Is Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled or embeddinggemma-300m smaller?

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

Which uses less memory at long context?

embeddinggemma-300m — its KV cache at 32K context is 5.2x smaller, because of how its attention layers cache.

Model comparison

Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled vs embeddinggemma-300m

These two publish different quantization sets; the table below has the exact sizes. At long context the gap widens: embeddinggemma-300m's KV cache at 32K is 5.2× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled	embeddinggemma-300m
Parameters	—	303M
Architecture	qwen3next	gemma-embedding
Layers	48	24
Native context	262,144	2,048
Mixture of experts	yes, 512 experts	no
Quantizations published	21	10
Smallest quantization	0.38 GiB	0.26 GiB
Q4_K_M	45.16 GiB	—
Licence	apache-2.0	—

KV cache by context

the term that decides long-context viability

Context	Qwen3-Coder-Next-Opus-4.6-Reasoning-Distilled	embeddinggemma-300m	Ratio
4,096	0.09 GiB	0.04 GiB	2.67×
8,192	0.19 GiB	0.05 GiB	3.69×
16,384	0.38 GiB	0.08 GiB	4.57×
32,768	0.75 GiB	0.14 GiB	5.19×
65,536	1.50 GiB	0.27 GiB	5.57×
131,072	3.00 GiB	0.52 GiB	5.77×