Is GigaChat3-10B-A1.8B-base or Qwen3.8-27B smaller?

GigaChat3-10B-A1.8B-base is smaller at Q4_K_M: 6.03 GiB against 15.66 GiB.

Which uses less memory at long context?

GigaChat3-10B-A1.8B-base — its KV cache at 32K context is 2.2x smaller, because of how its attention layers cache.

Model comparison

GigaChat3-10B-A1.8B-base vs Qwen3.8-27B

At Q4_K_M, GigaChat3-10B-A1.8B-base is the smaller download — 6,474,702,976 bytes against 16,810,714,336. At long context the gap widens: GigaChat3-10B-A1.8B-base's KV cache at 32K is 2.2× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	GigaChat3-10B-A1.8B-base	Qwen3.8-27B
Parameters	11.5B	27.8B
Architecture	deepseek2	qwen35
Layers	26	64
Native context	262,144	262,144
Mixture of experts	yes, 64 experts	no
Quantizations published	8	22
Smallest quantization	6.03 GiB	8.39 GiB
Q4_K_M	6.03 GiB	15.66 GiB
Licence	mit	apache-2.0

KV cache by context

the term that decides long-context viability

Context	GigaChat3-10B-A1.8B-base	Qwen3.8-27B	Ratio
4,096	0.11 GiB	0.25 GiB	2.19×
8,192	0.23 GiB	0.50 GiB	2.19×
16,384	0.46 GiB	1.00 GiB	2.19×
32,768	0.91 GiB	2.00 GiB	2.19×
65,536	1.83 GiB	4.00 GiB	2.19×
131,072	3.66 GiB	8.00 GiB	2.19×