thinking-2507 is the smaller download — 18,556,685,824 bytes against 20,219,911,424. At long context the gap widens: Qwen3-30B-A3B-Thinking-2507's KV cache at 32K is 2.0× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	WizardCoder-Python-34B-V1.0	Qwen3-30B-A3B-Thinking-2507
Parameters	33.7B	30.5B
Architecture	llama	qwen3moe
Layers	48	48
Native context	16,384	262,144
Mixture of experts	no	yes, 128 experts
Quantizations published	47	51
Smallest quantization	6.75 GiB	7.05 GiB
Q4_K_M	18.83 GiB	17.28 GiB
Licence	llama2	apache-2.0

KV cache by context

the term that decides long-context viability

Context	WizardCoder-Python-34B-V1.0	Qwen3-30B-A3B-Thinking-2507	Ratio
4,096	0.75 GiB	0.38 GiB	2.00×
8,192	1.50 GiB	0.75 GiB	2.00×
16,384	3.00 GiB	1.50 GiB	2.00×
32,768	6.00 GiB	3.00 GiB	2.00×
65,536	12.00 GiB	6.00 GiB	2.00×
131,072	24.00 GiB	12.00 GiB	2.00×