How to Choose a Chinese LLM: From Inference Services to Production Deployment

Starting with the “Model Marketplace”: How Inference-as-a-Service Changed Model Selection

Over the past two years, the way developers use large language models has changed significantly. Early on, developers had to download weights, configure inference environments, and manage GPU memory and concurrency themselves—the engineering barrier was high. Today, more and more models are offered as inference services: you don’t need to worry about how a model is deployed or how tensor parallelism works across multiple GPUs. You simply send text to an API endpoint, receive output, and pay per token.

This model is often described as a “model marketplace”: the shelves hold models with different capabilities and positioning, and developers take what they need. In the past, those shelves were mainly stocked with overseas open-source families such as Llama, Mistral, and Gemma. But over the past six months, Chinese models have appeared with increasing density. Flagship models such as GLM-5 and Kimi K2.5 not only perform well on authoritative leaderboards (such as the WebDev Leaderboard) but have also quietly made their way onto NVIDIA’s NIM (NVIDIA Inference Microservices) inference platform. In other words, users can already call these Chinese models through NVIDIA’s API services—there has been no large-scale announcement, but the endpoints are indeed open.

The signal worth noting here is that Chinese LLMs have reached a capability level that mainstream international inference infrastructure is willing to accept. For a technical decision-maker, the real question is no longer “are Chinese models good enough” but “given so many options, how do I choose.”

The Core Dimension of Selection: Task-Capability Fit, Not Rankings

Many people fall into “leaderboard worship” when selecting models, but in practice there is often a large gap between leaderboard rank and business performance. Leaderboards evaluate average capability on general tasks, while your business scenarios tend to be highly domain-specific. A model that handles general Q&A with ease may perform poorly on code completion, structured data extraction, or long-document understanding.

The right approach, therefore, is to classify tasks first. LLM application scenarios can generally be divided into the following categories:

• General dialogue and content generation: requires broad common-sense coverage, fluent language expression, and strong instruction-following.
• Code generation and program understanding: requires deep training on mainstream programming languages and the ability to handle algorithm logic, API calls, code review, and similar tasks.
• Reasoning and logical analysis: covers math problems, logical inference, multi-step reasoning, and requires strong chain-of-thought capability.
• Long-text understanding and knowledge Q&A: involves papers, contracts, reports, and other long documents, requiring a large context window and precise information localization.
• Structured output and tool calling: requires reliable generation of formatted data such as JSON and correct invocation of external tools or functions.

Each model has its strengths across these dimensions. Among the current top tier of Chinese models, some stand out in code capability, some are more mature in Chinese understanding and generation, and some have unique advantages in reasoning depth. The first step in selection is to clarify which category your core task belongs to, then evaluate candidate models accordingly.

Evaluation Methodology: Build Your Own “Minimal Viable Test Set”

Drawing on proven practices, I recommend that every team build its own “minimal viable test set” rather than relying entirely on public leaderboards. The approach is as follows:

1. Extract 20–30 typical questions from your business. The answers to these questions should be known and verifiable internally, not open-ended.
2. Group the questions by the five scenario categories above. Ensure at least five questions per category.
3. Design unified evaluation dimensions: answer accuracy, logical consistency, response speed, and output format compliance.
4. Blind-test each model: shuffle model outputs, have internal experts score them, and the model with the highest average score is the best fit for your current task stack.

This test set doesn’t need to be complete from day one—it can be expanded as the business evolves. But if you keep at it, your judgment of model capabilities will become increasingly accurate, rather than being confused by marketing language.

Key Engineering Practices: Inference Service Is Model-Agnostic, but API Design Determines Developer Experience

Once model capability basically meets requirements, the real differences show up in engineering details. Different models on the same platform, or even the same model accessed in different ways, can offer vastly different developer experiences. The following recommendations are worth attention:

1. The “Practical Usability” of Context Length Matters More Than the “Advertised Value”

Many models claim to support context windows of 100K or even 200K, but in practice performance degrades significantly beyond a certain length. What you need to test is whether, at 80% of maximum length, the model can still accurately extract key information from earlier content. This is an easily overlooked detail with far-reaching impact.

2. Reliable Structured Output Is the Foundation of Agent Applications

If you’re building automated agents, the stability of a model’s output format matters far more than the literary quality of its generated content. A model that frequently adds an extra comma in JSON or writes booleans as strings will directly cause downstream parsing failures. When selecting a model, construct several complex structured-output test cases specifically to verify format-following capability.

3. The Trade-off Between Response Latency and Throughput

Some models score high in offline evaluations but are unbearably slow in actual inference. If your business scenario is real-time conversation facing end users, latency must be a core hard metric. On the other hand, for batch data processing scenarios (such as log analysis or document summarization), throughput is more critical. You need to be confident that the chosen model meets your SLA requirements for P95 latency under peak load.

Deployment Trade-offs: API Access or Self-Hosted Deployment?

Once the model is selected, the next key decision is deployment. There are currently three mainstream routes:

• Public cloud API access: advantages are zero operations and good elasticity, suitable for prototyping and scenarios with fluctuating traffic.
• Self-hosted deployment: advantages are that data never leaves your domain and customization is flexible, suitable for enterprises with rigid data security requirements.
• Hybrid mode: run general capabilities on public cloud APIs while keeping core data-related tasks in a self-hosted environment.

Note that self-hosted deployment is not just a software problem—it also involves hardware procurement, operations team staffing, and model optimization (such as quantization and distillation). Many teams underestimate these costs early on, leading to project delays. My recommendation: first use APIs to validate the business workflow, confirm positive ROI, and then evaluate whether self-hosting is worth the investment.

Future Trends and Recommendations: Keep Tracking, but Don’t Switch Frequently

The LLM field iterates extremely quickly, with new versions released almost every quarter. But for enterprise users, frequently changing the underlying model is not wise. Model switching means re-evaluation, prompt adjustments, and business compatibility verification—hidden costs that are often overlooked.

A more rational approach:

• Abstract a model adaptation layer at the business layer. Encapsulate model calls behind a unified interface so that future model replacements only require swapping the adapter, without changing upper-layer business logic.
• Automate the evaluation set. Turn the test set into automated regression tests so that whenever a candidate model appears, tests run automatically and produce comparison reports.
• Watch the trend of inference costs. As Chinese models mature, inference unit prices continue to fall, meaning you can try larger-parameter models at lower cost.

Ultimately, the model is just a tool—what matters is always your depth of understanding of the business problem and your grasp of engineering details. Which model to choose, how to integrate it, and how to tune it—there are no standard answers to these questions, but through systematic evaluation and engineering practice, you can find the answer that fits you best.