Is SmolVLM-256M-Instruct or Qwen3-0.6B smaller?

Both publish quantizations; compare the table for the exact sizes.

Which uses less memory at long context?

SmolVLM-256M-Instruct — its KV cache at 32K context is 5.0x smaller, because of how its attention layers cache.

Model comparison

SmolVLM-256M-Instruct vs Qwen3-0.6B

These two publish different quantization sets; the table below has the exact sizes. At long context the gap widens: SmolVLM-256M-Instruct's KV cache at 32K is 5.0× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	SmolVLM-256M-Instruct	Qwen3-0.6B
Parameters	256M	752M
Architecture	llama	qwen3
Layers	30	28
Native context	8,192	40,960
Mixture of experts	no	no
Quantizations published	2	44
Smallest quantization	0.16 GiB	0.20 GiB
Q4_K_M	—	0.37 GiB
Licence	apache-2.0	—

KV cache by context

the term that decides long-context viability

Context	SmolVLM-256M-Instruct	Qwen3-0.6B	Ratio
4,096	0.09 GiB	0.44 GiB	4.98×
8,192	0.18 GiB	0.88 GiB	4.98×
16,384	0.35 GiB	1.75 GiB	4.98×
32,768	0.70 GiB	3.50 GiB	4.98×
65,536	1.41 GiB	7.00 GiB	4.98×
131,072	2.81 GiB	14.00 GiB	4.98×