Model comparison
Qwen3.5-9B-DeepSeek-3.2-Intense-Auto-Variable-Thinking vs HyperCLOVAX-SEED-Text-Instruct-1.5B
These two publish different quantization sets; the table below has the exact sizes.
From the file· summed bytes, KV per layer
Side by side
| Qwen3.5-9B-DeepSeek-3.2-Intense-Auto-Variable-Thinking | HyperCLOVAX-SEED-Text-Instruct-1.5B | |
|---|---|---|
| Parameters | 9.0B | 1.6B |
| Architecture | qwen35 | llama |
| Layers | — | — |
| Native context | — | — |
| Mixture of experts | no | no |
| Quantizations published | 24 | 1 |
| Smallest quantization | 2.28 GiB | 1.06 GiB |
| Q4_K_M | — | 1.06 GiB |
| Licence | apache-2.0 | other |
KV cache by context
the term that decides long-context viability
| Context | Qwen3.5-9B-DeepSeek-3.2-Intense-Auto-Variable-Thinking | HyperCLOVAX-SEED-Text-Instruct-1.5B | Ratio |
|---|---|---|---|
| 4,096 | — | — | — |
| 8,192 | — | — | — |
| 16,384 | — | — | — |
| 32,768 | — | — | — |
| 65,536 | — | — | — |
| 131,072 | — | — | — |