Choosing a Chinese LLM: Five Key Dimensions and a Practical Evaluation Framework

Navigating the LLM Landscape: Why Selection is Harder Than Ever

Over the past couple of years, the release cadence of Chinese LLMs has accelerated dramatically. Flagship models now iterate within months, with multiple premium models launching simultaneously. In benchmarks for coding and mathematical reasoning, these models have consistently reached the top tiers. Concurrently, various inference platforms have evolved into “model supermarkets.” While these shelves were once dominated by overseas open-source models, Chinese models now hold a significantly larger share. They can be accessed directly via standard HTTP APIs, often without complex application workflows.

While this abundance is a net positive, the paradox of choice creates new decision costs for technical leaders:

• Inconsistent benchmarks: Different leaderboards use varying metrics, causing the same model to rank differently across lists.
• Lack of third-party validation: Performance data shared during launch events is typically self-reported by vendors.
• The “average score” trap: While general capabilities have improved across the board, businesses usually need a “specialist” for a specific task rather than a generalist.
• Rapid obsolescence: Conclusions drawn today may become irrelevant in three months due to rapid model updates.

Instead of asking, “Which model is the strongest?” a more effective question is: Which model best fits your scenarios, your data, your engineering constraints, and your budget? Below is an engineering-driven methodology for LLM selection.

How to Use Leaderboards: A Screening Tool, Not a Decision Maker

Evaluation leaderboards—whether for general capabilities or domain-specific tasks like coding and math—still hold value, but you must understand their boundaries.

What they can answer:

1. Narrowing the candidate pool: Top-tier models generally have no glaring flaws in general capabilities, allowing you to reduce a list of dozens to a shortlist of three to five.
2. Observing capability trends: Tracking improvements in coding or long-context handling across recent generations helps determine if an upgrade is worthwhile.
3. Identifying model tiers: Differentiating between flagship, lightweight, and scenario-specific variants.

What they cannot answer:

1. Performance on your data distribution: Leaderboard prompts are sourced from public corpora and standard test sets, which rarely match your business Q&A, code styles, or document structures.
2. System integration compatibility: Performance in Retrieval-Augmented Generation (RAG), tool calling, and multi-turn dialogue orchestration cannot be measured by static benchmarks.
3. Real-world production metrics: Latency, throughput, and concurrency stability must be stress-tested in your actual pipeline.

A practical principle: Use leaderboards to make the shortlist, but use business evaluations to make the final decision.

Five Key Dimensions for Model Selection

1. Task Matching: Define the Model’s Role
Before evaluating models, draft a one-page requirement profile. Is the core task code generation, multi-turn customer service, document summarization, or structured information extraction? What are the input/output formats? Do you need long-context windows or multimodal capabilities? What is your tolerance for hallucinations? (Writing creative copy versus drafting contract clauses requires entirely different thresholds.) Different tasks demand different peak capabilities: coding scenarios should test real-world engineering code completion and debugging rather than algorithmic puzzles, while customer service requires strict instruction adherence and appropriate refusal boundaries.

2. Custom Evaluation Sets: Base Decisions on Your Data
This is the most cost-effective step in the entire process. The implementation is straightforward: extract 100 to 300 samples from real business data, covering typical scenarios, edge cases, and known bad cases. Prepare an ideal answer or a verifiable scoring rule for each sample. Call candidate models in batches using a unified system prompt and a fixed low temperature parameter to ensure reproducibility. Finally, conduct a blind manual review or use rule-based plus model-assisted scoring to record the pass rate.

Here is a reference implementation (the interface style follows the industry-standard chat completion format, adaptable to most inference platforms):

import requests
ENDPOINT = "https://<inference-service-endpoint>"  # Fill according to your chosen platform's API docs
API_KEY = "your_key"
def ask(model_id, system_prompt, question, temperature=0.1):
    payload = {
        "model": model_id,
        "messages": [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": question},
        ],
        "temperature": temperature,  # Fix randomness during evaluation to ensure reproducibility
        "max_tokens": 1024,
    }
    r = requests.post(ENDPOINT, json=payload,
                      headers={"Authorization": f"Bearer {API_KEY}"},
                      timeout=60)
    r.raise_for_status()
    return r.json()["choices"][0]["message"]["content"]
def evaluate(model_id, eval_set):
    passed = sum(1 for case in eval_set
                 if case["checker"](ask(model_id, case["system"], case["question"])))
    return passed / len(eval_set)  # Output pass rate for horizontal comparison

Three reminders: Update and strictly secure your evaluation set to prevent contamination from public corpora, which reduces its discriminative power. For open-ended tasks, you can introduce model-assisted scoring, but manually spot-check the quality. Blind evaluations (hiding model names) significantly reduce subjective bias.

3. Engineering: API Maturity and Inference Performance
A model’s theoretical capability is useless if it cannot be integrated stably. Key checks include: streaming output support, which directly impacts the time-to-first-token experience in conversational products; format adherence and multi-turn stability for function/tool calling, which form the foundation of AI Agents and RAG; and real-world long-context retention. Note that nominal context length does not equal effective length—you can use “needle in a haystack” tests to measure recall degradation when information is placed at different positions. Conduct stress tests targeting your expected concurrency, recording P50 and P95 latencies rather than single-request durations. Finally, ensure seamless integration with logging services, monitoring alerts, and transparent token usage statistics.

4. Cost: Calculate the Total Bill, Not Just Unit Price
Cost analysis operates on three levels:

• API Call Cost: Input and output tokens are usually priced separately. In long-document scenarios, input tokens often represent the bulk of the cost.
• Scale Cost: Estimate your monthly bill by multiplying average daily calls by average token count. A cheap single test does not translate to cheap production.
• Architecture Cost: Cloud APIs offer pay-as-you-go models with zero maintenance. Self-hosted open-source models might be cheaper at massive scale, but hidden personnel costs for inference optimization, auto-scaling, and incident management must be factored in.
  Additionally, data-sensitive businesses may face a hard constraint requiring privatization, which immediately narrows options to open-source models deployed on self-managed infrastructure.

5. Compliance and Ecosystem: Long-Term Viability
Open weights do not automatically mean unrestricted commercial use; pay attention to license terms regarding commercial use, modification, and attribution. When handling personal or regulated industry data, verify data processing boundaries and security commitments. The compatibility and community activity surrounding fine-tuning scaffolds, RAG components, and Agent frameworks dictate how quickly you can find solutions when you hit roadblocks. If you prefer to avoid building infrastructure from scratch, you can opt for one-stop platforms from major cloud providers. For example, Baidu AI Cloud offers enterprise-grade LLM services that manage model invocation, fine-tuning, deployment, and security auditing within a single pipeline, making it suitable for teams that do not want to maintain their own inference operations.

Turning Five Dimensions into a Workflow

A recommended six-step approach:

1. Requirement Profile: Define tasks, context length, concurrency, budget, and compliance red lines.
2. Leaderboard Screening: Shortlist three to five candidates.
3. Smoke Testing: Run 20 to 30 manual samples per model to quickly eliminate obvious mismatches.
4. Formal Evaluation: Conduct blind tests using your custom evaluation set, outputting quantitative pass rates and bad case lists.
5. Engineering Stress Test: Integrate candidates into a target pipeline featuring RAG and tool calling to observe stability.
6. Canary Release: Roll out with low-traffic A/B testing, using real-world metrics (resolution rate, adoption rate, human takeover rate) for the final verdict.

Establish a periodic re-evaluation mechanism. Whenever a model releases a version update, rerun your core evaluation set.

Three Common Pitfalls

1. Ordering based solely on leaderboards: Leaderboards use public data; your business uses its own data. The distributions are fundamentally different.
2. Testing all models with a single prompt: Models have varying sensitivities to prompt styles. At minimum, perform a round of lightweight prompt adaptation for each candidate to ensure a fair evaluation.
3. Treating selection as a one-time event: LLMs iterate rapidly. Building a “continuous evaluation” mechanism is far more valuable than making a single “correct choice.”

Conclusion

The dense release schedule of Chinese LLMs and the commoditization of inference platforms have lowered the barrier to model acquisition to historic lows—a few lines of code can now invoke competing flagships. The true differentiator lies in the work done after selection: defining clear business standards, accumulating proprietary evaluation assets, and deeply integrating model capabilities into your engineering pipelines. There is no standard answer for model selection, but there is a standard methodology. Once that methodology is established, no matter how the models on the shelf evolve, you will be able to provide your own data-backed answer within a week.