Is Qwen3-Coder-30B-A3B-Instruct-RTPurbo or Qwen3-30B-A3B-Thinking-2507 smaller?

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

Which uses less memory at long context?

Qwen3-30B-A3B-Thinking-2507 — its KV cache at 32K context is 1.0x smaller, because of how its attention layers cache.

Model comparison

These two publish different quantization sets; the table below has the exact sizes.

From the file· summed bytes, KV per layer

	Qwen3-Coder-30B-A3B-Instruct-RTPurbo	Qwen3-30B-A3B-Thinking-2507
Parameters	30.5B	30.5B
Architecture	qwen3moe	qwen3moe
Layers	48	48
Native context	262,144	262,144
Mixture of experts	yes, 128 experts	yes, 128 experts
Quantizations published	23	51
Smallest quantization	5.98 GiB	7.05 GiB
Q4_K_M	—	17.28 GiB
Licence	—	apache-2.0

the term that decides long-context viability

Context	Qwen3-Coder-30B-A3B-Instruct-RTPurbo	Qwen3-30B-A3B-Thinking-2507	Ratio
4,096	0.38 GiB	0.38 GiB	1.00×
8,192	0.75 GiB	0.75 GiB	1.00×
16,384	1.50 GiB	1.50 GiB	1.00×
32,768	3.00 GiB	3.00 GiB	1.00×
65,536	6.00 GiB	6.00 GiB	1.00×
131,072	12.00 GiB	12.00 GiB	1.00×