Is deepseek-coder-5.7bmqa-base or Qwen3-8B smaller?

deepseek-coder-5.7bmqa-base is smaller at Q4_K_M: 3.20 GiB against 4.68 GiB.

Which uses less memory at long context?

deepseek-coder-5.7bmqa-base — its KV cache at 32K context is 9.0x smaller, because of how its attention layers cache.

Model comparison

deepseek-coder-5.7bmqa-base vs Qwen3-8B

At Q4_K_M, deepseek-coder-5.7bmqa-base is the smaller download — 3,430,867,168 bytes against 5,027,783,488. At long context the gap widens: deepseek-coder-5.7bmqa-base's KV cache at 32K is 9.0× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	deepseek-coder-5.7bmqa-base	Qwen3-8B
Parameters	5.7B	8.2B
Architecture	llama	qwen3
Layers	32	36
Native context	16,384	40,960
Mixture of experts	no	no
Quantizations published	14	51
Smallest quantization	2.00 GiB	2.12 GiB
Q4_K_M	3.20 GiB	4.68 GiB
Licence	other	apache-2.0

KV cache by context

the term that decides long-context viability

Context	deepseek-coder-5.7bmqa-base	Qwen3-8B	Ratio
4,096	0.06 GiB	0.56 GiB	9.00×
8,192	0.13 GiB	1.13 GiB	9.00×
16,384	0.25 GiB	2.25 GiB	9.00×
32,768	0.50 GiB	4.50 GiB	9.00×
65,536	1.00 GiB	9.00 GiB	9.00×
131,072	2.00 GiB	18.00 GiB	9.00×