Is Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled or Llama-3.2-1B-Instruct smaller?

Llama-3.2-1B-Instruct is smaller at Q4_K_M: 0.75 GiB against 45.16 GiB.

Which uses less memory at long context?

Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled — its KV cache at 32K context is 1.3x smaller, because of how its attention layers cache.

Model comparison

Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled vs Llama-3.2-1B-Instruct

At Q4_K_M, Llama-3.2-1B-Instruct is the smaller download — 807,690,688 bytes against 48,489,047,936. At long context the gap widens: Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled's KV cache at 32K is 1.3× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled	Llama-3.2-1B-Instruct
Parameters	—	1.2B
Architecture	qwen3next	llama
Layers	48	16
Native context	262,144	131,072
Mixture of experts	yes, 512 experts	no
Quantizations published	21	39
Smallest quantization	0.38 GiB	0.39 GiB
Q4_K_M	45.16 GiB	0.75 GiB
Licence	apache-2.0	—

KV cache by context

the term that decides long-context viability

Context	Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled	Llama-3.2-1B-Instruct	Ratio
4,096	0.09 GiB	0.13 GiB	1.33×
8,192	0.19 GiB	0.25 GiB	1.33×
16,384	0.38 GiB	0.50 GiB	1.33×
32,768	0.75 GiB	1.00 GiB	1.33×
65,536	1.50 GiB	2.00 GiB	1.33×
131,072	3.00 GiB	4.00 GiB	1.33×