Is dolphin-2.9.1-mixtral-1x22b or Qwen3-Coder-30B-A3B-Instruct smaller?

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

Which uses less memory at long context?

Qwen3-Coder-30B-A3B-Instruct — its KV cache at 32K context is 2.3x smaller, because of how its attention layers cache.

Model comparison

dolphin-2.9.1-mixtral-1x22b vs Qwen3-Coder-30B-A3B-Instruct

These two publish different quantization sets; the table below has the exact sizes. At long context the gap widens: Qwen3-Coder-30B-A3B-Instruct's KV cache at 32K is 2.3× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	dolphin-2.9.1-mixtral-1x22b	Qwen3-Coder-30B-A3B-Instruct
Parameters	22.2B	30.5B
Architecture	llama	qwen3moe
Layers	56	48
Native context	65,536	262,144
Mixture of experts	yes, 1 experts	yes, 128 experts
Quantizations published	45	46
Smallest quantization	4.49 GiB	7.46 GiB
Q4_K_M	—	17.28 GiB
Licence	apache-2.0	apache-2.0

KV cache by context

the term that decides long-context viability

Context	dolphin-2.9.1-mixtral-1x22b	Qwen3-Coder-30B-A3B-Instruct	Ratio
4,096	0.88 GiB	0.38 GiB	2.33×
8,192	1.75 GiB	0.75 GiB	2.33×
16,384	3.50 GiB	1.50 GiB	2.33×
32,768	7.00 GiB	3.00 GiB	2.33×
65,536	14.00 GiB	6.00 GiB	2.33×
131,072	28.00 GiB	12.00 GiB	2.33×