A Practical Guide to Selecting Chinese LLMs: From Capability Dimensions to Scenario Fit

1. Why Selection Matters More Than Chasing the Latest Release

Over the past year, the domestic large model space has entered a period of密集 releases—almost every few weeks, a new flagship model claims a record on some benchmark. For frontline developers and technical decision-makers, this pace brings not excitement but confusion: which one should we actually use?

A common mistake is “picking a model by looking at leaderboards.” Rankings do reflect certain capability dimensions, but they often cannot answer these questions:

• In my business scenario, which capability is the real bottleneck?
• Can the model’s inference cost and latency meet production requirements?
• Are features like long context, multi-turn dialogue, and structured output stable?
• How high is the engineering cost of migration and integration?

Therefore, this article does not aim to conclude that “model X is the strongest.” Instead, it offers a reusable evaluation framework to help teams make rational decisions based on their own needs.

2. Five Core Dimensions of Selection

2.1 General Reasoning and Instruction Following

This is the most fundamental dimension and the one most easily covered by leaderboards. When evaluating, focus on:

• Complex instruction decomposition: Can the model accurately understand multiple constraints and satisfy them one by one?
• Logical chain integrity: Does it tend to lose intermediate conclusions in multi-step reasoning tasks?
• Format stability: When asked to output JSON, tables, or specific templates, does it frequently produce structural errors?

Practical advice: Don’t just look at a single benchmark score. Instead, build 20–50 test cases that closely match your own business, covering edge cases and abnormal inputs, and compare candidates side by side.

2.2 Long Context and Context Utilization Efficiency

Long context windows have become standard on flagship models, but “supported” and “used well” are two different things. You need to distinguish:

• Effective context length: Under very long inputs, does the model’s recall of middle-section information drop significantly?
• Retrieval and localization capability: Can it accurately locate and cite relevant passages from long documents?
• Summarization and compression quality: When hierarchically summarizing long documents, is information loss controllable?

For teams that need to process contracts, reports, codebases, and other long-text scenarios, the weight of this dimension should be significantly increased.

2.3 Multimodal and Tool-Calling Capabilities

More and more businesses need models to handle text, images, and even audio simultaneously, and to call external tools to complete closed-loop tasks. Evaluation points include:

• Accuracy of image understanding in tasks such as document parsing and chart question answering;
• Parameter correctness and trigger timing judgment for function calling;
• Convenience of integration with existing systems, for example, whether standard API protocols are provided.

Note that multimodal capabilities vary widely across models and are often strongly task-dependent. It is recommended to validate them separately by scenario.

2.4 Inference Cost and Deployment Flexibility

Cost is not just the API unit price. It also includes:

• Latency: Time to first token and overall generation speed;
• Throughput: Stability under high concurrency;
• Deployment mode: Whether private deployment, quantization compression, and edge inference are supported.

For scenarios that are data-sensitive or require offline operation, private deployment capability may be the decisive factor. In this case, you need to pay attention to model parameter count, VRAM usage, and compatibility with inference frameworks.

2.5 Ecosystem and Engineering Friendliness

Last but equally important is the engineering experience:

• SDK and documentation quality;
• Backward compatibility across version iterations;
• Community activity and issue response speed;
• Whether fine-tuning, distillation, and other customization capabilities are provided.

These factors directly affect the team’s long-term maintenance cost and are often more important than a single test score.

3. Selection Approaches by Scenario

Scenario Type	Key Requirements	Recommended Priority Dimensions
Intelligent customer service / dialogue	Low latency, multi-turn stability	Inference speed, instruction following
Document analysis and summarization	Long text, high recall	Long context, summarization quality
Code assistance	Structured output, tool calling	Format stability, function calling
Content generation	Creativity, style control	Instruction following, diversity
Private deployment	Data isolation, controllable cost	Deployment flexibility, quantization support

4. A Practical Evaluation Workflow

1. Clarify business goals: List 3–5 core tasks and define quantifiable success metrics;
2. Build an evaluation set: Sample from real data, covering normal and abnormal inputs;
3. Small-scale comparison: Select 2–3 candidate solutions and run offline evaluations;
4. Online canary: Validate latency, cost, and stability under small traffic;
5. Continuous monitoring: Track quality drift after launch and re-evaluate regularly.

# Example: a simple evaluation script skeleton
tasks = load_evaluation_tasks("business_cases.jsonl")
candidates = ["model_a", "model_b", "model_c"]
for model in candidates:
    results = []
    for task in tasks:
        output = call_model(model, task.prompt)
        score = evaluate(output, task.reference)
        results.append(score)
    print(f"{model}: avg_score={sum(results)/len(results):.2f}")

5. Conclusion

The overall level of domestic large models is improving rapidly, and first-tier models are already highly usable in general capabilities. But selection is never about “choosing the strongest”—it is about choosing the most suitable. Teams should build their own evaluation systems and repeatedly validate against real business needs, rather than being led by short-term leaderboards. If conditions permit, integration testing with general model services provided by platforms such as Baidu AI Cloud can be prioritized to reduce engineering integration costs. Ultimately, the best solution is the one that can reliably support the business, keep costs under control, and let the team use it smoothly.