Is LFM2.5-Embedding-350M or jina-embeddings-v5-text-small smaller?

LFM2.5-Embedding-350M is smaller at Q4_K_M: 0.21 GiB against 0.37 GiB.

Which uses less memory at long context?

LFM2.5-Embedding-350M — its KV cache at 32K context is 9.3x smaller, because of how its attention layers cache.

Model comparison

LFM2.5-Embedding-350M vs jina-embeddings-v5-text-small

At Q4_K_M, LFM2.5-Embedding-350M is the smaller download — 229,311,232 bytes against 396,705,152. At long context the gap widens: LFM2.5-Embedding-350M's KV cache at 32K is 9.3× smaller, which usually matters more than the difference in weights.

From the file· summed bytes, KV per layer

Side by side

	LFM2.5-Embedding-350M	jina-embeddings-v5-text-small
Parameters	354M	596M
Architecture	lfm2	qwen3
Layers	16	28
Native context	128,000	32,768
Mixture of experts	no	no
Quantizations published	23	56
Smallest quantization	0.15 GiB	0.19 GiB
Q4_K_M	0.21 GiB	0.37 GiB
Licence	other	cc-by-nc-4.0

KV cache by context

the term that decides long-context viability

Context	LFM2.5-Embedding-350M	jina-embeddings-v5-text-small	Ratio
4,096	0.05 GiB	0.44 GiB	9.33×
8,192	0.09 GiB	0.88 GiB	9.33×
16,384	0.19 GiB	1.75 GiB	9.33×
32,768	0.38 GiB	3.50 GiB	9.33×
65,536	0.75 GiB	7.00 GiB	9.33×
131,072	1.50 GiB	14.00 GiB	9.33×