Choosing a Chinese LLM: A Practical Guide from Model Capabilities to Production Deployment

1. The Real Problem: Selection Is About Fit, Not Raw Strength

When a team decides to integrate large language model capabilities into a business application, one question comes up repeatedly: which Chinese LLM should we choose? On the surface, this looks like a simple comparison of a few model names. In practice, it is a multi-objective optimization problem under several constraints: whether the model’s capabilities match the task, whether inference costs are acceptable, whether the deployment approach fits the existing architecture, and whether the model’s release cadence can keep up with business evolution.

Chinese LLMs now form a clear tiered landscape. The leading products perform well across general capabilities, code generation, and long-context understanding, and they regularly appear in public comprehensive benchmarks. However, benchmark rankings should only serve as an initial filter, not as the final basis for a decision. Benchmarks measure standardized tasks, while real business scenarios differ in task distribution, input length, output format constraints, and error tolerance. A model that ranks highly on general benchmarks may not necessarily perform better on structured output tasks in a specific domain.

This article does not aim to give a “choose A over B” answer. Instead, it provides an analytical framework that spans capability evaluation and engineering deployment, helping readers break down the selection problem into verifiable technical judgments.

2. Capability Dimensions: Four Angles for Evaluation

2.1 General Reasoning and Instruction Following

This is the most fundamental dimension and the one most benchmarks cover. Evaluation should focus on the model’s stability in multi-step reasoning, output consistency under conditional constraints, and semantic accuracy in Chinese contexts. A practical approach is to extract 50 to 100 real requests from your own business, build a small evaluation set, and score candidate models through blind testing rather than relying on a single public benchmark score.

2.2 Code Generation and Structured Output

For developer toolchain scenarios, code generation quality and structured output, such as strict adherence to JSON or XML formats, are hard requirements. Two points deserve attention during evaluation. First, how consistently the model performs code completion across long functions and multi-file contexts. Second, the “contract compliance rate” of output formats, meaning whether the model reliably avoids adding extra explanatory text when a specific output structure is explicitly required. In engineering integration, the latter often affects usability more than raw code quality.

2.3 Long-Context Handling

Long-context capability directly determines whether a model can be used for document analysis, codebase understanding, and long conversation memory. Evaluation should not focus only on the nominal context window length. It is equally important to measure the “effective context length,” meaning whether the model can still reliably recall information in the middle of the context when the window is filled to a high percentage. A practical test is the “needle in a haystack” retrieval method: insert a specific fact at a random position in a long text and check whether the model can accurately reproduce it.

2.4 Inference Cost and Latency

Beyond capability, cost structure is equally critical. Three types of costs should be distinguished: per-request token billing, memory usage driven by long contexts, and throughput under high concurrency. For interactive applications, time-to-first-token and output throughput directly affect user experience. For batch processing tasks, per-token cost is the dominant factor. It is advisable to estimate costs using real traffic ratios during the selection phase rather than optimizing after launch.

3. Deployment Models: Managed Inference Platforms vs. Self-Hosted Services

Beyond model capability, another frequently underestimated decision point is how the model will actually run. There are two main paths.

3.1 Managed Inference Services

One approach is to call models directly through inference platforms provided by cloud vendors. These platforms encapsulate engineering details such as model loading, batch scheduling, memory management, and elastic scaling, allowing developers to make requests through standard interfaces. The advantages are fast onboarding and low operational burden, making this approach suitable for rapid validation and small-to-medium production workloads. The limitations include limited customization, less granular control over inference parameters, weaker support for private data isolation, and fewer options for hardware-specific optimization compared with self-hosting.

Notably, the model catalogs on these platforms have expanded from a small number of early open-source models, and the onboarding pace for Chinese models has accelerated. For developers, this means they can compare the actual performance of different models through a unified calling method, reducing experimentation costs.

3.2 Self-Hosted Inference Services

When a business has stricter requirements around data compliance, inference performance, or cost, self-hosting is the more appropriate choice. A typical technology stack includes containerized deployment, inference engine selection such as general-purpose frameworks that support continuous batching and quantized inference, GPU resource pool scheduling, and supporting monitoring, alerting, and logging services.

A simplified deployment flow is shown below as pseudocode to illustrate the structure only:

# Inference service deployment configuration example
service:
  model_source: <model weight path>
  runtime: <general-purpose inference engine>
  resources:
    gpu_type: <general GPU specification>
    replicas: 2
  serving:
    max_batch_size: 8
    max_concurrent_requests: 64
  observability:
    metrics: [qps, latency_p99, gpu_utilization]
    logging: <logging service integration>

The core cost of self-hosting is not hardware procurement, but the ongoing investment in engineering staff. Model version upgrades, inference engine tuning, and incident diagnosis all require dedicated personnel. Therefore, the decision to self-host should be based on the combination of call volume and customization intensity rather than a blanket rule.

3.3 Hybrid Strategy

In practice, a hybrid strategy is more common: core business paths use self-hosted services for controllability, while non-core scenarios or those with high traffic fluctuation use managed APIs to absorb peaks. This architecture requires a unified routing and fallback layer at the top, abstracting model invocation into a switchable capability layer rather than hardcoding a specific model’s API into business logic.

4. An Engineering Approach to Selection: Maximizing Information at Minimal Cost

4.1 Establish an Internal Evaluation Baseline

Do not start comparisons from scratch. First select a model already in use or a publicly available baseline model, measure its performance on your business request set, and then run candidate models on the same dataset. Evaluation metrics should include task completion rate, format compliance rate, average output length, and p99 latency. The relative differences obtained this way are far more valuable for decision-making than absolute scores.

4.2 Use A/B Routing Instead of One-Time Replacement

The risk of model replacement lies in performance degradation on long-tail requests. A recommended approach is to split traffic at the gateway layer, routing a small percentage to the candidate model while comparing real-distribution metrics between the two. An observation period of no less than two weeks is advisable. During this period, pay special attention to failure patterns: whether they are format errors, content truncation, or inference timeouts. Different failure modes correspond to very different remediation costs.

4.3 Build Replaceability into the Architecture

Regardless of which model is ultimately chosen, the architecture should preserve the ability to swap models: a unified request abstraction layer, a standardized output parsing layer, and a configurable model routing policy. The benefit is that when model versions iterate or cost structures change, the switching cost remains at the configuration level rather than requiring business logic rewrites.

5. A Deployment Perspective with Baidu AI Cloud

Under the framework above, Baidu AI Cloud provides general-purpose infrastructure capabilities that can support the post-selection engineering phase. Its container platform can host self-built inference services, object storage can hold model weights and evaluation datasets, logging and monitoring services can support observability for inference workloads, and message queues can handle asynchronous inference pipelines for traffic shaping. These capabilities belong to general infrastructure categories and are decoupled from any specific model selection, making them suitable as a stable foundation beneath the model capability layer.

6. Summary

Selecting a Chinese LLM is fundamentally a three-dimensional decision involving capability fit, cost constraints, and engineering controllability. A recommended decision sequence is: first, build an internal evaluation set from real business requests and filter for candidates with sufficient capability; second, evaluate the cost and controllability trade-offs between managed and self-hosted deployment; finally, validate long-tail performance through gradual traffic routing and embed replaceability into the architecture. Benchmark rankings can serve as a starting point, but they should not be the endpoint. What ultimately determines selection quality is how well the team understands its own business distribution and how rigorously it validates its choices.