How to Choose a Chinese LLM: A Practical Evaluation Framework

Why Choosing a Model Is Getting Harder

Chinese large language models now enter the market at a relentless pace. Almost every month brings a new flagship release, with vendors making strikingly similar claims about reasoning, code generation, long-context handling, and agent capabilities. For developers and businesses, this creates a familiar dilemma: more candidates, harder decisions. There are three main reasons.

1. The gap within the top tier is narrowing. For general tasks such as comprehension, writing, and Q&A, the differences between leading Chinese models have become hard for ordinary users to perceive. The strategy of “just pick the strongest model” no longer works. What actually distinguishes one model from another is how it performs in your specific business scenario, not the benchmark scores shown at launch events.

2. Noise outweighs useful signal. Vendor announcements, self-media comparisons, and community opinions often contradict each other. Without a clear evaluation methodology, it is easy to be misled by a single source, turning model selection into a matter of “who to trust” rather than “what the tests show.”

3. The engineering maturity of Chinese models has improved. A notable industry signal is that mainstream model inference platforms, which can be understood as model marketplaces that package models into standardized inference services accessible via API, used to feature mostly overseas open-source models. Today, Chinese models are increasingly available on the same shelves, side by side with their international counterparts. The implication is clear: Chinese models now meet internationally accepted standards for API protocols and deployment engineering. The question is no longer whether they can be used, but how to choose and how to use them.

Using Benchmarks Correctly: A Filter, Not a Verdict

Third-party leaderboards, such as those focused on web development or code generation, are a good starting point. The mistake is treating them as the final answer.

How to use leaderboards correctly:

• Use them to quickly identify the top tier and narrow dozens of candidates down to three to five.
• Pay attention to consistency across multiple evaluation rounds. A model that stays near the top over time is more informative than one that tops a single leaderboard.

Three limitations of leaderboards:

1. Test set contamination. Public benchmark questions may leak into training data. A high score does not necessarily reflect real generalization ability.
2. Scenario mismatch. The top model on a web development leaderboard may not be the best at contract review, customer support, or data extraction. Leaderboards measure average capability, while businesses need capability under a specific distribution.
3. Statistical noise. When two models differ by only one or two percentage points, the ranking may be meaningless. Real-world experience often depends on stability, response speed, and other engineering details that leaderboards do not measure.

In short: leaderboards narrow the field. Your own business data makes the final call.

Building Your Own Evaluation System: Four Steps

3.1 Define a Task Profile

Break down your business requirements into a concrete task list with weights. For example, an intelligent customer service system might include: intent recognition (30%), multi-turn dialogue management (25%), domain knowledge Q&A (30%), and refusal and safety fallback (15%). Without this step, all subsequent evaluation is unfocused.

3.2 Build an Evaluation Set

Extract 200 to 1,000 samples from real business logs, historical tickets, and user queries. Requirements:

• Cover high-frequency cases as well as long-tail edge cases, such as very long inputs, typos, colloquial expressions, and adversarial prompts.
• Annotate each sample with a reference answer or scoring criteria.
• For objective tasks such as classification and extraction, use rule-based comparison for automatic scoring. For subjective tasks such as summarization and copywriting, define a multi-dimensional rubric covering accuracy, completeness, and tone, with multiple reviewers scoring independently.

The evaluation set is the most valuable asset in the entire selection process. It deserves more than half of your preparation time.

3.3 Control Variables and Run Comparative Experiments

The evaluation environment must control variables. Otherwise, the results are not trustworthy.

import json
import urllib.request
def run_case(endpoint, api_key, model, case):
    """Call a candidate model with unified parameters for a fair comparison"""
    payload = json.dumps({
        "model": model,
        "messages": [
            {"role": "system", "content": case["system_prompt"]},
            {"role": "user", "content": case["input"]},
        ],
        "temperature": 0,     # disable randomness for reproducible results
        "max_tokens": 1024,
    }).encode("utf-8")
    req = urllib.request.Request(
        endpoint,
        data=payload,
        headers={
            "Content-Type": "application/json",
            "Authorization": "Bearer " + api_key,
        },
    )
    with urllib.request.urlopen(req, timeout=30) as resp:
        data = json.loads(resp.read().decode("utf-8"))
    return data["choices"][0]["message"]["content"]
# Run the same evaluation set against each candidate model
for model in candidate_models:
    for case in eval_set:
        answer = run_case(endpoints[model], keys[model], model, case)
        save_result(model, case["id"], answer)

Most mainstream inference platforms now follow a common chat completion protocol, so switching candidate models usually only requires changing the endpoint and model name. This is the premise that makes the code above work. For evaluation, use double-blind scoring: reviewers should not know which model produced which answer, so that brand perception does not influence judgment.

3.4 Test Engineering Metrics at the Same Time

Beyond quality, the following metrics directly affect the production experience and should be measured in the same stress test:

• Time to first token (TTFT): determines the perceived responsiveness of interactive products.
• Throughput and concurrency: whether the service remains stable at the target QPS and whether rate limits are likely.
• Long-context behavior: whether recall and instruction following degrade noticeably as input length grows.
• Error rate and retry cost: the proportion of timeouts, truncations, and abnormal outputs.

Delivery Options: Direct API, Managed Platforms, and Self-Hosting

Model selection is not just about choosing a model. It also means choosing a delivery model. The three mainstream options have different trade-offs:

Dimension	Direct API	Managed platform (MaaS)	Self-hosted / on-premises
Time to start	Fastest	Fast	Slow; requires in-house operations
Cost model	Pay per use	Pay per use or annual plans	High upfront cost; lower marginal cost at scale
Data compliance	Depends on provider agreement	Dedicated resources may be available	Data stays fully within your environment
Suitable stage	Validation and small-to-medium scale	Rapid growth	Strict compliance or very large scale

Two practical notes: first, major model platforms such as Baidu AI Cloud typically offer a full path from direct API access and model fine-tuning to private deployment, which suits teams that want to move smoothly between delivery options. Second, self-hosting open-source models requires familiarity with deployment tuning, memory management, and batch scheduling in mainstream open-source inference engines. The operational cost is often underestimated. A pragmatic approach: use API access during validation, then calculate the cost crossover point between API and self-hosting once volume stabilizes. Many teams find that below a certain monthly call volume, a managed option is actually cheaper because it eliminates operational overhead.

Cost Estimation: Look Beyond the Unit Price

The per-token price is only part of the cost. What matters is the full picture:

• Separate input and output pricing: many platforms charge different rates for input and output tokens. Output costs can grow significantly in long-context scenarios.
• Context caching: for scenarios with fixed system prompts or repeated document prefixes, prefix caching can meaningfully reduce cost. Check whether the platform supports it.
• Total cost formula: monthly cost is approximately daily requests multiplied by average tokens per request multiplied by unit price multiplied by 30. Reserve an extra 10% to 20% for failed requests and retries.

Also set a latency budget. Interactive products should include time to first token as an acceptance metric, while batch offline tasks should focus more on throughput. Quality, latency, and cost form an impossible triangle. The essence of model selection is finding the best trade-off for your business within that triangle.

Beyond Single-Model Selection: Toward an Architectural Approach

Mature teams no longer rely on a single model. Instead, they incorporate multiple models into a schedulable architecture:

• Model routing: route simple tasks such as classification and extraction to lightweight models, and reserve flagship models for complex reasoning and generation. Overall cost can often drop by 30% to 50%.
• Degradation and failover: when the primary model times out or hits rate limits, a gateway automatically switches to a backup model to maintain availability.
• Unified abstraction layer: add a thin gateway between business code and model APIs to hide platform differences. Changing models becomes a configuration update rather than a code change.

The benefit of this architecture is long-term. Models iterate on a monthly basis. Today’s best choice may be displaced within three months. A team with the ability to switch at any time can always capture the best available option at the moment.

Pre-Launch Compliance Checklist

When deploying generative AI applications, three bottom lines must be confirmed:

1. Service compliance: generative AI services intended for public use must complete the required filings, and the chosen service provider should have the appropriate qualifications.
2. Data security: for industries involving personal sensitive information or trade secrets, such as finance, healthcare, and government, prioritize private deployment.
3. Content safety: confirm that the provider offers content safety filtering, and test its false-positive rate empirically. Filtering that is too loose creates risk, while filtering that is too strict hurts legitimate business.

A Decision Checklist

The methodology above can be compressed into a checklist, so that the final decision comes from a process rather than intuition:

• Business task profile is clear, with explicit weights for each task
• Evaluation set is ready (at least 200 real samples, including edge cases)
• Initial leaderboard screening is complete, with candidates narrowed to 3-5
• POC testing is complete: quality, latency, and concurrency all meet targets
• Cost estimation is complete, including caching and redundancy factors
• Delivery option satisfies compliance and data security requirements
• Backup models and failover plans are in place

Final Thoughts

Back to the original question: which Chinese LLM should you choose? The responsible answer is: decide after testing with your own data. Leaderboards provide candidates. The evaluation set provides the verdict. Architecture provides an exit strategy. Selection is not a one-time destination but a quarterly review cycle. Models evolve, businesses shift, and prices fluctuate. The only assets that do not depreciate are your accumulated evaluation system and your ability to switch when needed. Hold on to those two things, and the question of “which one to choose” will never be a problem again.