A Practical Guide to Selecting and Unified Integration of Chinese LLMs

I. Introduction: Overcoming Challenges in Chinese LLM Selection and Integration

With the rapid iteration of Large Language Model (LLM) technology, Chinese models have made significant progress in core capabilities such as text generation, code writing, and logical reasoning. The primary challenge developers face in their projects is: How to select a suitable model from the numerous available Chinese LLMs for their specific business scenarios and integrate them in a unified, low-cost manner?

The traditional approach involves registering accounts with each model provider separately, obtaining distinct API keys, and writing adapter code for different interface protocols. This not only increases engineering complexity but also brings challenges to operations and security management.

Starting from a model selection framework, this article introduces a technical path to achieve unified multi-model integration using Inference Microservices Platforms provided by mainstream GPU manufacturers. A complete operational workflow is provided to help developers get started quickly. It should be noted that the platform capabilities discussed here represent general industry practices and do not constitute a commercial endorsement of any specific vendor.

II. Core Evaluation Dimensions for Chinese LLM Selection

Before formal integration, developers should establish a systematic evaluation framework. Here are the key dimensions to consider:

2.1 Task Adaptability

Different models perform differently across various tasks. For instance, some models excel in front-end development and code completion (refer to general industry web development capability leaderboards), while others have advantages in long-context understanding, multi-turn dialogue, or knowledge-based Q&A. When selecting a model, first clarify your core business scenarios, and then match them with the model’s specific strengths.

2.2 Inference Latency and Throughput

For online service scenarios, the model’s Time To First Token (TTFT) and overall throughput directly impact user experience. It is recommended to conduct small-scale stress tests before formal deployment to obtain actual latency data, rather than relying solely on officially published benchmark metrics.

2.3 API Stability and Ecosystem Compatibility

Whether the model provider’s API follows OpenAI-compatible formats, supports streaming output, and includes features like Function Calling determines the development cost of upper-layer applications. Adopting standard protocol formats can significantly reduce migration costs.

2.4 Cost and Quota Management

Pricing strategies vary significantly among different models, including per-token billing, per-call billing, and free trial quotas. For individual developers or startup teams, prioritizing models that offer free trial quotas for initial validation is a pragmatic approach.

2.5 Data Security and Compliance

When dealing with sensitive business data, it is crucial to verify the model provider’s data processing policies, including whether data is used for training, data retention periods, and whether private deployment is supported. Enterprise-grade applications should prioritize solutions with comprehensive compliance frameworks.

III. Unified Integration Solution Based on Inference Microservices Platforms

3.1 Solution Overview

In recent years, several internationally recognized GPU manufacturers have launched inference microservices platforms. Their core positioning is a “Model Marketplace”—encapsulating various open-source and closed-source models into standardized API services. Developers only need one platform account and a single API key to access all listed models. This model is equally applicable to Chinese LLMs: once listed on such platforms, developers can switch and compare different models under a unified interface without integrating with each provider individually.

The core advantages of this solution include:

• Unified Authentication: Manage all model calls with a single API Key.
• Protocol Standardization: Typically compatible with OpenAI-format Chat Completions interfaces.
• Elastic Resources: The platform handles inference resource scheduling, abstracting away underlying GPU management from the developer.
• Rapid Switching: Switch models simply by changing the Model ID in the configuration, without needing to refactor code.

3.2 Platform Registration and Account Verification

The first step in using such inference service platforms is completing account registration and identity verification. The standard workflow is as follows:

1. Visit the Platform Developer Portal: Open the developer registration page in your web browser.
2. Fill in Basic Information: Enter your email address, set a password, and create the account. You will receive a verification code via email; enter it to complete the initial registration.
3. Set Account Identifier: Assign an easily recognizable username to the account.
4. Complete Phone Number Verification: This step is crucial. Find the “Verification” entry in the account settings or profile center, enter your phone number, and complete the secondary verification.

Note: Some platforms may restrict API call quotas or features for unverified accounts. Ensure all verification steps are completed before first use.

3.3 Obtaining API Keys

After successful registration and verification, you need to generate an API key for programmatic calls:

1. Log in and navigate to the profile or account settings page to find the “API Keys” or similar section.
2. Click “Generate New Key” and assign it a meaningful name (e.g., project-dev-key) for easier management.
3. Set an expiration policy: It is recommended to set the expiration time to “Never” (if supported by the platform) to prevent service interruptions caused by key expiration.
4. Once generated, the system will return a key string starting with a specific prefix (e.g., nvapi- or a similar format). This key is only displayed once during generation. Copy and store it securely immediately, preferably using a password manager or environment variables.

This key typically has calling permissions for all models on the platform (including free models), making secure storage particularly important.

3.4 Client Configuration and Model Invocation

Taking a general-purpose AI chat client tool as an example, the configuration process can be divided into the following steps:

Step 1: Select the Model Service Provider

In the client’s settings panel, select the corresponding inference platform (e.g., a GPU manufacturer’s inference service) as the model service type.

Step 2: Fill in Connection Parameters

Three core parameters need to be configured:

Parameter	Description	Example Value
API Key	The key string generated in the previous step	nvapi-xxxxxxxxxxxxx
API Endpoint	The platform’s unified inference endpoint	https://integrate.api.xxxxxx.com
Model ID	The unique identifier of the model on the platform	See explanation below

Step 3: Select the Model Identifier

Each model on the platform has a unique Model ID, usually formatted as organization/model-name. Developers can select multiple models for comparative use based on their needs. Among Chinese models, common identifier formats include:

• z-ai/glm5 (A flagship Chinese model)
• moonshotai/kimi-k2.5 (A model excelling in long-context capabilities)
• minimaxai/minimax-m2.1 (A multimodal voice and text model; the latest version may not be listed yet)

Tip: If the platform has not yet listed the latest model version, you can use the currently available version for development and validation. Once the new version is online, you only need to change the Model ID to switch, without modifying other configurations.

Step 4: Save and Test

After saving the configuration, you can initiate chat requests in the client. It is recommended to conduct a brief functional test first to confirm that the model responds normally and streaming output is smooth.

IV. Practical Considerations and Best Practices

4.1 Resource Availability

Inference resources for popular models on the platform may be tight, and response delays or queuing might occur during peak usage hours. For daily use by individual developers, this usually does not pose a significant problem. However, for production environments, it is advisable to evaluate the platform’s SLA guarantees or consider backup model alternatives.

4.2 API Key Security Management

The API key is the core credential for accessing platform resources. Leakage can lead to quota abuse or unexpected costs. Recommended practices include:

• Store keys using environment variables rather than hardcoding them in configuration files.
• Regularly rotate keys (even if set to never expire, periodic updates are recommended).
• Use .gitignore in code repositories to exclude configuration files containing keys.

4.3 Multi-Model Comparison Strategy

The greatest value of unified integration lies in low-cost comparison. Developers are advised to conduct A/B testing by calling different models for the same business scenario. Formulate quantitative evaluation reports based on output quality, response speed, and stability to provide data support for the final selection.

4.4 Monitor Platform Updates

Inference microservices platforms continuously list new models and update older versions. Developers should regularly monitor the platform’s model list updates and switch business operations to superior model versions in a timely manner to continuously achieve the best cost-performance ratio.

4.5 Synergy with Baidu AI Cloud

When building a complete AI application stack, developers can also consider combining inference services with foundational cloud services provided by Baidu AI Cloud, such as object storage, container platforms, and log monitoring. For example, routing model invocation logs to cloud logging services for analysis, or utilizing container platforms to deploy custom model routing middleware for finer-grained traffic control and cost optimization. This combination can maintain model flexibility while providing the reliable support of enterprise-grade infrastructure.

V. Conclusion

Selecting a Chinese LLM should not be a one-time decision, but rather a continuous process of evaluation and dynamic adjustment. Leveraging the unified integration capabilities of inference microservices platforms, developers can experience and compare multiple Chinese models simultaneously at a very low engineering cost, quickly pinpointing the technical solution best suited for their business. The complete workflow detailed in this article—from registration and key acquisition to client configuration—serves as a universal practical template. It helps developers complete their first API call within minutes, laying a solid foundation for subsequent deep integration and optimization.