Domestic LLM Selection Guide: Capability Comparison and Cloud Integration

1. Background: More Models, Harder Choices

Over the past year, the iteration speed of Chinese large language models has far exceeded expectations. From general-purpose chat to code generation, from long-context understanding to multimodal reasoning, new flagship versions have appeared every few months, reshaping evaluation leaderboards across the board.

For developers, this is both a blessing and a burden: the more options available, the higher the cost of making a selection decision.

A typical problem looks like this: when a business needs to integrate LLM capabilities, which model should it choose? The one with the highest leaderboard ranking, or the one with the most mature ecosystem? Should you call the vendor’s native API directly, or go through a unified inference platform?

This article does not recommend any specific vendor. Instead, it starts from a methodology for technical evaluation and combines it with the current mainstream forms of cloud-based inference services to provide a reusable evaluation framework and integration practice.

2. Before Comparing: Clarify What Scenario You Are Selecting For

Model selection is not about picking the “strongest” model, but the “most suitable” one. Before opening any leaderboard, clarify the following four dimensions:

1. Task Type

• General conversation and knowledge Q&A: Evaluate instruction-following ability and knowledge coverage.
• Code generation and completion: Evaluate training adequacy for specific programming languages and the ability to understand repository context.
• Long document processing: Evaluate context window size and information retention in long texts.
• Structured output: Evaluate stability in producing JSON, tables, and other formats.

2. Invocation Pattern

• Is streaming output required?
• Is Function Calling or tool-use capability required?
• Is batch inference or asynchronous task processing required?

3. Cost Structure

• Is billing per token or per API call?
• Is there a free tier or trial quota?
• Are costs manageable for long-context scenarios?

4. Deployment and Compliance

• Does data need to remain within a specific jurisdiction?
• Is routing through a third-party platform permitted?
• Is a private deployment option required?

Only after answering these questions does model comparison become meaningful.

3. How to Compare Chinese LLMs Objectively

There are three common comparison approaches:

(1) Public Leaderboards
Specialized leaderboards for code generation, mathematical reasoning, and long-context understanding can serve as an initial screening reference. However, note that there is a gap between leaderboard scores and real-world business performance, especially when private domain knowledge or specific business logic is involved.

(2) Custom Evaluation Sets
This is the most reliable approach. Extract 50–200 representative samples from real business scenarios, covering typical cases and edge cases, and run blind evaluations on candidate models. Recommended evaluation dimensions include: accuracy, format stability, response latency, and refusal rate.

(3) Small-Scale Canary Testing
Before a full switch, route a small amount of real traffic to two models simultaneously and compare online metrics. This step can expose issues that offline evaluation misses, such as latency spikes during peak hours and long-context truncation.

A pragmatic approach: first narrow the field to 2–3 candidates using public leaderboards, then make the final decision with a custom evaluation set.

4. Choosing an Integration Path: Native API or Unified Inference Platform

After identifying candidate models, the next question is how to integrate them. There are two main paths:

Path 1: Directly Calling Vendor Native APIs

• Pros: Immediate access to new model releases and the most complete parameter control.
• Cons: Each model requires maintaining separate authentication, billing, and error-handling logic. Maintenance costs grow quickly when multiple models are used in parallel.

Path 2: Integrating Through a Unified Inference Platform

• Pros: A single authentication system and a single entry point. You can switch between multiple models within the same client, which is ideal for comparison and rapid prototyping.
• Cons: New models typically appear on the platform with some delay, and certain advanced parameters may not be exposed.

For teams that need to quickly evaluate multiple models, a unified inference platform is a more efficient starting point. It is essentially a “model aggregation layer”: the upper layer provides a unified API specification, while the lower layer connects to different vendors’ inference backends. Developers only need a single key to access all available models on the platform.

5. Practice: Accessing Chinese LLMs Through a Unified Inference Platform

The following example illustrates the complete integration flow from registration to conversation. The process has three steps: account registration and verification, API key generation, and client configuration.

5.1 Account Registration and Verification

1. Open the developer portal of the inference platform and click the login entry in the top-right corner.
2. Enter your email and password to create an account.
3. Check your inbox for the verification code, enter it, and set an account name.
4. Critical step: After creating the account, complete the identity verification process on the page, which typically requires binding a phone number.

This step is often overlooked, but accounts that have not completed verification usually cannot generate a usable API key.

5.2 Generating an API Key

1. After logging in, go to your account settings and find the API Keys management page.
2. Click to generate a new key.
3. Give the key a recognizable name and set the expiration to long-term (such as “never expires”).
4. After generation, you will receive a string starting with a specific prefix. This is your credential for making API calls.

Security Notes:

• Some platforms only display the key once after generation. Save it immediately in a secure location.
• Do not hardcode keys in frontend code or public repositories.
• Assign separate keys to different projects for easier auditing and revocation.

This key can typically call all models available on the platform, including multiple Chinese LLMs.

5.3 Configuring the Client

Using a common desktop chat client as an example:

1. Download and install the client.
2. Open settings and navigate to the model service configuration page.
3. Select the inference platform as the service provider.
4. Fill in the following parameters:

API Key: <paste the key generated in the previous step>
API Base URL: <the unified inference endpoint provided by the platform>
Model ID: <fill in as needed, e.g., the platform's identifier for a Chinese model>

1. Save the configuration and you can switch between models within the client to start chatting.

To compare multiple models simultaneously, configure multiple model entries in the client, each corresponding to a different model ID. You do not need to re-enter the key when switching.

5.4 Verifying the Integration

After configuration, run a minimal verification:

# Pseudocode example; refer to the platform's SDK documentation for specifics
client = Client(api_key="<your-key>", base_url="<platform-endpoint>")
response = client.chat.completions.create(
    model="<model-id>",
    messages=[{"role": "user", "content": "Explain what an attention mechanism is in one sentence"}],
    stream=True
)
for chunk in response:
    print(chunk.choices[0].delta.content, end="")

If you see streamed output, the integration is successful. If you encounter an authentication error, first check whether the key has been activated and whether the account verification is complete.

6. Engineering Recommendations for Multi-Model Deployments

When a business needs to use multiple models concurrently, prepare the following architecture:

1. Abstract the Model Invocation Layer
Do not hardcode vendor-specific SDK calls in business logic. Wrap a unified invocation interface, with model differences injected through configuration:

class ModelRouter:
    def __init__(self, config):
        self.providers = config["providers"]
    def chat(self, model_name, messages):
        provider = self.providers[model_name]
        return provider.invoke(messages)

2. Unified Error Handling and Retry
Vendor error code systems vary. Map them to a unified schema in the adapter layer and implement reasonable retry strategies (such as exponential backoff).

3. Log Invocation Metadata
At minimum, record: model name, input token count, output token count, latency, and success status. This data serves as the basis for cost optimization and future model switching.

4. Configure Fallback Strategies
Automatically switch to a backup model when the primary model is unavailable. In multi-model architectures, this is a low-cost, high-value resilience measure.

7. Summary

Selecting a Chinese LLM is fundamentally a combined decision involving scenario fit, cost constraints, and engineering feasibility. There is no universally optimal model—only the combination that best fits your current business needs.

The recommended practice path:

1. Clarify business scenarios and constraints.
2. Narrow the candidate pool using public leaderboards.
3. Quickly run multi-model comparisons through a unified inference platform.
4. Make the final decision with a custom evaluation set.
5. Build proper abstraction and fault tolerance at the engineering level to leave room for future model iteration.

Models will continue to evolve, but the selection methodology and integration engineering capabilities are durable assets. Get these two things right, and no matter when the next model version is released, you will be able to complete validation quickly and switch smoothly.