Chinese LLM Comparison and Selection: A Practical Six-Dimension Evaluation Framework

1. Introduction: Why LLM Selection Is Difficult

With large language model technology iterating rapidly, Chinese LLMs have demonstrated considerable practical value in text generation, coding, logical reasoning, and knowledge-based question answering. In practice, however, development teams often run into three recurring dilemmas.

First, public benchmark scores often diverge noticeably from real-world experience. Many models perform similarly on standardized evaluations, but once integrated into real production workflows, differences in instruction-following stability, long-context information retention, and consistency on complex reasoning quickly widen.

Second, the same model rarely performs evenly across task types. A model that excels at mathematical reasoning may not be competitive at structured information extraction or code generation—and vice versa.

Third, the trade-off between cost and quality is far more complex than it appears. Inference cost depends not only on per-call pricing, but also on prompt length, output length, reasoning steps, and concurrency levels. Comparing “price per thousand tokens” alone can easily lead to misleading conclusions.

Rather than asking “which model is the best,” it is more productive to build a multi-dimensional evaluation framework aligned with your own business characteristics.

2. Six Dimensions for Evaluating Model Capabilities

To compare LLMs systematically, observe and test them across the following six dimensions.

2.1 Reasoning Depth and Chain-of-Thought Quality

Reasoning depth refers to how a model handles complex problems that require multi-step deduction, conditional judgment, and hypothesis verification. A model with strong reasoning typically exhibits the following traits:

• It recognizes implicit conditions and constraint relationships in a problem;
• It builds a traceable reasoning path before producing a final answer;
• It shows good sensitivity to counterfactual hypotheses and edge cases;
• It demonstrates some ability to self-correct when contradictions arise mid-reasoning.

In testing, you can construct multi-hop logic problems, questions with nested conditions, and problems that require first identifying whether the problem statement itself is flawed. Note that some models tend to “quickly produce a plausible-looking answer,” which fails under strict verification because the reasoning chain breaks down. Others “think more slowly,” but their reasoning is more complete and their conclusions hold up better under scrutiny.

2.2 Knowledge Coverage and Freshness

A model’s knowledge coverage depends on the breadth and quality of its pretraining corpus and the design of its subsequent knowledge-update mechanisms. When evaluating this dimension, distinguish among:

• Static knowledge: historical facts, concept definitions, and domain common sense learned during training;
• Dynamic knowledge: awareness of recent events, newly published standards, and newly released tools and frameworks;
• Knowledge boundary awareness: whether the model can accurately judge what it does and does not know, rather than generating confident but incorrect content when uncertain.

In engineering practice, knowledge gaps are usually mitigated with retrieval-augmented generation (RAG). A model with good knowledge-boundary awareness tends to work better with external knowledge bases—it is more willing to acknowledge uncertainty when information is insufficient, rather than fabricating facts.

2.3 Instruction Following and Structured Output

In production systems, models rarely engage in free-form conversation; they must produce output in strictly specified formats. Instruction-following capability shows up in:

• Whether the model accurately understands instructions with multiple simultaneous constraints;
• Whether it reliably produces content conforming to a JSON Schema or a specific template;
• Whether it maintains adherence to early instructions across long contexts;
• How reliably it executes negative constraints (“do not do X”) and conditional constraints (“if A then B, else C”).

Structured output capability is critical for automated pipelines. You can design a set of test cases that include nested fields, enum types, optional fields, and format restrictions, then run repeated tests to observe output stability and parse success rates.

2.4 Code Generation and Engineering Assistance

Coding is one of the highest-value applications for LLMs. When evaluating coding ability, do not stop at “can it pass the test cases of a given algorithm problem”; focus on metrics closer to real engineering work:

• Multi-file project understanding: given a project with multiple modules, can the model correctly understand the dependencies between them?
• Context-aware code completion: within an existing codebase, can the model generate snippets consistent with local style and matching interfaces?
• Bug localization and diagnosis: given defective code, can the model locate the problem and propose a reasonable fix?
• Architecture trade-off analysis: among several viable implementations, can the model provide engineering-grade analysis and a justified recommendation?

A widely recommended best practice is to use the commit history of real projects as a test set, comparing the model’s suggested changes against what developers actually did, in order to assess real-world engineering usability.

2.5 Inference Latency and Throughput Characteristics

Latency directly affects user experience and system design. Key metrics to watch include:

• Time to first token: the time from request submission to the first output token;
• Total generation time: the total time required to complete a full response;
• Concurrent throughput: whether the service maintains stable latency under high concurrency;
• Behavior on long outputs: whether latency growth stays under control as generated content gets longer.

It is worth emphasizing that reasoning depth and latency are usually positively correlated. Some models, in pursuit of lower latency, may adopt more aggressive internal pruning or early-exit strategies. This partly explains why certain models “feel dumbed down”—they may be selecting shallower reasoning paths on some inputs.

2.6 Cost Structure and Price-Performance

Cost evaluation requires a full-chain perspective, not just the per-call unit price. A complete cost model should include at least:

Total cost = input tokens × input unit price
           + output tokens × output unit price
           + fixed API call fees (if applicable)
           + cache-related costs (if any)
           + additional call costs from retries and fault tolerance

Among these, retry costs are frequently underestimated. If a model has a low unit price but unstable instruction following or a high output-format error rate, causing many requests to be retried, the effective cost may far exceed the headline price.

3. Model Capability Profiles

Based on publicly available technical material and community feedback, Chinese LLMs—while converging in overall capability—do show distinct capability orientations. The following profiles reflect general observations and do not refer to any specific vendor or product.

3.1 General-Purpose, Balanced Models

These models are designed to achieve balanced performance across many tasks rather than pushing a single dimension to the extreme. Typical characteristics include:

• Stable performance in general conversation, text generation, and basic reasoning;
• Capability and latency that sit in a relatively balanced range;
• Suitability as a default choice, with adjustments made per task as needed.

The core advantage of general-purpose models is predictability: teams do not need to tune prompt strategies separately for every task, which lowers engineering maintenance costs. The potential drawback is that in scenarios demanding extreme reasoning depth or high specialization, they may underperform models optimized for those niches.

3.2 Deep-Reasoning-First Models

These models invest more training and inference resources in complex logical reasoning, mathematical proof, and multi-step planning. They usually exhibit the following behaviors:

• A tendency to perform lengthy internal reasoning before producing a final answer;
• More fine-grained recognition of a problem’s constraints;
• Higher accuracy in scenarios that demand rigorous logical chains;
• Slower response times and typically longer outputs.

In practice, deep-reasoning-first models suit offline batch processing or scenarios tolerant of longer wait times—for example, complex data analysis report generation, technical proposal evaluation, and math- and logic-intensive tasks. For interactive scenarios that require sub-second responses, techniques such as caching and streaming output optimization are needed to offset the latency disadvantage.

3.3 Efficiency- and Cost-First Models

These models use compression, distillation, quantization, and inference optimization to minimize inference cost and latency while maintaining usable capability. Their advantages include:

• Higher throughput per unit of cost;
• Suitability for large-scale, high-frequency, low-complexity call scenarios;
• Easy deployment in resource-constrained environments.

Their limitations are equally clear: on tasks requiring deep reasoning or complex knowledge integration, output quality can degrade noticeably. They are better suited to text classification, simple information extraction, format conversion, and low-complexity Q&A—not core reasoning responsibilities.

4. Engineering Practices for Model Selection

4.1 Build Your Own Evaluation Set

Public benchmarks can inform initial screening, but they should not be the final decision basis. Every team should build an in-house evaluation set containing three types of cases.

The first type is real business samples: desensitized requests sampled from production environments, covering both high-frequency and long-tail scenarios.

The second type is adversarial test samples: cases deliberately designed to probe boundary behavior—for example, instructions containing contradictory information, requests that should be declined, and multi-constraint format requirements.

The third type is regression test samples: used to verify that baseline capabilities have not regressed whenever a model version is upgraded or a candidate model is swapped in.

4.2 Establish a Multi-Model Routing Strategy

In experienced engineering teams, the common approach is not “pick one model for everything,” but rather building a multi-model routing layer. The core logic: dynamically assign each request to the most suitable model based on task type, complexity estimate, budget constraints, and latency requirements.

A simplified routing decision can be expressed in pseudocode:

def route_request(task):
    if task.requires_deep_reasoning():
        return select_model(pool="deep_reasoning", budget=task.budget)
    elif task.requires_low_latency():
        return select_model(pool="fast_response", timeout=task.timeout_ms)
    elif task.requires_structured_output():
        return select_model(pool="instruction_following", schema=task.schema)
    else:
        return select_model(pool="general", fallback=True)

The advantage of this architecture is that each model is used in the scenarios where it performs best, so the overall system’s capability ceiling and price-performance both exceed a single-model approach.

4.3 Continuous Monitoring and Iteration

Model capabilities are not static. Upstream model updates, server-side policy adjustments, and data distribution drift can all cause online performance to shift. Establish a continuous monitoring system covering the following metrics:

• Output format compliance rate;
• P50/P95/P99 percentiles of time to first token;
• Average token consumption per call;
• Retry and timeout rates;
• Business-side satisfaction ratings.

When monitoring metrics fluctuate abnormally, trigger an evaluation-set regression promptly, and adjust routing policies or replace candidate models when necessary.

5. Summary and Recommendations

Chinese LLMs have reached a stage where they can be deployed at scale, yet capability differences between models remain significant. For developers and technical decision-makers, the most important shift is to abandon the “find the single best model” mindset and instead build a business-driven, multi-dimensional evaluation and dynamic routing system.

In practice, proceed along the following steps:

1. Map the distribution of task types and constraints across your business scenarios;
2. Build an in-house evaluation set containing real business samples;
3. Evaluate candidate models per scenario based on evaluation results;
4. Deploy a multi-model routing layer that assigns requests by scenario;
5. Establish continuous monitoring and periodic regression mechanisms to maintain system resilience.

LLM technology is still evolving rapidly—today’s selection conclusions may need to be revisited in the short term. Rather than anchoring to one specific model, invest in evaluation capability and routing architecture. Those are the long-term assets that will outlast successive waves of technological change.