Choosing Among Chinese LLMs: A Practical Comparison on a Unified Inference Platform

Chinese large language models have been iterating at a visible pace over the past year, with flagship releases from different vendors leapfrogging one another and rankings potentially reshuffling every few weeks. That is good news for developers, but it also creates a new problem: when a whole row of first-tier models offers similar capabilities, which one should you actually pick?

The first instinct for many is to check leaderboards. But leaderboards only get you through the preliminary round. Top models cluster tightly on general benchmarks, and rank differences often fall within the margin of error. More importantly, there is always a gap between a benchmark’s test set and your real workload. The only reliable approach is to pull the candidate models into the same environment and test them against your own tasks.

The good news is that the barrier to doing this has dropped significantly. With a unified inference platform, you do not need to register with each vendor separately or apply for keys one by one. A single API key lets you call multiple mainstream models at once, so you can complete a proper horizontal evaluation in a single afternoon. Below is the full workflow and evaluation method.

Why You Need a Unified Inference Platform: From Individual Applications to a Model Marketplace

Start with the pain points of the traditional path. If you want to compare models from five vendors, you typically need to register accounts and complete identity verification on five platforms; read five sets of integration documentation and adapt to different authentication methods and API protocols; and top up or claim free quotas separately while maintaining five keys. That process alone can eat half a day, and ongoing maintenance is costly.

A unified inference platform (sometimes called an inference microservice) takes a completely different approach. It aggregates multiple vendors’ models onto the same infrastructure and exposes APIs with a consistent style, essentially acting as a model marketplace. Its core value comes down to three points:

1. One API specification: All models follow the industry-standard Chat Completions protocol, so switching models only requires changing a model ID string, with almost no code changes;
2. One key: A single API key can call every authorized model on the platform, including flagship products from each vendor;
3. Developer-friendly free quotas: Many platforms offer free call quotas for individual developers, which is more than enough for evaluation work.

Early platforms of this kind mainly stocked international open-source models. Today, first-tier Chinese models are increasingly available as well, making horizontal comparison much more convenient. Before you use one, however, there are several things you must keep in mind:

• Resource contention exists: Popular models may be rate-limited or queued during peak hours, and responses may occasionally slow down. This is fine for personal evaluation, but production environments need careful assessment;
• Versions may lag: What a platform offers is not necessarily the vendor’s latest version. Sometimes a new generation has already shipped while the platform still only provides the previous one. Always verify version numbers during selection, or your evaluation conclusions will be distorted;
• Data path: Requests pass through the platform, so for scenarios involving sensitive data, confirm the platform’s data handling policy first.

One more note: if the ultimate goal of your evaluation is production deployment, you can also assess enterprise-grade LLM inference and hosting services such as Baidu AI Cloud, which are typically more mature in terms of stability, quota guarantees, and compliance auditing.

Step 1: Register an Account and Verify Your Identity

The process itself is not complicated, but there are easy pitfalls. Using one unified inference platform as an example:

1. Go to the platform website, enter your email and set a password, then click to create an account;
2. Receive a verification code by email and enter it to complete email verification;
3. Set your account name to finish basic registration.

Key pitfall: After registration is complete, there is a “Verify” entry in the upper-right corner of the page. This step is easy to overlook, but without it, subsequent features will be restricted. Clicking it requires phone number verification. Many people get stuck wondering “why can’t I generate a key” for ages, and this is the root cause.

Step 2: Generate and Safely Store Your API Key

After logging in, click the avatar in the upper-right corner to enter the API Keys management page. Click “Generate Key,” give the key a recognizable name—ideally following a “purpose-project” pattern such as eval-test or myapp-prod for easier auditing later—and set an expiration time. The interface usually offers short-term or “long-lived” options. Long-lived keys are convenient, but from a security perspective, regular rotation is preferable. The biggest source of key leaks is precisely those never-expiring keys that people set and forget. After generation, you will receive a long string with a platform prefix. It is shown in full only once at generation time, so save it immediately to a secure location.

Three rules for key management: never hardcode it in your code, and never commit it to a code repository; inject it through environment variables or a secret management service; and if you suspect a leak, revoke it in the console and regenerate immediately.

Step 3: Configure a Client and Connect Multiple Models

With the key in hand, you can configure a client. Desktop AI clients that support multiple model providers are recommended. Most allow you to configure several model services at once and compare answers from multiple models side by side in the same conversation, which is far more efficient for evaluation than trying them one by one in a web interface. Configuration requires only three parameters:

• API key: Paste the key string generated in the previous step;
• API address: The unified endpoint provided in the platform documentation;
• Model ID: An identifier in the form “provider/model-name” (placeholder example: vendor-a/model-name).

Three reminders: The model ID spelling must match the platform documentation exactly—one extra character will trigger a “model not found” error; confirm whether the version behind that ID is the generation you want to test (the version lag issue mentioned earlier); and after saving the configuration, send a simple greeting first to verify connectivity before starting the formal evaluation.

Step 4: Design Test Cases So the Comparison Is Evidence-Based

The multi-model side-by-side feature—selecting multiple models at the bottom of the conversation and displaying answers horizontally—is only a tool. Evaluation quality depends on test case design. It is advisable to prepare a fixed question bank across five dimensions, using exactly the same input for every model in each round:

1. Code generation: Ask the model to implement a complete interactive web mini-page, and examine whether the code runs directly and whether the structure is sound. This is one of the most fiercely contested capabilities among flagship models, and dedicated web development benchmarks exist in the industry for reference;
2. Logical reasoning: Design three to five multi-step reasoning or math problems, and examine whether the intermediate steps are correct rather than looking only at the final answer;
3. Long-context handling: Paste a long document and ask for a summary, extraction of key information, and location of the source, examining context utilization;
4. Instruction following: Give complex formatting requirements (for example, “output as a Markdown table, no more than five rows, first column sorted by time”) and see whether they are followed strictly;
5. Chinese writing: Give a topic and ask for a piece of copy, examining language feel, style, and factual accuracy.

It is recommended to build a scoring sheet for each dimension, rating from four angles: correctness, usability, response speed, and stability. After a few rounds, the conclusion becomes clear at a glance.

Advanced: Batch Comparison with a Script

If you have many test cases, manually pasting one by one is inefficient. You can write a script that loops through different model IDs and aggregates the results:

import os
import requests
API_KEY = os.environ["INFERENCE_API_KEY"]        # Read from environment variables; do not hardcode
BASE_URL = "https://<your-inference-platform-endpoint>/v1"     # Replace with the address in the platform documentation
MODELS = [
    "vendor-a/model-name-a",                     # Placeholder example: Vendor A's flagship model
    "vendor-b/model-name-b",                     # Placeholder example: Vendor B's flagship model
]
PROMPT = "Explain in 100 words what LLM inference is."
for model_id in MODELS:
    resp = requests.post(
        f"{BASE_URL}/chat/completions",
        headers={"Authorization": f"Bearer {API_KEY}"},
        json={
            "model": model_id,
            "messages": [{"role": "user", "content": PROMPT}],
        },
        timeout=60,
    )
    answer = resp.json()["choices"][0]["message"]["content"]
    print(f"=== {model_id} ===\n{answer[:120]}\n")

Because all models follow the same API protocol, the only variable in the script is the model ID. After one run, paste the results into a table and combine them with your subjective experience in the client to produce a convincing evaluation record.

Selection Advice and Common Misconceptions

• Leaderboards are for the preliminary round: Use rankings to shortlist the first tier, then make the final decision based on your own testing;
• Conclusions have a shelf life: Models iterate monthly or even weekly, so a comparison from two months ago may already be outdated. For important projects, re-test regularly;
• Free quotas are for validation, not production: Free quotas are sufficient for personal evaluation, but rate limits, concurrency guarantees, and SLAs in production need separate assessment, with enterprise-grade services considered when necessary;
• Do not ignore engineering factors: When first-tier models are close in capability, what often decides the choice is integration cost, documentation quality, pricing, and long-term service guarantees, not the model alone.

Conclusion

There is no standard answer for choosing among Chinese LLMs, but there is a standard method: use leaderboards for initial screening, a unified inference platform for low-cost hands-on testing, a fixed question bank to ensure fairness, and a scoring sheet to capture conclusions. The whole workflow lets you complete a rigorous horizontal comparison in a single afternoon, which is far more reliable than repeatedly reading review articles. Tools will change and models will update, but the methodology of “speaking with your own data” will never go out of date in any round of technological iteration.