Translation note: This English version was translated by Codex (GPT-5) on 2026-04-20 18:01:46 CST. The source text is the corresponding Chinese post in this repository.
Written on 2026-03-30, 7:53 PM - 9:40 PM
Preface
In the first article, I covered the software ecosystem. In the AI era, models are no longer just tools; they are infrastructure. This piece is not just an introduction to models, but a question about how to build your own capability stack in a world controlled by different companies and different countries.
The Current Model Landscape
The third-party benchmarking site Artificial Analysis shows a useful vendor ranking:
The competition is no longer just about raw capability. It is becoming a battle over infrastructure control:
- US frontier labs define the upper limit
- Chinese models compete by balancing quality and cost
- Other companies extend the ecosystem through clouds, GPUs, or social platforms
First tier
The first tier is dominated by the major US players:
- Google Gemini
- Anthropic Claude
- OpenAI ChatGPT
They define the frontier of capability.
Second tier
Chinese companies focus more on “good enough + much cheaper”:
- DeepSeek
- Qwen
- Kimi
- MiniMax
- GLM
They win through cost effectiveness.
Other players
- xAI (Grok)
- NVIDIA (Nemotron)
- Amazon (Nova)
- Meta (Llama)
They expand through cloud, GPU, or platform ecosystems.
Coding Ability
Coding benchmarks show the same overall trend: US models still lead, while Chinese models remain highly competitive.
In real-world coding, Claude is still widely regarded as one of the strongest everyday experiences.
Subscription Models
AI models are powered by tokens, which are basically the electricity of the AI world.
API usage
Pay-as-you-go is like connecting a power line and paying for what you consume.
Subscription plans
Gemini, Claude, and ChatGPT all offer subscription products. The mainstream plans are around $20/month and include web access plus AI coding quota.
Coding plans
As token burn increased, domestic vendors introduced package-style coding plans. This reflects AI moving from “usage-based billing” to a more infrastructure-like model.
Relays and intermediaries
Because of export restrictions and regional availability issues, users sometimes rely on reputable intermediaries such as OpenRouter.
Conclusion
The core job of a developer is changing:
- understand model capability
- control cost structure
- design a sustainable model stack
In the AI era, the competitive advantage does not come from a single model. It comes from how you combine and use them.