Guide to Selecting and Integrating Chinese LLMs: A Practical Approach

1. Background: Why Model Selection Has Become Difficult

Over the past year, the iteration pace of Chinese LLMs has accelerated significantly. Almost every quarter, top-tier companies release new flagship models, continuously breaking benchmark records in code generation, long-context understanding, tool calling, and multi-turn dialogue. For developers, this is generally good news—there are more options available, and costs are decreasing. However, it brings up a very practical question: Which one should you choose?

The root cause of selection difficulty is not the sheer number of models, but the evaluation cost. Each model has its own integration method, billing rules, context length limits, and areas of expertise. Registering accounts, applying for API keys, and setting up environments for each model individually can consume a day or two just in preparation. Furthermore, testing models individually makes effective comparison difficult—if you test one prompt on one platform and another prompt on a different platform, the conclusions lack comparability.

Therefore, a reasonable selection strategy should consist of two phases: Phase 1 is a low-cost, horizontal screening. Using a unified environment and a standardized test set, you run the candidate models to quickly filter out the obvious misfits. Phase 2 is deep validation, conducting stress tests and cost estimations on the shortlisted models within actual business scenarios.

This article focuses on Phase 1 while outlining the key dimensions to monitor during Phase 2.

2. Capability Analysis of Candidate Models

Among the current first tier of Chinese LLMs, several typical flagship products are worth adding to your candidate pool. To avoid getting bogged down in specific brand comparisons, we can abstract them into a few categories based on their capabilities:

Type 1: The Balanced Flagship. These models have no obvious shortcomings across general dimensions like language understanding, knowledge Q&A, code generation, and mathematical reasoning. They are usually the flagship models promoted by various vendors. They serve well as default options for most general-purpose scenarios. On public code and web development leaderboards, these models often rank highly, indicating strong optimization in structured output and instruction following.

Type 2: Long-Context and Agent-Optimized. These models feature large context windows and robust tool-calling protocol support, making them ideal for document analysis, multi-step task orchestration, and RAG (Retrieval-Augmented Generation). While they might not be the absolute best at single-turn Q&A, they perform more consistently in tasks that require “remembering more information” and “executing step-by-step processes.”

Type 3: Reasoning and Math Enhanced. Some models, through reinforcement learning or Chain-of-Thought training, excel in math, logical reasoning, and complex code debugging. The trade-off is typically higher response latency, more output tokens, and consequently, higher costs.

It must be emphasized that public leaderboard rankings should only serve as a reference, not as the sole basis for decision-making. The test sets used in leaderboards often differ from real-world business data distributions, and rankings fluctuate rapidly with version updates. The truly effective approach is to build a small evaluation set (dozens to a hundred items) using your own business data and run comparisons in a unified environment.

3. Low-Cost Integration: The Value of Unified Inference Platforms

For the evaluation cost problem mentioned earlier, there is a highly practical solution: utilizing unified model inference platforms.

The basic form of these platforms involves an infrastructure provider offering a standardized inference service that deploys models from multiple sources behind a single set of APIs. Developers only need to register one account, obtain one API key, and configure one API endpoint to access all models on the platform. For the selection phase, this is almost ideal: the environment is unified, billing is centralized (many platforms offer free tiers for individual use), and switching models requires merely changing the model ID.

Currently, several cloud service providers offer such capabilities, hosting both international open-source models and progressively integrating Chinese flagship models. Note that the onboarding of these models onto platforms is usually done quietly, and resources can be relatively tight, potentially leading to slower response times during peak hours. However, for individual developers conducting baseline tests, it is more than sufficient.

3.1 Integration Workflow Breakdown

The workflow can be broken down into three main parts: Account Registration & Verification, API Key Generation & Management, and Client Configuration.

Step 1: Account Registration and Verification

Open the platform’s portal and register using an email. The process is typically: enter email and password → submit → check your inbox for a verification code and enter it → set an account name. There are generally no pitfalls here.

Pay special attention to the account verification phase. Many platforms require an additional identity verification step after registration (usually via phone number). If you skip this, subsequent key generation or API calls might be blocked. Standard phone numbers generally work; just ensure you select the correct country code. It is recommended to complete this step immediately after registration to avoid permission issues during debugging later.

Step 2: Generate API Keys

After logging in, navigate to the personal account settings, find the API Key management page, and generate a new key. Here are a few best practices:

• Naming Convention: Give the key a descriptive name indicating its purpose, such as selection-test-2025, to facilitate future identification and cleanup.
• Expiration: For the selection testing phase, set it to long-term validity (some platforms offer a “never expire” option) to prevent the key from failing mid-test. However, for production environments, always set a reasonable expiration cycle and rotate keys regularly.
• Secure Storage: The key is usually only displayed in full once at generation and cannot be viewed again. Immediately store it in a password manager or as an environment variable; do not hardcode it into your repository.

The generated key is typically a string with a fixed prefix. The permissions for this key usually cover all available models on the platform, meaning once you have it, you can freely switch among your candidate models.

Step 3: Client Configuration

For the client, choose a chat tool that supports custom API endpoints. The configuration logic is universal, centering around three parameters:

API Key:  <your-key-string>
API URL:  <unified-inference-endpoint-provided-by-platform>
Model ID: <provider-name/model-name identifier>

Model ID naming usually follows the “provider/model-name” convention. For instance, a flagship model from provider A might be identified as provider-a/flagship-1, while a long-context model from provider B might be provider-b/long-ctx-2. Always refer to the platform’s official documentation or console for the exact IDs; do not guess.

Once configured and saved, you can start a conversation. It is recommended to first test the connectivity with a simple prompt (e.g., asking it to write a quicksort algorithm) to ensure the pipeline works before entering formal evaluation.

3.2 Techniques for Parallel Multi-Model Comparison

The most valuable action during the selection phase is having multiple models answer the exact same question simultaneously.

Most mainstream chat clients allow you to select multiple models within a single session, displaying their answers side-by-side or in columns. This is immensely beneficial for selection: using the same prompt and context, you can see the output differences across several models in seconds, offering a level of intuition that far exceeds individual testing.

The recommended approach is as follows:

1. Prepare a Unified Test Set: Categorize by business scenarios, such as 10 “code generation” prompts, 5 “document summary”, 5 “multi-turn instruction following”, and 5 “structured output (JSON)”. Keep the prompts fixed for each test case; do not modify them on the fly.
2. Batch Parallel Prompting: Send the identical prompt to all candidate models simultaneously and record the outputs.
3. Manual Scoring & Latency Tracking: Scoring dimensions should include at least: correctness, completeness, format compliance, and response speed. A simple spreadsheet with a row per model per question works well.
4. Focus on Failure Modes: More informative than “who answered best” is “how they failed”—was it a misunderstanding, a formatting deviation, or a mid-way truncation? Failure modes often determine a model’s viability in your business.

A quick reminder: some platforms allocate tight resources to newly listed models. High concurrency might lead to timeouts or queuing. Control the concurrency during testing—running 3 to 5 models in parallel is sufficient. Retry if necessary.

4. Deep Validation Phase: What Really Matters

Once the horizontal screening is complete and you have shortlisted 2–3 candidates, proceed to deep validation. In this phase, focus on evaluating the following dimensions:

1. Instruction Following and Format Stability: In production environments, model outputs are frequently parsed by downstream programs. If a model occasionally adds an explanatory sentence to a JSON output or misses a field, the downstream pipeline will throw frequent errors. The testing method: create 20 prompts strictly requiring JSON output and calculate the format compliance rate. This metric impacts engineering implementation more than “how good the answer sounds.”

2. Context Length and Practical Usability: The context window advertised by vendors is one thing; maintaining retrieval accuracy at long contexts is another. It is recommended to perform a “needle in a haystack” test: embed a specific fact in the middle of a long document and see if the model can accurately retrieve it. Recall rates in the latter half of the context window can vary drastically between models.

3. Tool Calling Protocol Support: If your business involves Agents or function calling, verify the completeness of the model’s support for tool calling formats. Does it support parallel calls? Can it maintain correct parameters across multiple turns? Can it self-correct erroneous parameters? For this part, read the platform’s official documentation on tool calling and write a few practical use cases to verify.

4. Latency and Cost: Record the time-to-first-token and total latency for each model on typical tasks. Estimate unit business costs based on the platform’s billing method (per-token vs. per-call). Note the pricing difference between input and output tokens—output is generally more expensive.

5. Version Stability and Update Cadence: Selection is not a one-time decision. Model versions update, and behaviors may change. Record the tested model version identifiers in your selection report, and establish a regression testing mechanism post-launch to rerun the evaluation set when versions update.

5. Common Pitfalls and Engineering Recommendations

Based on practical experience, here are a few easily overlooked issues to proactively avoid:

• Do not hardcode API keys into source code. Use environment variables or secret management services, and incorporate secret scanning into your CI pipeline.
• Be mindful of rate limits. Free tiers usually come with QPS limitations. Confirm the limits before stress testing to avoid triggering throttling, which might be misinterpreted as a service outage.
• Distinguish between “model unavailable” and “network unavailable”. During debugging, use a minimal request (like a single-turn short prompt) to verify connectivity before troubleshooting model-side issues.
• Preserve raw evaluation records. Archive outputs, latencies, and scores from every test. This provides a baseline for comparison when switching models or adjusting prompts later.
• Platform model availability fluctuates. Certain models might be temporarily delisted or replaced with different versions. Selection conclusions should note the testing date, and production environments should have a backup model plan.

6. Conclusion

Selecting a Chinese LLM is essentially an engineering problem of minimizing cost while maximizing information gain. The logical path is: leverage unified inference platforms to drive integration costs to the lowest point, use a standardized test set for horizontal screening, and then conduct deep validation on shortlisted models using actual business scenarios. The entire process requires no complex infrastructure—one account, one API key, and a client supporting parallel multi-model queries are sufficient to make the majority of your decisions.

What truly determines the quality of your selection is not leaderboard rankings, but whether you have conducted comparisons using your own business data, within your own scenarios, and via reproducible methods. By building an evaluation set and keeping records, model selection transforms from “going by feel” into an evidence-based engineering decision.