A Practical Guide to Selecting Domestic Large Language Models and Building Inference Platform Workflows

1. Background: Domestic LLMs Enter the “First Tier”

Over the past two years, large language model technology has moved from research labs into engineering practice, and domestic models have made significant progress on key metrics such as reasoning capability, code generation, and long-context processing. In code generation and complex reasoning tasks in particular, some domestic models now rank near the top of internationally recognized evaluation benchmarks, marking a shift from “usable” to “genuinely useful.”

For developers and enterprises, the question is no longer whether to adopt LLMs, but how to make sound technical choices among the many available options. This article addresses three areas—technical evaluation dimensions, the role of inference platforms, and hands-on integration practices—to help readers build a reusable selection methodology.

2. Core Evaluation Dimensions for Model Selection

Model selection is not simply a matter of comparing benchmark scores. It should be grounded in business scenarios and organized around a multi-dimensional evaluation framework. The following five dimensions have proven most critical in practice.

2.1 Reasoning and Logic

Reasoning capability directly determines how well a model performs on complex tasks such as multi-step mathematical derivation, code debugging, and structured data analysis. When evaluating, do not rely on a single benchmark; instead, focus on:

• Balanced multi-task performance: how evenly the model performs across math, science, and code;
• Stability of long-chain reasoning: whether it drifts or loses intermediate conclusions during multi-step reasoning;
• Instruction-following: whether it strictly adheres to output formats and constraints.

When selecting a model, build an internal evaluation set and run blind tests with real business samples rather than relying on a single public benchmark score.

2.2 Context Window and Long-Document Handling

Long-context capability determines whether a model can handle complete code repositories, lengthy contracts, multi-turn conversation history, and similar scenarios. Key evaluation points include:

• Effective context length: advertised window sizes often differ from what is actually usable;
• Long-document recall quality: whether key information can be accurately extracted from tens of thousands of tokens of context;
• Position sensitivity: whether recall performance is consistent when information appears at the beginning, middle, or end of the context.

For applications that must process large codebases or long documents, long-context capability is often the deciding factor.

2.3 Code Generation and Engineering Fit

Code generation is one of the most widely adopted enterprise use cases today. Evaluation should focus on:

• Multi-language support: coverage of mainstream programming languages and generation quality;
• Engineering readiness: whether the model produces maintainable code that follows project conventions, not just snippets that happen to run;
• Tool and function calling: whether it supports structured output and integration with external tools.

2.4 Deployment Options and Inference Cost

However strong a model is, it is hard to scale if deployment costs are too high. Consider:

• Inference latency: time to first token and overall throughput;
• Quantization and acceleration support: whether low-precision quantization and batch optimization are supported;
• Hosted services vs. self-hosting: using a hosted API or deploying privately directly affects cost structure and data compliance.

2.5 Ecosystem and Toolchain Maturity

This includes SDK completeness, documentation quality, community activity, and support by mainstream inference platforms. The more mature the ecosystem, the lower the integration and migration costs.

3. The Role of Inference Platforms: From “Model Marketplace” to Unified Access Layer

3.1 Why Inference Platforms Matter

As the number of models grows rapidly, developers face a practical problem: different models use different API protocols, authentication methods, and parameter naming conventions. Integrating each model separately carries extremely high engineering costs.

The value of an inference platform lies in providing a unified inference access layer: it wraps multiple models behind a consistent API, so developers only need to integrate one protocol to switch between models. Think of it as a “model marketplace”—the platform hosts a variety of models that can be called on demand.

3.2 Practical Benefits

• Rapid validation: compare how different models perform on real tasks via API without deploying anything yourself;
• Lower migration costs: with a unified interface, switching from model A to model B requires only changing the model identifier;
• Simplified operations: elastic scaling, monitoring and alerting, and rate limiting are handled by the platform.

3.3 Typical Integration Example

The following pseudocode illustrates the basic shape of a unified inference call:

# Unified inference client example (pseudocode)
from inference_platform import Client
client = Client(api_key="YOUR_API_KEY", base_url="https://api.example.com/v1")
response = client.chat.completions.create(
    model="model-name",          # Switching models only requires changing this identifier
    messages=[
        {"role": "system", "content": "You are a senior code assistant."},
        {"role": "user", "content": "Please explain the time complexity of this code."}
    ],
    temperature=0.2,
    max_tokens=1024
)
print(response.choices[0].message.content)

This unified interface design turns model selection from an “engineering overhaul” into a “parameter change,” greatly improving validation efficiency.

4. Selection in Practice: How to Decide Quickly

4.1 Build an Evaluation Process

A recommended step-by-step approach:

1. Define the scenario: identify the core task type (code generation, document Q&A, data analysis, etc.);
2. Shortlist candidates: select 3–5 candidate models based on public information and community feedback;
3. Build an evaluation set: extract 50–200 samples from real business data, covering both typical and edge cases;
4. Test on a unified platform: call each model through the same interface on an inference platform to eliminate integration differences;
5. Score comprehensively: weight accuracy, latency, cost, and stability;
6. Validate with small traffic: roll out gradually in production and observe long-term behavior.

4.2 Common Pitfalls

• Benchmark worship: public benchmarks and business tasks have different distributions; a high score does not guarantee a good fit;
• Ignoring cost: focusing only on quality while overlooking inference cost and latency, which prevents scaling;
• One-time selection: models iterate quickly, so establish a periodic re-evaluation mechanism instead of locking in a choice permanently.

4.3 Multi-Model Strategies

In practice, a single model rarely covers all scenarios. A more pragmatic approach is multi-model composition:

• Use stronger reasoning models for high-complexity tasks;
• Use lightweight models for high-concurrency, low-latency scenarios;
• Route dynamically by task type through a unified inference layer.

This architecture preserves quality while controlling cost, and is a common practice in enterprise applications today.

5. Conclusion

Domestic LLMs are now capable of competing with mainstream international models. The key to selection is not “which one is strongest,” but “which one best fits the current business.” Building a multi-dimensional evaluation framework, validating quickly through a unified inference platform, and adopting a multi-model strategy are effective ways to reduce selection risk and improve deployment efficiency.

Developers should return to business fundamentals when selecting models: define task types clearly, build realistic evaluation sets, pay attention to cost and latency, and keep re-evaluating on a regular basis. Only then can you continue to make sound technical decisions in a fast-evolving LLM ecosystem.