Is Phind-CodeLlama-34B-Python-v1 or Qwen3.6-27B smaller?

Qwen3.6-27B is smaller at Q4_K_M: 15.41 GiB against 18.83 GiB.

Which uses less memory at long context?

Qwen3.6-27B — its KV cache at 32K context is 3.0x smaller, because of how its attention layers cache.

Model comparison

Phind-CodeLlama-34B-Python-v1 vs Qwen3.6-27B

At Q4_K_M, Qwen3.6-27B is the smaller download — 16,547,397,888 bytes against 20,219,900,064. At long context the gap widens: Qwen3.6-27B's KV cache at 32K is 3.0× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	Phind-CodeLlama-34B-Python-v1	Qwen3.6-27B
Parameters	33.7B	27.8B
Architecture	llama	qwen35
Layers	48	64
Native context	16,384	262,144
Mixture of experts	no	no
Quantizations published	33	40
Smallest quantization	6.75 GiB	8.74 GiB
Q4_K_M	18.83 GiB	15.41 GiB
Licence	llama2	apache-2.0

KV cache by context

the term that decides long-context viability

Context	Phind-CodeLlama-34B-Python-v1	Qwen3.6-27B	Ratio
4,096	0.75 GiB	0.25 GiB	3.00×
8,192	1.50 GiB	0.50 GiB	3.00×
16,384	3.00 GiB	1.00 GiB	3.00×
32,768	6.00 GiB	2.00 GiB	3.00×
65,536	12.00 GiB	4.00 GiB	3.00×
131,072	24.00 GiB	8.00 GiB	3.00×