Choosing a Chinese LLM: From Capability Evaluation to Practical Integration

I. Background: A Crowded Market Makes Selection Difficult

Over the past year, the iteration pace of Chinese large language models (LLMs) has accelerated significantly. New flagship versions are released almost every few weeks, constantly pushing the boundaries of parameter scale, context window size, reasoning capabilities, and multimodal support. For developers and enterprise technical teams, this is both a blessing and a curse—when faced with multiple candidate models in the same tier, which one should you choose?

Common dilemmas include:

• How significant are the performance differences among various models in general conversation, code generation, and long-document comprehension?
• How should one balance free API quotas, API stability, and response latency?
• Is it necessary to lock into a specific cloud provider? Can multiple models be accessed via a unified API?
• How can local clients simultaneously compare the outputs of multiple models to make quick judgments?

This article does not review or recommend specific commercial brands. Instead, starting from a general technical selection methodology, it combines current mainstream unified inference platform integration approaches to provide a reusable, practical path.

II. Five Core Dimensions for Model Selection

When selecting a Chinese LLM, it is recommended to establish an evaluation framework based on the following dimensions rather than relying solely on leaderboard rankings.

1. Foundational Capabilities

• General Reasoning and Knowledge QA: Evaluate the model’s accuracy in mathematics, logic, and common sense questions.
• Code Generation and Comprehension: Check if it supports multi-language code completion, unit test generation, and cross-file refactoring suggestions.
• Long Context Processing: Context windows have evolved from early 8K and 32K to 128K or even higher today. Scenarios like long-document summarization, contract review, and paper reading are highly sensitive to window length.
• Multimodal Capabilities: Determine if it supports image comprehension, audio input/output, and video frame analysis.

2. Engineering

• API Compatibility: Whether it is compatible with OpenAI-style interface specifications, which determines migration costs.
• Concurrency and Rate Limiting: The QPS limits under free quotas and the friendliness of timeout retry mechanisms.
• Response Latency: Time to first token and overall generation speed directly impact the interactive experience.

3. Cost

• Is the free quota sufficient for long-term use by individual developers?
• Are the pricing tiers transparent? Is billing based on tokens or API calls?
• Are there hidden charges for inference resource consumption?

4. Ecosystem and Toolchain

• Are there ready-made adaptations for mainstream clients, IDE plugins, and Agent frameworks?
• Does it support function calling, structured output, and streaming responses?

5. Compliance and Data Security

• Is user data used for model training?
• Does it support private deployment or dedicated instances?
• Do log retention policies comply with enterprise internal control requirements?

III. Unified Inference Platforms: Reducing Multi-Model Integration Costs

The traditional approach requires registering an account, applying for a key, and adapting the SDK for each model provider, resulting in high maintenance costs. Recently, unified inference platforms have emerged as an efficient compromise.

These platforms typically feature:

• Aggregating APIs from multiple model providers and exposing a unified calling endpoint;
• Using a unified authentication method (e.g., a Key with an nvapi- prefix);
• Compatibility with the OpenAI API protocol, requiring only a one-time client configuration;
• Offering free calling quotas for some models, suitable for individual developer verification and prototyping.

Note: The deployment and removal of models on these platforms are decided by the platform providers. A situation may arise where “the official vendor has released a new version, but the platform still hosts the older version.” During selection, rely on the actual model IDs available on the platform.

IV. Practice: Three Steps to Integrate a Multi-Model Chat Environment

Using a general process as an example, below is how to complete registration, authentication, and client configuration on a unified inference platform. These steps do not involve any specific vendor’s proprietary interfaces and represent common industry practices.

Step 1: Registration and Identity Verification

1. Open the developer portal of the unified inference platform;
2. Register an account using an email address and set a password;
3. Check your email for the verification code and enter it;
4. Complete secondary phone verification (some platforms support international numbers);
5. Set an account display name to complete registration.

Common Pitfall: On some platforms, a “Verify” entry will appear in a corner of the page after registration. You must manually click it and complete phone verification; otherwise, you will encounter a “permission denied” prompt when generating an API Key later.

Step 2: Generate an API Key

1. Log in and navigate to User Center → API Keys;
2. Click “Generate API Key”;
3. Name the Key (it is recommended to name it by purpose, e.g., local-cherry);
4. Set the expiration time to “Never” or the longest available period;
5. Copy the generated Key (usually starting with a fixed prefix) and save it to a password manager immediately.

This Key can typically be used to call all models listed on the platform, including flagship versions from different vendors.

Step 3: Configure the Local Client

Choose a client tool that supports parallel multi-model conversations. The configuration parameters generally include:

{
  "provider": "NVIDIA",
  "apiKey": "nvapi-xxxxxxxx",
  "baseURL": "https://integrate.api.nvidia.com",
  "models": [
    "z-ai/glm5",
    "moonshotai/kimi-k2.5",
    "minimaxai/minimax-m2.1"
  ]
}

Notes:

• baseURL serves as the unified inference endpoint. The exact address varies by platform; refer to the official documentation.
• Model IDs use a “provider/model-name” namespace format.
• If the latest minor version of a model is not yet listed on the platform, you can use the second-latest version as a substitute and switch once the platform updates.

Once configured, check multiple models at the bottom of the client and select a “side-by-side” layout for answers. This achieves a comparative effect of one prompt, multiple model responses.

V. Practical Tips for Multi-Model Comparison

1. Unified Prompt Templates: Send the exact same prompt to multiple models to avoid misjudgments caused by prompt variations.
2. Fixed Evaluation Sets: Prepare 20 to 50 tasks covering code, math, writing, and long-text processing to form an internal benchmark.
3. Record Latency and Token Consumption: Evaluate not only answer quality but also engineering metrics.
4. Monitor Version Drift: Re-run the evaluation set after a model update to avoid “selecting a new model based on old conclusions.”
5. Combined Usage: Different models have different strengths. In production environments, adopt a “routing + fallback” strategy—for example, routing coding tasks to one model and long-document tasks to another.

VI. Conclusion

There is no single correct answer when selecting a Chinese LLM. The key is to establish a quantifiable, reproducible, and iterable evaluation process. The emergence of unified inference platforms allows developers to access multiple candidate models simultaneously using a single Key and one client, significantly lowering trial-and-error costs. It is recommended to start with free quotas, get the integration pipeline working, and then conduct small-scale comparisons using your own business data to ultimately form the optimal combination for your team.

Moving forward, as model capabilities become increasingly differentiated, multi-model collaboration is highly likely to become the mainstream architecture. Mastering unified integration and comparison methods is the first step toward this architecture.