Selecting Chinese LLMs: A Systematic Guide from Evaluation to Engineering Implementation

1. The Real Dilemma Behind Model Selection

Over the past couple of years, the supply landscape of Large Language Models (LLMs) has shifted significantly. When technical teams discussed open-source models previously, options were largely limited to a few overseas solutions. Today, models released by various Chinese teams have entered the mainstream, demonstrating strong competitiveness in certain benchmarks, code generation evaluations, and Chinese language tasks. Simultaneously, cloud providers and compute platforms have integrated these models into unified inference services, allowing developers to call models from different sources via standard APIs.

While this shift offers more choices and lowers the barrier to access, it introduces a new challenge: an increase in the number of models does not necessarily reduce the difficulty of selection. On the contrary, when multiple models achieve very similar scores on leaderboards, simply looking at rankings is no longer sufficient to drive decision-making.

Consider these common real-world scenarios:

• Team A wants to build an intelligent customer service bot. They care about Chinese language comprehension, multi-turn dialogue stability, and response latency.
• Team B wants to build a coding assistant. They care about long-context reasoning, code completion quality, and tool invocation capabilities.
• Team C wants to implement document summarization and knowledge retrieval within internal systems. They care about deployment costs, private deployment capabilities, and data security.

The same model may perform vastly differently across these three scenarios. Therefore, the key to model selection is not finding the “world’s most powerful model,” but rather finding the model that best matches your specific constraints.

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2. Define Evaluation Dimensions Before Comparing Models

A relatively comprehensive evaluation framework for Chinese LLMs should include at least the following five dimensions.

1. Base Capabilities and Language Performance

Base capabilities typically include language understanding, knowledge coverage, logical reasoning, and generation quality. For business scenarios heavily reliant on the Chinese language, special attention should be paid to:

• Stable comprehension of both colloquial and formal Chinese;
• Robustness in handling complex sentence structures, polysemous words, and omissions;
• Accurate processing of Chinese numerals, dates, organizational names, and industry terminology;
• Consistency and logical coherence in long-form text generation without abrupt breaks.

These capabilities are difficult to capture with a single benchmark score. They are better evaluated through small-scale human evaluations using real business data. For example, prepare 30 to 50 typical inputs, have two or more developers score them based on standardized criteria, and then compare the performance distribution across different models.

2. Code Generation and Tool Invocation

Code generation is one of the most directly valuable applications of LLMs today. When evaluating this, do not just look at whether the model can “generate runnable code.” Also, focus on:

• Accurately understanding task context rather than just generating textbook-style snippets;
• Maintaining consistent variable naming and structural style across cross-file and cross-function modifications;
• Using third-party libraries, frameworks, and APIs in accordance with common practices;
• Possessing basic testing awareness, such as generating boundary condition tests after the implementation;
• Tool invocation capabilities, meaning the ability to select appropriate functions, parameters, and return formats based on user input.

An actionable suggestion is to extract desensitized real tasks from your internal code repositories to build a fixed test set. Every time a new model is tested, run the same batch of tasks and record the pass rate, average execution time, and failure modes. The data accumulated this way will align much closer to your business needs than any public leaderboard.

3. Inference Services and Deployment Options

Deployment conditions vary greatly among technical teams. Some can accept public cloud APIs, while others require private or dedicated cloud deployments. During evaluation, you need to confirm:

• Whether the model supports private deployment;
• Whether the inference service provides stable HTTP or SDK interfaces;
• Support for concurrent scaling, streaming output, and timeout control;
• Whether the first-token latency and generation speed meet business requirements under identical hardware conditions.

For teams requiring private deployment, model weights, deployment solutions, and version update strategies should be confirmed upfront. If a model is only accessible via external APIs, it cannot be included in the selection scope for strict compliance scenarios.

4. Context Length and Long Document Processing

Many enterprise scenarios involve tasks like long document analysis, contract review, and meeting minutes summarization, which demand longer context windows. Note that a model’s “supported length” and its “effective usable length” are not always equivalent. Some models may experience information loss or forget critical details in the latter half of ultra-long inputs.

Therefore, specific long-text tasks should be designed during evaluation, such as:

• Locating a specific detail within a document exceeding 10,000 words;
• Generating segmented summaries of the entire text before creating a unified overall summary;
• Maintaining accurate references to early information during a long multi-turn dialogue.

These tests quickly expose the model’s true performance when handling long contexts.

5. Cost and Sustainability

Cost is not just about “how much per 1,000 tokens.” It also includes:

• Hardware and operational costs for private deployment;
• Business opportunity costs resulting from API latency;
• Whether upper-layer applications need re-adaptation after model upgrades;
• The service stability and version continuity of the provider.

For larger-scale operations, it is recommended to split cost estimations into “experimental phase costs” and “stable operation phase costs.” The experimental phase can tolerate slightly higher API costs in exchange for rapid validation, but the long-term service costs must be clearly calculated for the stable operation phase.

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3. Use Leaderboards, but Don’t Be Dominated by a Single Score

Leaderboards are a valid reference point for selection, especially for objective dimensions like code generation and logical reasoning. However, they have several inherent limitations:

1. Once test sets become public, models may be optimized specifically for the leaderboard, causing a deviation between scores and real-world performance;
2. Leaderboard tasks often lean towards academic or standardized problems, which differ significantly from real enterprise tasks;
3. Different leaderboards use varying evaluation criteria, prompts, and temperature parameters, requiring caution during horizontal comparisons.

A more rational approach is to use public leaderboards for an initial rough filter, and then use your own test set for a fine selection. The rough filtering quickly eliminates obviously unqualified models, while the fine selection uses real business data to make the final decision.

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4. Build a Reusable Internal Evaluation Pipeline

A minimum viable evaluation process can be set up following these steps.

Step 1: Define Scenarios and Metrics

Instead of vaguely asking to “pick the best model,” clearly define:

• The specific business scenario;
• Core metrics, such as accuracy, recall, availability, average latency, and cost limits;
• Which metrics are hard constraints and which are soft indicators.

For instance, a hard constraint for a contract clause extraction task might be an “entity recognition accuracy of no less than 92%,” while a soft indicator could be an “average response time of under 3 seconds.”

Step 2: Prepare the Test Dataset

The test set should cover the distribution of real inputs as closely as possible and remain version-locked. It is recommended to include at least three types of data:

• Regular samples: Representing the vast majority of user inputs;
• Boundary samples: Long texts, colloquial expressions, improperly formatted content, etc.;
• Adversarial samples: Content prone to causing model hallucinations, incorrect citations, or format disarray.

Using the same dataset for each evaluation ensures reproducible results.

Step 3: Standardize Invocation Methods and Parameters

Different models have varying sensitivities to prompts. During evaluation, maintain reasonable prompt flexibility for each model, but keep core parameters—such as maximum output length, temperature, and stop conditions—uniform. This reduces misjudgments caused by parameter discrepancies.

Below is a simplified evaluation example illustrating how to standardize calls to multiple candidate models:

# Pseudo-code: Unified model evaluation entry
def evaluate_model(model_name, test_cases):
    results = []
    for case in test_cases:
        prompt = build_prompt(case)
        output = call_model(model_name, prompt, max_tokens=512, temperature=0.1)
        score = compute_score(output, case["expected"])
        results.append({
            "case_id": case["id"],
            "score": score,
            "latency": output.latency,
        })
    return results

In actual implementation, encapsulate these calls based on the unified access layer provided by the respective model services to avoid hardcoding vendor-specific interface details into your test code.

Step 4: Document Failure Modes, Not Just Scores

Failure modes are more critical than overall scores. A model with 90% accuracy might be less usable if its errors are concentrated in high-risk scenarios compared to an 85% accurate model with dispersed errors.

Therefore, an evaluation report should be generated post-testing, including:

• Overall metrics;
• Scenario-specific metrics;
• Typical correct examples;
• Typical failure examples and root cause categorization;
• Improvement suggestions, such as prompt optimization, Retrieval-Augmented Generation (RAG), or fine-tuning.

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5. Engineering Advice: Moving from “Functional” to “Highly Usable”

Selecting a model is just the first step; the degree of engineering determines the actual deployment outcome. Here are several proven recommendations.

1. Establish a Unified Access Layer for Models

Even the same model might be offered through different service formats. It is recommended to add an adaptation layer between your application and the model to abstract away underlying differences. This ensures that business code requires minimal changes when models are upgraded or replaced.

2. Make Retrieval-Augmented Generation (RAG) the Default

For knowledge-intensive tasks, relying solely on a model’s internal capabilities is often insufficient. Combining enterprise knowledge bases with RAG can significantly reduce hallucinations and improve answer credibility. During selection, also test how well the model cooperates with retrieved context—for example, whether it correctly cites provided text segments instead of fabricating content.

3. Implement Sampling Evaluation and Regression Testing

Once a model version updates, old evaluation results may become invalid. Run regression tests on the same test set whenever a model is upgraded to confirm there is no significant degradation before fully switching over.

4. Control Prompt Complexity

More complex prompts are not inherently better. Overly complex prompts increase maintenance costs and make the model more sensitive to input variations. It is recommended to split prompts into a three-part structure: “System Constraints + Task Instructions + Input Content,” keeping them clear and stable.

5. Focus on Security and Compliance Boundaries

In enterprise scenarios, model outputs may directly face customers or internal employees. Establish a content security and compliance review process beforehand to prevent inappropriate content from entering production. Regardless of the model service used, maintain necessary audit and traceability capabilities for both inputs and outputs.

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6. How to Make the Final Choice

In summary, there is no single correct answer for selecting a Chinese LLM. The differing priorities of various teams directly dictate the final choice:

• Prioritizing API services vs. private deployment;
• Prioritizing conversational capabilities vs. code generation;
• Prioritizing rapid integration vs. self-controlled infrastructure;
• Prioritizing per-call cost vs. total cost of ownership (TCO).

A viable directional conclusion is:

• If your business focuses on conversational interfaces and document comprehension, prioritize testing models with solid base language capabilities and stable long-text performance;
• If your business centers around coding assistants and R&D efficiency, focus on tool invocation stability and open API adaptability;
• If your business operates in a strict compliance environment, first confirm whether the model supports private deployment and assess the deployment complexity;
• If your team is small and aims for a quick launch, prioritize unified inference endpoints that are simple to integrate and highly stable.

Ultimately, model selection should not be a one-time “leaderboard vote,” but rather a continuously running capability evaluation mechanism. As model capabilities evolve, the alignment between technology and business will shift dynamically. Building your own test sets, evaluation pipelines, and fallback plans holds more long-term value than memorizing the “optimal choice” at any given moment.