guide
Getting Started with Chinese AI Models
A practical starting path for developers and enterprises evaluating Chinese model APIs, local deployment and cloud platforms.
Verdict
Start with API access for speed, use local deployment only when license and hardware requirements are clear, and move to hybrid architecture when sensitive data or latency justify the added complexity.
Ranking basis
This guide converts the supplied getting-started material into an implementation path for global developers and enterprise evaluators.
Developer quick start
Most teams should begin with hosted APIs because they make model comparison, latency testing and cost measurement faster.
Use API access for the first benchmark
Test DeepSeek, Qwen, Kimi or GLM with your own prompts and record quality, latency, refusal behavior and token cost.
Use local deployment for control
Evaluate llama.cpp, Ollama or vLLM only after checking model license, quantization quality and hardware budget.
Use cloud platforms for managed rollout
Alibaba Cloud, Baidu Qianfan and other China cloud paths are most useful when account, region and compliance requirements fit your team.
Model selection shortcuts
Use these as starting hypotheses, then verify with your own workload.
Start with DeepSeek for code and add GLM when autonomous coding-agent behavior is important.
Start with Qwen when multilingual breadth and model-family coverage matter.
Start with Kimi when the workload is long documents, research files or contracts.
Sources
Next actions
- - Create a 20-prompt evaluation set from your real workload before comparing vendors.
- - Record region, billing method, latency, output quality and data-handling terms for each tested model.