Why Open Models Matter to OPCs
For a one-person company, open models are not about being free — they deliver three things: client data never leaves your network, costs are predictable, and model behavior is customizable. When delivering on-premise work or handling sensitive data, closed APIs are often disqualified outright.
By 2026 the open-source landscape has consolidated into three camps: Alibaba's Qwen, DeepSeek, and Meta's Llama.
The Three Camps Compared
| Dimension | Qwen3 | DeepSeek-V3.2 | Llama 4 |
|---|---|---|---|
| License | Apache 2.0, commercial-safe | MIT, most permissive | Community license with MAU clauses |
| Chinese capability | Top tier | Strong, best reasoning | Weak, needs fine-tuning |
| Code and reasoning | Strong | Top tier | Medium |
| Single-GPU variants | 8B/14B/32B full range | Distilled versions fit | Scout needs quantization |
| Ecosystem | Richest | Fast growing | Strongest overseas |
Choosing by Scenario
- Chinese support, writing, knowledge-base QA: Qwen3 first — best Chinese corpus and tool-calling support;
- Code generation and complex reasoning: DeepSeek, with the strongest reasoning chains;
- Overseas clients needing a Western compliance story: Llama 4, with the strongest overseas ecosystem;
- Edge and low-cost deployment: quantized Qwen3-8B runs smoothly on a single 4090.
Deployment Cost Math
Renting beats buying for OPCs: a cloud RTX 4090 (24GB) instance costs about 1200-1800 CNY per month and runs 8B-14B quantized models, covering up to 50k calls per day. A 32B model needs an A100 or dual 4090s, starting at 4000 CNY per month. Equivalent closed-API usage costs 2000-8000 CNY monthly — self-hosting wins once you pass roughly 30k calls per day.
Bottom line: default to Qwen for Chinese workloads, DeepSeek for reasoning-heavy tasks, Llama for overseas compliance. Start on pay-per-call APIs, and only self-host after you cross 30k daily calls.