Evaluating Chinese LLMs: A Practical Guide to Multi-Model Comparison and Free API Usage

1. The Challenge: Choosing the Right Chinese LLM

Over the past year, Chinese LLMs have entered a period of rapid iteration. Major vendors have released their next-generation flagship models around the same time, with capabilities generally reaching the first tier. The performance gap in general conversation, code generation, long-context understanding, and Agentic tasks is steadily narrowing. For developers, this is both a blessing and a curse: having too many models makes it difficult to choose.

The traditional approach is to register on each vendor’s open platform separately, apply for different API keys, integrate different interfaces, and write individual scripts for stress testing. The cost of this workflow is significant:

• Repetitive registration and identity verification processes consume time;
• Inconsistent SDKs and interface protocols require multiple sets of adaptation code;
• Scattered free quotas with varying rules make fair horizontal comparison difficult;
• Some platforms require prepayment for full access, increasing trial-and-error costs.

A more efficient approach is to leverage a unified model inference platform to test multiple candidate models horizontally using a single interface protocol and API key. Once a clear conclusion is reached, you can decide whether to deeply integrate a specific vendor. This article focuses on this approach, providing a practical and actionable solution.

2. The Value of Unified Inference Platforms

A unified inference platform essentially acts as a “model supermarket.” The platform provider encapsulates models from different sources into standardized inference services, exposing a unified HTTP interface and authentication method. Developers only need one account, one API Key, and one Base URL to call all models listed on the platform.

These platforms typically share the following characteristics:

1. Unified Interface Protocol: Most platforms adopt request formats compatible with mainstream open interfaces (such as the Chat Completions style), meaning existing client tools, SDKs, and scripts can be integrated with little to no modification.
2. Centralized Model Catalog: The list of available models is continuously updated. While early catalogs focused on overseas open-source models, the addition of Chinese models has accelerated significantly over the past two years, and flagship models from the first tier are generally available.
3. Available Free Quotas: Most platforms offer free invocation quotas for individual developers for evaluation and small-scale trials, providing a low-cost basis for model comparison.
4. Resource Contention: Free resources often have concurrency or rate limits. During peak hours, responses may slow down or become temporarily unavailable. This is sufficient for personal testing but not suitable as the sole dependency in a production environment.

It is important to clarify that these platforms are suitable for evaluation and prototype validation, not as the core of a long-term production architecture. For production environments, factors such as SLA, billing models, data compliance, and regional availability must be comprehensively considered.

3. Practical Workflow Overview

The entire process can be broken down into three independent and sequential stages:

Register Account -> Generate API Key -> Configure Client -> Multi-Model Comparison

Below is a step-by-step breakdown with common pitfalls highlighted.

3.1 Account Registration and Verification

1. Visit the platform’s official website and navigate to the registration page;
2. Fill in the required credentials (e.g., email and password) to create an account;
3. Check your email for the verification code (usually a 6-digit number) and complete the email verification;
4. Set up your account name to finish the basic registration.

Key Note: After registration, there is typically a separate verification step requiring phone number validation. This step cannot be skipped, otherwise, subsequent attempts to generate keys or call APIs may be blocked. This is the most easily overlooked step in practice.

3.2 Generating and Managing API Keys

After logging in, navigate to the API Keys management page via your profile menu:

1. Click to generate a new key;
2. Give the key an identifiable name (naming by purpose is recommended for easier auditing);
3. Set the expiration time to “Never” or the longest available period;
4. Once generated, you will receive a string with a specific prefix. Copy and save it immediately.

The permissions for this key usually cover all free models listed on the platform, including the Chinese models discussed in this article. Security best practices recommend:

• Do not hardcode keys into code repositories; use environment variables or local configuration files for injection;
• Use different keys for different purposes to allow for individual revocation;
• If a key is compromised, delete it immediately in the console and generate a new one.

3.3 Client Configuration

It is recommended to use a desktop client that supports multi-model parallel conversations. Configuration typically involves only three items:

Configuration Item	Description
API Key	The key string generated in the previous step
API Address	The unified inference endpoint URL provided by the platform
Model ID	The identifier for the model listed on the platform, filled in as needed

Model IDs generally follow a vendor/model-name naming convention, with distinct identifiers for flagship conversation models, long-context models, and multimodal models. Note: The model versions listed on the platform may lag behind the vendor’s latest release. If the latest version of a model is not yet available, use the previous version for capability evaluation and re-test once the platform updates.

Once configured, you can start chatting. The core advantage of such clients is multi-model side-by-side comparison: select multiple models at the bottom, set the response layout to horizontal, and ask a single question to view the outputs of multiple models in parallel. This is highly effective for subjective quality assessment.

4. How to Conduct Scientific Model Comparisons

Getting the models running is just the first step; the evaluation method truly determines the selection. It is recommended to design comparison experiments across the following dimensions.

4.1 Capability Dimensions

• General Conversation and Instruction Following: Give the same set of instructions and observe whether the model accurately understands constraints (e.g., word count limits, formatting requirements, persona settings).
• Code Generation and Debugging: Use the same algorithm problem or code snippet requiring refactoring, and compare the correctness, readability, and comment quality of the generated results.
• Long-context Processing: Input the same long document to test summarization, retrieval, and cross-paragraph reasoning performance.
• Structured Output: Require the model to output strict JSON to observe format stability and field completeness, which is a high-frequency requirement for engineering implementation.

4.2 Engineering Dimensions

• First Token Latency and Throughput: Take the median of multiple samples under the same network environment to avoid being misled by single-run jitter.
• Context Length Support: Confirm the actual maximum input length the model can accept.
• Rate Limiting: Concurrency and QPS limits under free quotas directly determine whether batch testing is feasible.
• Interface Compatibility: Whether it supports common features like streaming output, function calling, and system prompts.

4.3 Evaluation Methodology Recommendations

1. Fixed Test Set: Prepare 20–50 prompts covering actual business scenarios to form a reusable evaluation set;
2. Blind Evaluation: Hide model names during comparison to reduce inherent bias;
3. Multiple Sampling: Generative tasks involve randomness; run the same prompt at least 3 times to observe stability;
4. Record Costs: Even if currently free, record token consumption to provide a basis for cost estimation when switching to a paid plan later.

A simple evaluation record structure can look like this:

{
  "case_id": "code_001",
  "prompt": "Implement a concurrent request aggregation function with timeout control",
  "models": [
    {"name": "model_a", "latency_ms": 1200, "tokens": 860, "score": 4},
    {"name": "model_b", "latency_ms": 980, "tokens": 1020, "score": 5}
  ],
  "notes": "model_b output includes complete error handling, model_a lacks boundary checks"
}

5. A Framework for Model Selection Decisions

Once comparison data is obtained, selection should not be based solely on “who scored higher,” but rather on a weighted judgment combined with business constraints. It is recommended to prioritize the following:

1. Compliance and Data Security: Whether it meets the data residency requirements of your industry and supports private or dedicated cloud deployment;
2. Capability Match: Actual tested performance in core business scenarios, rather than leaderboard rankings;
3. Cost Structure: Input/output token pricing, tiered pricing availability, and whether long-context incurs extra charges;
4. Stability and SLA: The production environment must have a clear Service Level Agreement commitment;
5. Ecosystem and Toolchain: SDK completeness, observability support, and integration cost with existing tech stacks.

For Chinese LLMs, there is a practical advantage worth considering: the quality of understanding and expression in Chinese contexts, as well as the adaptability to domestic business scenarios (such as Chinese document formats and localized expression habits), which usually performs more stably in real-world tests. Furthermore, if the team already uses Baidu AI Cloud, you can further evaluate the synergy between its model services and existing cloud resources. For example, combining it with general cloud products like object storage, logging services, and monitoring can reduce the complexity of cross-platform data transfer.

6. Common Issues and Precautions

• Authentication failure after registration: Prioritize checking whether the verification step was fully completed, and ensure the key was copied entirely (watch for leading/trailing spaces).
• Intermittent timeouts: Free resources are competitive, making this a normal occurrence. A retry mechanism with exponential backoff is sufficient; do not use this as a basis for judging model performance.
• Inconsistent model versions: Platform catalogs have update delays. Confirm the actual listed version of each model before comparing to avoid dismissing a new version’s capabilities based on old version results.
• Context mixing during multi-model comparison: Some clients pass conversation history to all models. For strict comparisons, open an independent session for each model to ensure identical inputs.
• Do not use free tiers for production: Free quotas are for validation. Production environments must switch to official services with SLA guarantees.

7. Conclusion

The first-tier Chinese LLM products are now capable and usable. The real challenge of model selection is not “whether it can be used,” but “how to efficiently validate and make evidence-based decisions.” By leveraging unified inference platforms, developers can use one key, one interface, and one client to complete horizontal comparisons of multiple candidate models within hours, shifting selection from “gut feeling” to “data-driven.”

The recommended implementation path is: use free tiers for capability profiling -> use a fixed test set for quantitative comparison -> make weighted decisions based on compliance, cost, and SLA -> finally, deeply integrate the target model’s production services. This workflow is equally applicable for rapid re-evaluation of future model releases, forming a sustainable model selection mechanism.