A Practical Guide to Selecting Domestic LLMs: Multi-Dimensional Comparison and Engineering Practices

1. Background: Domestic LLMs Enter the “Selection Phase”

Over the past year, domestic large language models have shifted from “whether one exists” to “which one to choose.” Every few months, a new batch of model versions is released: general conversation capabilities keep improving, coding, long-context, and multimodal capabilities are gradually being filled in, and some flagship models have reached the first tier on public benchmark leaderboards.

For developers, this is both good news and a new problem—when faced with multiple models that have similar capabilities and positioning, how exactly should you choose?

This article does not recommend any specific vendor or product. Instead, it approaches the problem from an engineering perspective, outlining a general selection methodology and providing a practical unified integration solution. Whichever model you ultimately choose, this approach applies.

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2. Before Selection: What Problem Are You Solving?

Many teams’ first reaction during selection is “which model is the strongest.” But “strongest” is a vague concept. What really determines the selection outcome is the constraints your business scenario places on the model.

You can start with a self-assessment across the following dimensions:

1. Task type: Is it general conversation, code generation, document summarization, or structured extraction? Different models perform noticeably differently across tasks.
2. Context length requirements: Do you need to process long documents, large codebases, or long multi-turn conversations? This directly determines whether you can choose a model with a smaller context window.
3. Response latency requirements: Is it offline batch processing or online real-time interaction? Latency-sensitive scenarios need to prioritize inference speed.
4. Cost structure: Is billing per token, per call, or based on a self-built inference cluster? Different cost models lead to completely different selection logic.
5. Compliance and data security: Can data leave your domain? Is private deployment required? This is a hard threshold for many enterprise scenarios.
6. Ecosystem compatibility: Is it compatible with mainstream API protocols? Does it support function calling, structured output, and streaming responses? This determines integration cost.

Once these constraints are clearly listed, the selection range usually narrows automatically to two or three candidates.

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3. A Framework for Comparing Core Capabilities

Without specific product parameters, you can use a general capability framework to evaluate any large model. The following dimensions are widely recognized evaluation directions in the industry:

3.1 Language Understanding and Reasoning

This includes commonsense reasoning, logical reasoning, multi-step mathematical reasoning, and similar capabilities. These are usually compared horizontally through public benchmark sets (such as general benchmarks like MMLU and GSM8K). Note that benchmark scores can only serve as a reference, because the distribution of real business data often differs significantly from benchmark sets.

Recommended practice: build a small-scale evaluation set from your own business data, covering typical inputs and edge cases, and run each candidate model against it with manual or semi-automated scoring. This is more reliable than looking at leaderboards.

3.2 Coding Capabilities

If your business involves code generation, code completion, or code review, focus on the model’s performance on coding tasks. Evaluation dimensions include:

• Correctness rate for single-function generation
• Cross-file context understanding
• Familiarity with specific languages/frameworks
• Support for derived tasks such as code explanation and unit test generation

Again, validate with tasks from real codebases rather than relying solely on public leaderboards.

3.3 Context Window and Long-Text Processing

Context window size directly affects the types of tasks you can handle. Common window sizes range from tens of thousands of tokens to millions of tokens. But note: a large window does not equal good long-text performance. Some models exhibit “lost in the middle” behavior in long contexts, where recall of information in the middle of a document drops noticeably.

Evaluation method: construct a “needle in a haystack” test, insert key information at different positions in a long document, and observe whether the model can recall it accurately.

3.4 Structured Output and Function Calling

In engineering scenarios, models often need to output structured data such as JSON or XML, or call external tools. This requires the model to have:

• Stable JSON output capability (without arbitrarily adding explanatory text)
• Support for the Function Calling protocol
• Ability to follow a schema

In real projects, these capabilities matter far more than whether the chat feels fluent.

3.5 Inference Performance and Cost

This includes time to first token, throughput, and cost per token. For high-concurrency scenarios, inference performance is often more critical than model capability—a slightly weaker but faster and cheaper model may be more suitable for online services than the “strongest model.”

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4. Choosing a Deployment and Integration Model

Selection is not just about choosing a model; it is also about choosing an integration approach. The main integration models currently available are:

4.1 Public Cloud API Calls

The most common approach, billed per token or per call, with no need to build your own infrastructure. The advantages are fast integration and good elasticity; the disadvantages are that data must leave your domain and long-term costs may be higher.

4.2 Private Deployment

Deploy the model on your own servers or in a dedicated cloud environment. The advantages are that data stays within your domain and controllability is strong; the disadvantages are the need for GPU resources, high operations and maintenance costs, and the need to track model updates yourself.

4.3 Hybrid Model

Sensitive tasks go through private deployment, while non-sensitive tasks go through public cloud APIs. This model is relatively common in industries such as finance and healthcare.

4.4 Unified Access Layer

Regardless of which model is used underneath, it is recommended to add a unified access layer between the application and the model to shield differences between vendor APIs. The value of doing this is:

• No need to modify business code when switching models
• Enables canary releases and A/B testing
• Enables unified rate limiting, retries, logging, and monitoring

Below is a simplified example of a unified access layer (Python):

from abc import ABC, abstractmethod
from typing import List, Dict, Any
class LLMProvider(ABC):
    """Abstract base class for model providers"""
    @abstractmethod
    def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
        ...
class ProviderA(LLMProvider):
    def chat(self, messages, **kwargs) -> str:
        # Call provider A's SDK or HTTP interface
        ...
class ProviderB(LLMProvider):
    def chat(self, messages, **kwargs) -> str:
        # Call provider B's SDK or HTTP interface
        ...
class LLMRouter:
    """Unified routing layer"""
    def __init__(self, providers: Dict[str, LLMProvider]):
        self.providers = providers
    def chat(self, provider_name: str, messages, **kwargs) -> str:
        provider = self.providers.get(provider_name)
        if not provider:
            raise ValueError(f"Unknown provider: {provider_name}")
        return provider.chat(messages, **kwargs)

With this abstraction, business code depends only on LLMRouter, and switching the underlying model only requires registering a new Provider.

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5. Unified API Protocol: The Key to Reducing Integration Cost

If every model vendor uses a different API protocol, integration costs will be very high. There are currently two mainstream approaches in the industry:

1. OpenAI-compatible APIs: Many model service providers offer interfaces compatible with OpenAI Chat Completions, so developers can use the same client code to call different models.
2. A self-built gateway for protocol conversion: Convert each vendor’s API into a unified format at the gateway layer and expose a single endpoint externally.

Either way, the goal is to decouple business code from specific models. Below is a typical configuration structure example:

models:
  - name: model-a
    provider: provider_a
    endpoint: https://api.example.com/v1
    api_key_env: PROVIDER_A_KEY
    max_tokens: 8192
    supports_function_calling: true
  - name: model-b
    provider: provider_b
    endpoint: https://api.example.com/v1
    api_key_env: PROVIDER_B_KEY
    max_tokens: 32768
    supports_function_calling: true

Business code selects a model by name, and the routing layer decides which Provider to use.

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6. General Configuration Approach for Client Tools

For developers using desktop clients or IDE plugins, the process for integrating a new model is usually:

1. Obtain an API key: Generate it in the model provider’s console, and make sure to set a reasonable expiration time and permission scope.
2. Configure the API address: Enter the inference endpoint provided by the service provider. If the client supports custom providers, choose “generic OpenAI-compatible” or a similar option.
3. Fill in the model ID: Different providers use different model ID naming conventions, usually in the form vendor/model-name. Refer to the provider’s documentation for the exact ID.
4. Verify connectivity: Send a simple message first to confirm that authentication, networking, and model availability are all working.
5. Configure advanced parameters: Such as temperature, maximum tokens, timeout, and proxy settings.

A common pitfall is the permission scope of the API key. Some keys can only call certain models, and some keys have expiration times, so be sure to confirm before configuring.

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7. Selection Decision Checklist

Based on the above, here is an actionable selection decision checklist:

Dimension	Key Question	Validation Method
Task fit	How does the model perform on your core tasks?	Build your own evaluation set and score manually
Context capability	Can it handle your longest input?	Long-text recall test
Structured output	Does it reliably output JSON / call functions?	Schema adherence rate test
Latency and throughput	Does it meet your online SLA?	Load testing, observe P99 latency
Cost	Is the cost per token acceptable?	Estimate monthly cost based on business volume
Compliance	Can data leave your domain?	Legal and security assessment
Integration cost	Is it compatible with existing protocols?	Trial integration through the unified access layer
Stability	How is service availability?	Observe over a period of time and check the error rate

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8. Conclusion

Selecting a domestic large model is essentially an engineering decision problem under multiple constraints, not simply a question of “who is strongest.”

The recommended practice path is:

1. Clarify business constraints first (tasks, latency, cost, compliance)
2. Use a self-built evaluation set to screen candidate models
3. Shield vendor differences through a unified access layer
4. Validate with small-traffic canary releases, then scale up gradually
5. Monitor continuously and re-evaluate periodically

Models iterate quickly, and today’s optimal solution may no longer be optimal a few months later. Therefore, keeping the architecture replaceable is more important than choosing the right model once. Keep the access layer thin and generic, and whatever new models emerge in the future can be incorporated into your tech stack at low cost.