A Practical Guide to Selecting Chinese LLMs: From Capability Dimensions to Engineering Practice

1. Why Model Selection Matters More Than Chasing the Latest Release

Over the past two years, Chinese large language models have gone through round after round of version iterations. Every few months, a new release tops the leaderboards. But for developers who actually need to deploy models in production, a practical problem persists: a high benchmark score does not mean the model fits your use case.

The reason is straightforward. Different models make different trade-offs in training data composition, alignment strategies, and inference architecture. These trade-offs surface as distinct “capability personalities”:

• Some models have long reasoning chains and think deeply, but produce results slowly;
• Some respond extremely fast at very low cost, but tend to skim the surface on complex tasks;
• Some are well-rounded overall, but perform inconsistently in scenarios that require multi-step reasoning.

So the essence of model selection is not finding the “strongest model”—it is finding the combination of capabilities that best matches your business constraints. The following sections break this down across several key dimensions.

2. Five Core Dimensions for Evaluating LLM Capabilities

2.1 Reasoning Depth and Chain-of-Thought Quality

Reasoning depth determines whether a model can handle tasks that require multi-step deduction—complex code debugging, mathematical proofs, or solution design under multiple constraints. When evaluating this dimension, looking only at whether the final answer is correct is not enough. You also need to check whether the intermediate reasoning process is self-consistent, or whether it “skips steps” or “takes detours” along the way.

One practical observation method: give the model a problem that requires at least three reasoning steps, and check whether it provides clear justification at each step or jumps straight to a conclusion. The latter may look efficient on simple tasks, but once task complexity increases, the error rate climbs sharply.

2.2 Response Latency and Throughput

Latency directly affects user experience and system architecture design. For interactive applications such as code completion or real-time Q&A, time to first token and output tokens per second are hard requirements. For offline batch tasks such as bulk document analysis or data labeling, throughput matters more than per-call latency.

Note that reasoning depth and latency often involve a trade-off. A model willing to “think a few more steps” will naturally be slower than one that “answers directly.” The key question is whether your scenario allows for that latency budget.

2.3 Task Completion and Output Stability

Completion measures whether a model can deliver a usable result in a single pass. Some models take longer to think but produce well-structured output that accounts for edge cases, reducing the cost of manual rework. Others are fast but produce highly variable output quality, requiring multiple retries or manual corrections—making the total cost higher in practice.

When evaluating completion, consider:

• Whether the output format consistently follows instructions;
• Whether long-form generation introduces internal contradictions;
• Whether generated code runs directly or requires substantial patching.

2.4 Cost Structure

Cost is not just the per-call API price. The full cost picture includes:

• Token pricing: input and output are typically billed separately;
• Retry cost: additional calls triggered by unacceptable output;
• Manual review cost: the proportion of outputs that require human correction;
• Indirect cost of latency: such as user churn or pipeline blocking.

A model with a low unit price but requiring three retries may end up costing more than a slightly more expensive model that passes on the first attempt.

2.5 Instruction Following and Controllability

In real engineering work, whether a model strictly follows system prompts, output format constraints, and tool-calling conventions often affects deployment efficiency more than raw intelligence. A model with strong reasoning ability but a habit of ignoring format requirements will introduce substantial extra processing logic during integration.

3. Matching Capability Tiers to Scenarios

In real projects, the common approach is not “pick one model and use it for everything,” but rather tiered invocation by task type. Below is a typical scenario-to-capability matching framework.

3.1 High-Complexity Reasoning Tasks

Typical scenarios: complex code refactoring, architecture design, multi-step logical reasoning, in-depth technical analysis.

These tasks have the highest demand for reasoning depth and can tolerate higher latency. When selecting a model, prioritize those with complete reasoning chains and high output completion. In practice, you can validate this as follows:

# Pseudocode: evaluating a model on multi-step reasoning tasks
def evaluate_reasoning_depth(model, test_cases):
    results = []
    for case in test_cases:
        response = model.generate(
            prompt=case.prompt,
            temperature=0.2,  # lower randomness for easier comparison
        )
        # Check whether the reasoning steps are complete
        steps_valid = check_reasoning_steps(response.reasoning_trace)
        # Check whether the final answer is correct
        answer_correct = verify_answer(response.final_answer, case.expected)
        results.append({
            "steps_valid": steps_valid,
            "answer_correct": answer_correct,
            "latency": response.latency,
            "tokens": response.total_tokens,
        })
    return results

3.2 High-Throughput, Low-Cost Tasks

Typical scenarios: batch text classification, log summarization, simple information extraction, data cleaning.

These tasks are usually not sensitive to per-call latency but are extremely sensitive to total cost. When selecting a model, prioritize low unit price and fast response. Importantly, set up a reasonable fallback strategy: when the low-cost model’s output confidence is insufficient, automatically escalate to a higher-capability tier for a retry.

# Pseudocode: tiered invocation strategy
def tiered_inference(task, input_text):
    # Tier 1: low-cost, fast model
    result = fast_model.generate(input_text)
    # Confidence check
    if result.confidence < THRESHOLD:
        # Tier 2: escalate to a high-capability model
        result = strong_model.generate(
            input_text,
            context=result.raw_output,  # use the initial result as reference
        )
    return result

3.3 Balanced General-Purpose Tasks

Typical scenarios: conversational systems, document Q&A, moderately complex code generation.

These scenarios require balancing quality, latency, and cost. When selecting a model, look for balanced overall capability and stable performance across a wide range of tasks. The key is to establish a continuous evaluation mechanism, because a model’s capability characteristics may shift after version updates.

4. Key Strategies in Engineering Practice

4.1 Build Scenario-Specific Evaluation Sets

Do not rely on general leaderboards. Instead, extract real tasks from your own business and build a small evaluation set (50–200 items) covering typical scenarios and edge cases. Each time a model version is updated or a vendor is switched, compare using the same evaluation set.

The evaluation set should include:

• Input samples (covering different difficulty levels and types);
• Expected outputs (or scoring criteria);
• Evaluation metrics (accuracy, format compliance rate, average latency, average token consumption).

4.2 Design Fallback and Retry Chains

In production environments, single-model call failures or unacceptable outputs are the norm. A multi-level fallback strategy is needed:

1. Same-model retry: adjust temperature or add prompt constraints;
2. Cross-tier escalation: upgrade from a fast model to a high-capability model;
3. Human fallback: for critical tasks, set up a manual review queue.

4.3 Monitoring and Continuous Optimization

After launch, continuous monitoring is needed for:

• Call volume, latency distribution, and error rate per model;
• Output quality metrics (such as format compliance rate and manual correction ratio);
• Cost trends (broken down by scenario and by model).

This data feeds back into model selection decisions and prompt optimization.

4.4 Coordinating Prompt Engineering with Model Capabilities

Different models have different sensitivity to prompts. A prompt template that works well on model A may underperform on model B. Recommendations:

• Maintain separate prompt templates for each model;
• Explicitly state output format and constraints in the prompt;
• For reasoning-oriented models, avoid over-constraining their thinking process.

5. Summary

There is no one-size-fits-all answer for selecting Chinese LLMs. The core approach is:

1. Define scenario constraints: latency budget, cost budget, quality requirements;
2. Build an evaluation framework: score across five dimensions—reasoning depth, latency, completion, cost, and controllability;
3. Tiered invocation: match different tasks to different capability tiers and design fallback chains;
4. Iterate continuously: let business data drive selection optimization rather than making a one-time decision.

The ultimate goal is not to use the “strongest model,” but to use the most appropriate combination of models—finding the optimal balance among quality, efficiency, and cost for your specific business.