Is Qwen3-Coder-30B-A3B-Instruct or WizardCoder-Python-34B-V1.0 smaller?

Qwen3-Coder-30B-A3B-Instruct is smaller at Q4_K_M: 17.28 GiB against 18.83 GiB.

Which uses less memory at long context?

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

Model comparison

Qwen3-Coder-30B-A3B-Instruct vs WizardCoder-Python-34B-V1.0

At Q4_K_M, Qwen3-Coder-30B-A3B-Instruct is the smaller download — 18,556,689,568 bytes against 20,219,911,424. At long context the gap widens: Qwen3-Coder-30B-A3B-Instruct'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

	Qwen3-Coder-30B-A3B-Instruct	WizardCoder-Python-34B-V1.0
Parameters	30.5B	33.7B
Architecture	qwen3moe	llama
Layers	48	48
Native context	262,144	16,384
Mixture of experts	yes, 128 experts	no
Quantizations published	46	47
Smallest quantization	7.46 GiB	6.75 GiB
Q4_K_M	17.28 GiB	18.83 GiB
Licence	apache-2.0	llama2

KV cache by context

the term that decides long-context viability

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