How to Choose a Chinese LLM: Evaluation Framework and Quick Integration Guide

Introduction: Why Model Selection Has Become Harder

Over the past two years, Chinese LLMs have iterated at a breakneck pace. Flagship models from different vendors trade places on public leaderboards monthly, release presentations sound nearly identical—“reasoning on par with frontier models,” “significantly better code generation,” “context window doubled again.”

For developers and engineering teams, the problem has become more difficult, not less. With so many models and such similar marketing claims, which one should you actually use?

Instead of parsing promotional language, it helps to return to engineering fundamentals. Model selection is essentially a process of “evaluation framework plus low-cost empirical testing.” This article breaks it down into three steps: clarifying your business scenario, building an evaluation framework, and running horizontal tests through a unified API. It also provides a directly reusable configuration workflow and code examples.

Step 1: Define What You Need the Model to Do

The starting point for selection is not the leaderboard—it is your own business. Different scenarios place vastly different demands on model capabilities. Blindly pursuing the “strongest flagship” often means paying for capabilities you will never use.

Common scenarios can be roughly divided into five categories:

1. General conversation and Q&A: Customer service, knowledge base retrieval, and question answering. Key focus areas are Chinese language understanding, instruction following, and clear refusal boundaries.
2. Code generation assistance: Developer productivity. Code completion, function generation, and unit test writing quality matter most.
3. Long-document processing: Contract review, research report summarization. Context window length and information recall over long texts are hard requirements.
4. Agents and tool calling: Automated workflows. Reliable function calling and multi-step reasoning are essential.
5. Multimodal understanding: Image and chart parsing. This requires native multimodal architecture.

We recommend writing a task list based on the typical tasks in your own scenario—this list will later become your evaluation set.

Step 2: Build a Three-Dimensional Evaluation Framework

Once your scenario is clear, score candidate models across three dimensions.

Capability

• General ability: Be cautious when referencing public benchmark leaderboards. When scores are close, the real-world experience gap may be much smaller than the score gap suggests. Also be aware of “benchmark gaming”—some models perform well on leaderboard tasks but are inconsistent in real business use.
• Code ability: Test directly with real engineering tasks, e.g., “Implement a thread-safe LRU cache with a time-to-live and add unit tests.”
• Context handling: Distinguish between “nominal window” and “effective window.” Some models claim support for hundreds of thousands of tokens, but information recall degrades noticeably on very long inputs. Use a “needle in a haystack” test (hide a key fact in a long document and then ask about it) to verify.
• Instruction following: Whether the model can reliably output structured JSON or Markdown. This directly affects downstream parsing success rates in production pipelines.

Engineering

• Latency: Distinguish between time-to-first-token (drives interactive experience) and overall throughput (drives batch task efficiency).
• Concurrency and rate limits: Free or low-cost tiers often come with strict concurrency restrictions. Confirm limits before load testing.
• API compatibility: Whether the model is compatible with the industry-standard chat/completions protocol and function calling format. This determines whether your client code can switch models smoothly.
• Version management: Whether the model identifier includes a version number and whether upgrades come with deprecation notices. This affects production stability.

Cost and Compliance

• Pricing model: Mainstream pricing is per token for input and output. Note whether cache hits receive discounts.
• Free quota: Whether it is sufficient for a meaningful round of evaluation.
• Data compliance: For enterprise scenarios, confirm whether data will be used for training and whether private or dedicated cloud deployment is supported.

Step 3: Use a Unified API for Horizontal Comparison

Registering on every vendor site, reading every document, and writing adapters for each is the most time-consuming part of the evaluation phase. A more efficient approach is to use a model aggregation inference service.

There are hosted inference platforms that unify multiple open-source models behind a single, standard chat/completions-compatible API. For developers, this brings three direct benefits:

1. One key, many models: A single API key can call all hosted models on the platform, eliminating per-vendor registration.
2. Zero adaptation cost: Client code is written once. Switching models only requires changing a model ID string.
3. Low-cost trial: Most aggregation platforms offer free call quotas for hosted models (usually rate-limited), which is sufficient for small-scale evaluation.

One caveat: model versions on aggregation platforms may lag behind the latest official releases, and new models take time to appear. Always record “which version you tested” during evaluation, and re-validate through the official channel before production deployment.

Step 4: Practical Workflow—From Registration to First Request

Using a typical aggregation inference platform as an example, the standard workflow has four steps.

Register and Verify

Visit the platform’s console registration page, sign up with your email, enter the verification code, and set an account name. Two details to note:

• Some platforms provide an additional identity verification entry on the page. Skipping it may prevent you from generating keys or may limit API access.
• Phone verification typically supports international numbers. If you do not receive the SMS, check whether the number format includes the wrong country code.

Generate an API Key

After logging in, go to the “API Keys” or “Access Credentials” page and generate a new key:

• Give the key a descriptive name, e.g., eval-testing.
• Set the expiration period to “no expiration” if available, to avoid key failure mid-evaluation.
• The generated key is a long string with a fixed prefix. It is usually shown only once. Store it immediately in a password manager or environment variable. Never hardcode it in source code or commit it to a repository.

Configure the Client

Two typical approaches:

Option 1: Desktop chat client. Most mainstream chat clients support custom model endpoints. You only need to fill in three fields: API key (paste the generated string), API base URL (the inference endpoint from the platform docs), and model ID (usually in the format “vendor/model-name”). After saving, you can switch models in the conversation window and start chatting.

Option 2: Write code directly. See the next section.

Verify Connectivity

Send a simple message first (e.g., “Hello, please introduce yourself”) to confirm a normal response, then move on to formal evaluation.

Step 5: Code Example—Batch Horizontal Comparison

Below is a self-contained test script based on the Python standard library. It works with any platform that is compatible with the chat/completions protocol:

import json
import time
import urllib.request
API_KEY = "YOUR_API_KEY"                       # Recommended: read from an environment variable
BASE_URL = "https://<platform-inference-endpoint>/v1/chat/completions"
CANDIDATE_MODELS = [
    "vendor-a/model-a",
    "vendor-b/model-b",
    "vendor-c/model-c",
]
TEST_CASES = [
    {"id": "code",   "prompt": "Implement a thread-safe LRU cache and include a complexity analysis."},
    {"id": "json",   "prompt": "Extract names, dates, and amounts from the given text. Output strictly as JSON."},
    {"id": "reason", "prompt": "A multi-step logical reasoning problem..."},
]
def chat(model, prompt):
    payload = json.dumps({
        "model": model,
        "messages": [{"role": "user", "content": prompt}],
        "temperature": 0.2,
    }).encode()
    req = urllib.request.Request(
        BASE_URL, data=payload,
        headers={"Authorization": f"Bearer {API_KEY}",
                 "Content-Type": "application/json"},
    )
    with urllib.request.urlopen(req, timeout=120) as resp:
        return json.loads(resp.read().decode())
for model in CANDIDATE_MODELS:
    for case in TEST_CASES:
        start = time.time()
        result = chat(model, case["prompt"])
        elapsed = time.time() - start
        content = result["choices"][0]["message"]["content"]
        tokens = result.get("usage", {}).get("completion_tokens", 0)
        print(f"[{model}] case={case['id']} "
              f"elapsed={elapsed:.1f}s output_tokens={tokens}")

Running this once gives you three pieces of first-hand data for each model: response content, latency, and output token count—directly supporting cost and performance estimates.

Engineering recommendations:

• Fix sampling parameters (e.g., temperature uniformly set to 0.2) to ensure reproducible results.
• Run each case three times per model and take the median to filter out transient fluctuations.
• Record failures and rate-limit errors (e.g., HTTP 429). In practice, free tiers may experience queuing or slower responses during peak hours. This is acceptable for personal evaluation, but formal load testing requires careful assessment.

Step 6: Advanced—Build Your Own Evaluation Set

General impressions are unreliable. The scientific approach is to accumulate your own evaluation set:

1. Sample: Extract 50–100 representative requests from real business logs, covering hard cases and edge cases.
2. Define acceptance criteria: For each sample, define an ideal output or an automatically checkable rule (unit test pass rate, JSON structure validation, keyword matching).
3. Blind review: Mix outputs from different models without identifiers, then have people familiar with the business score them. This avoids brand bias.
4. Automate: Once the evaluation set is fixed, extend the script above into a scheduled task. Re-run it whenever model versions change to continuously monitor capability drift.

Step 7: From Evaluation to Production

After evaluation narrows down the candidate model, four items remain before going live:

• Deployment model: For non-sensitive data, call a cloud-hosted model inference service directly (e.g., model service platforms offered by major cloud providers). For sensitive data, evaluate private deployment.
• Observability: Integrate logging and monitoring alerts to track latency, error rate, and cost continuously.
• Resilience design: Abstract the interface layer and keep the ability to switch to a backup model to avoid deep coupling to a single model version.
• Cost accounting: Use the token consumption measured during evaluation to estimate monthly costs and set budget alerts.

Step 8: Pitfalls to Avoid

• Leaderboard-only thinking: Leaderboards are a screening tool, not a decision basis.
• API key leakage: A key committed to a repository is a gift to anyone who finds it.
• Version drift: Models can change behavior after silent upgrades. Pin versioned model identifiers in production.
• Ignoring rate limits: “Free quota works” and “free quota is enough for load testing” are two different things.
• Too-small sample size: Drawing conclusions from one or two test runs is statistically meaningless.

Conclusion

The “difficulty of choosing” among Chinese LLMs is essentially a decision problem caused by information overload. The solution is not mysterious: clarify your scenarios, quantify your evaluation dimensions, and use a unified API for low-cost empirical testing. Let data replace marketing claims. Once this workflow is run once, every subsequent model iteration can be re-evaluated within a day or two—which is far more valuable than remembering which vendor claims to be the latest and greatest.