Methodology
Fixed environment: a single RTX 4090 (24GB), vLLM serving, 4-bit quantization. The 40-question suite covers Chinese marketing copy, Python/SQL generation, multi-step reasoning, and 3+ turn dialogues, blind-scored by three annotators on a 5-point scale.
Results (Representative Models)
| Model | Chinese Writing | Code | Reasoning | Overall |
|---|---|---|---|---|
| Qwen3-32B | 4.6 | 4.4 | 4.3 | 4.4 |
| DeepSeek-R1-Distill-32B | 4.1 | 4.6 | 4.7 | 4.5 |
| GLM-4-32B | 4.3 | 4.0 | 4.0 | 4.1 |
| Qwen3-14B | 4.2 | 3.9 | 3.8 | 4.0 |
| Llama-4-Scout (quantized) | 3.2 | 3.8 | 3.9 | 3.6 |
| InternLM3-8B | 3.8 | 3.4 | 3.3 | 3.5 |
Two counterintuitive findings. First, Qwen3-14B trails the 32B version by only 0.4 points in Chinese writing while running 2.1x faster, making it the value pick for support bots. Second, Llama 4's Chinese weakness is glaring in marketing copy and needs fine-tuning before any Chinese-facing use.
Deployment Cost Math
Monthly: 8B-class models on a rented 4090 (~1500 CNY) handle 50k calls per day; 32B-class on dual GPUs or an A100 (~4500 CNY) handles 20k high-quality calls. Below 10k calls per day, DeepSeek's official API (~2 CNY per million tokens) is usually cheaper.
Recommendations
- Support and content generation: Qwen3-14B — the best speed-quality balance;
- Coding assistants and data analysis: DeepSeek-R1-Distill-32B;
- Tight budgets: Qwen3-8B or InternLM3-8B on a consumer GPU;
- Overseas English scenarios: only then consider Llama 4.
Bottom line: the sweet spot for private deployment is 14B-32B models on Chinese workloads. Under 10k daily calls, just buy APIs; beyond 30k, self-hosting pays for the GPU within a month.