Is Codestral-22B-v0.1 or Qwen3-30B-A3B-Thinking-2507 smaller?

Codestral-22B-v0.1 is smaller at Q4_K_M: 12.42 GiB against 17.28 GiB.

Which uses less memory at long context?

Qwen3-30B-A3B-Thinking-2507 — its KV cache at 32K context is 2.3x smaller, because of how its attention layers cache.

Model comparison

Codestral-22B-v0.1 vs Qwen3-30B-A3B-Thinking-2507

At Q4_K_M, Codestral-22B-v0.1 is the smaller download — 13,341,237,600 bytes against 18,556,685,824. At long context the gap widens: Qwen3-30B-A3B-Thinking-2507'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

	Codestral-22B-v0.1	Qwen3-30B-A3B-Thinking-2507
Parameters	22.2B	30.5B
Architecture	llama	qwen3moe
Layers	56	48
Native context	32,768	262,144
Mixture of experts	no	yes, 128 experts
Quantizations published	50	51
Smallest quantization	4.50 GiB	7.05 GiB
Q4_K_M	12.42 GiB	17.28 GiB
Licence	other	apache-2.0

KV cache by context

the term that decides long-context viability

Context	Codestral-22B-v0.1	Qwen3-30B-A3B-Thinking-2507	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×