Is WeirdDolphinPersonalityMechanism-Mistral-24B 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 1.7x smaller, because of how its attention layers cache.

Model comparison

WeirdDolphinPersonalityMechanism-Mistral-24B 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 1.7× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	WeirdDolphinPersonalityMechanism-Mistral-24B	Qwen3-Coder-30B-A3B-Instruct
Parameters	23.6B	30.5B
Architecture	llama	qwen3moe
Layers	40	48
Native context	32,768	262,144
Mixture of experts	no	yes, 128 experts
Quantizations published	23	46
Smallest quantization	4.91 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	WeirdDolphinPersonalityMechanism-Mistral-24B	Qwen3-Coder-30B-A3B-Instruct	Ratio
4,096	0.63 GiB	0.38 GiB	1.67×
8,192	1.25 GiB	0.75 GiB	1.67×
16,384	2.50 GiB	1.50 GiB	1.67×
32,768	5.00 GiB	3.00 GiB	1.67×
65,536	10.00 GiB	6.00 GiB	1.67×
131,072	20.00 GiB	12.00 GiB	1.67×