Choosing a Chinese LLM in Practice: A Unified Access and Side-by-Side Evaluation Guide

1. Background: Why Model Selection Has Become an Engineering Problem

Over the past year, the iteration pace of Chinese large language models has been impossible to miss: general conversation, code generation, long-context understanding, agent tool calling—a new flagship appears almost every quarter. For developers this is good news, but it also creates three very concrete pain points:

1. High per-vendor registration cost. Every model vendor has its own console, its own API key, and its own billing system. To fully try out several first-tier models, you can easily spend half a day just on registration and configuration.
2. Inconsistent leaderboard criteria. The same model can rank very differently on general benchmarks, code leaderboards, and long-context leaderboards. Leaderboards rarely answer the question “which one should I use for my scenario.”
3. High risk of deciding by impression. Onboarding into production based only on demos and marketing copy has a non-trivial chance of failure.

A more reasonable approach is to treat model selection as an engineering problem: first build a unified access and parallel comparison environment, then let your own evaluation set speak with data. This article provides a complete, actionable workflow.

2. Overall Approach: Three Access Paths and What to Choose During Evaluation

Depending on your purpose, there are roughly three paths to obtaining model capabilities:

1. Model aggregation inference platforms (the “model marketplace” model). These platforms host open models from multiple vendors in one place, offering a unified calling protocol and key system, with some models providing free usage quotas. The advantage is “one key opens many doors,” which is especially suitable for individual experimentation and side-by-side evaluation. The downside is resource contention during peak hours, and model versions may lag behind official releases.
2. Official vendor APIs or cloud-hosted services. This is the first choice for production. Mainstream cloud platforms (including Baidu AI Cloud) provide LLM hosting and invocation services with better stability and SLAs, and make it easy to integrate directly with object storage, message queues, logging, and monitoring/alerting components.
3. Local or private deployment of open weights. Suitable for highly sensitive data scenarios, but requires your own compute and operations capabilities.

The conclusion is clear: use an aggregation platform to get through evaluation quickly, then migrate to official hosted services once the selection is settled. The rest of this section focuses on how to set up the evaluation stage.

3. Four Steps to Build a Unified Evaluation Environment

Step 1: Register an account and complete all verifications

Registration flows on aggregation platforms are broadly similar: open the official site, register with an email, enter the email verification code, and set an account name. But there are two easy-to-miss pitfalls:

• Missing secondary verification. Many platforms have a separate “verification” entry tucked away in a corner of the page, requiring a phone number binding before full invocation permissions are unlocked. Skip this step and subsequent API calls will keep returning permission errors—a very subtle problem.
• Phone number availability. Mainstream platforms generally support +86 number verification, and a few platforms restrict virtual number ranges. It is worth confirming before you start testing.

Step 2: Generate and manage API keys

After logging in, go to the key management page and click generate. Two settings deserve attention:

• Key naming. Name keys by “purpose + date,” for example eval-20250201, to make future auditing and rotation easier.
• Validity period. For evaluation purposes you can set a longer period to avoid frequent renewal; but for production, follow the principle of least privilege, set a reasonable validity period, and rotate regularly.

After generation you get a key string with a specific prefix. It is usually shown in full only once at generation time, so save it immediately to a password manager or environment variable. This key typically grants access to all models enabled on the platform—very broad permissions. If leaked, it is equivalent to handing over your entire account. Never hardcode it into a code repository or commit it to version control.

Step 3: Configure a unified client

A desktop client that supports “multi-model parallel conversation” is recommended. Selection criteria: compatibility with the industry-standard chat completion protocol and support for custom model IDs. Configuration requires only three parameters:

• API key: paste the key you just saved;
• API base URL: the unified inference endpoint provided by the platform;
• Model ID: usually in the form “org-name/model-name,” which can be found on the platform’s model list page.

A typical configuration structure looks like this (placeholder example):

provider: some-model-aggregation-platform
api_key: ${MODEL_HUB_API_KEY}   # read from environment variables, never hardcode
base_url: https://<platform-domain>/v1
models:
  - org-a/model-a    # vendor A's flagship chat model
  - org-b/model-b    # vendor B's code-enhanced model
  - org-c/model-c    # vendor C's long-context model

On the protocol side, the de facto industry standard is a common chat completion interface (chat/completions), followed by most aggregation platforms and clients, so migration cost is minimal. Once configured, you can verify connectivity by sending a POST request to the inference endpoint with any HTTP client. Authentication uses a Bearer token, and the core request body fields are as follows:

{
  "model": "org-a/model-a",
  "messages": [
    { "role": "user", "content": "Implement an LRU cache in Python and analyze its time complexity" }
  ],
  "temperature": 0.2
}

Step 4: Run multiple models in parallel

Once configured, check multiple models at the bottom of the client to have them answer the same question simultaneously, with answers arranged side by side. This “same question, side-by-side” approach is far more efficient than switching between models one by one, and it effectively avoids anchoring bias—what you see is always a direct comparison under identical input, not a vague impression formed days apart.

4. How to Compare Scientifically: A Lightweight Evaluation Methodology

With a parallel comparison environment in place, the next question is “how to compare.” Three recommendations:

1. Treat leaderboards as reference, not conclusion. Public leaderboards focused on web development and coding ability, plus general capability benchmarks, reflect community reputation and rough tiers, but their evaluation criteria often diverge from your real scenario. The right way to use leaderboards is to shortlist 3–5 candidates, not to draw conclusions directly.
2. Build your own evaluation set. Extract 20–50 typical tasks from real business scenarios, covering the capabilities you care about most: code generation and bug fixing, long-document summarization, structured output (JSON, tables), Chinese semantic understanding, and multi-turn instruction following. Fix the prompts and temperature parameters so all candidate models answer the same questions under controlled variables.
3. Quantify with unified scoring dimensions. You can refer to a weight table like this:

Dimension	Suggested weight	Focus
Correctness	40%	Is the result correct and logically consistent
Instruction and format adherence	20%	Does it strictly output JSON or Markdown as required
Completeness	15%	Does it cover all requirement points, without lazy truncation
Hallucination rate	15%	Does it fabricate nonexistent APIs or facts
Engineering metrics	10%	First-token latency, throughput, context window size

Scoring can be done manually, or you can have a “judge model” do batch initial scoring with manual spot-check review. Twenty tasks times five models takes only an hour or two to produce fairly convincing comparative data.

5. Common Pitfalls and Caveats

Based on hands-on experience, a few reminders:

• Free resources fluctuate. Free quotas on aggregation platforms are in high demand; during peak hours you may encounter queuing, rate limiting, or occasional timeouts. This is entirely sufficient for individual evaluation, but do not infer production performance from it—that requires stress testing on official hosted services.
• Watch version currency. Model versions hosted on a platform may lag behind the vendor’s latest release. Confirm the specific version number on the platform before selecting, to avoid misjudgment from “testing the old version but deploying the new one.”
• Keep results reproducible. Record the model version, parameters, and prompts for each evaluation. Otherwise, when you re-test a month later, you cannot explain whether the data difference comes from the model or from the configuration.
• Keep key security front and center. Use environment variables or a key management tool; do not write keys into configuration files committed to a repository. If a leak is discovered, revoke and reissue immediately.

6. Scenario-Based Decision Framework

Finally, a decision reference organized by scenario:

• Code and engineering tasks: shortlist candidates using code-oriented leaderboards first, then test with real tasks from your own project, focusing on “first-pass success rate” and bug-fixing ability.
• Long-document processing: verify context window size first, then test summarization faithfulness with real long documents, and watch for implicit laziness such as detailed openings and rushed endings.
• Chinese semantics and format adherence: first-tier Chinese models are generally strong here; differences show up more in output style and format stability, and structured output tasks expose gaps most easily.
• Cost-sensitive production scenarios: after settling on candidates, compare each vendor’s official pricing and free quotas. If your business already runs on a particular cloud, prefer that cloud’s hosted model service (for example, the model services offered by Baidu AI Cloud), which avoids the extra cost of cross-cloud networking, authentication, and monitoring integration, and connects seamlessly with your existing architecture.

7. Final Thoughts

There is no standard answer to “which Chinese LLM should I choose,” but there is a standard process: unified access, parallel comparison, data-driven decisions. Use an aggregation platform to drive evaluation cost close to zero, use a custom evaluation set to turn selection from “gut feeling” into “looking at data,” and finally migrate to official hosted services for stable operation. This approach is not tied to any specific product: tools will change and models will change, but the process can be reused long-term. The next time a new model is released, simply add it to your configuration list, run your evaluation set, and the answer will emerge on its own.