Chinese LLM Selection Guide: Evaluation Criteria and Inference Platform Deployment Practices

Background: Why Inference Service Platforms Have Become a Key Selection Variable

Over the past year, Chinese-developed large language models have iterated at a remarkable pace, with several technical teams releasing new generations of flagship models in quick succession. At the same time, model inference service platforms—the “middle layer” between models and developers—have been rising rapidly in importance. Understanding the role of this layer is a prerequisite for any serious discussion of model selection.

Informally, an inference service platform works like a model catalog: the platform provider wraps models from different sources behind standardized interfaces, and developers call them through a unified API without having to deal with weight loading, GPU memory scheduling, batching optimization, or other low-level engineering problems. Early on, these platforms mostly listed open-source models from the international community; recently, some have begun onboarding Chinese models as well—a sign that Chinese models have reached a level of engineering quality and ecosystem compatibility that third-party platforms are willing to accept.

The implication for selection is significant: models are no longer isolated artifacts but have entered an infrastructure layer where they can be compared, replaced, and combined. When evaluating a model, developers need to look beyond benchmark scores and ask whether it is available on mainstream inference platforms, whether its API is stable, and whether its version iteration is predictable.

Five Core Evaluation Dimensions

Faced with multiple Chinese model options, it is easy to fall into leaderboard-only thinking. Benchmark rankings matter, but production outcomes usually hinge on other factors. A sound evaluation framework should cover the following five dimensions.

Core Capabilities: Reasoning, Code, and Long Context

Core capabilities are the starting point of selection, but they need to be broken down:

• General reasoning and knowledge QA: Examine how the model performs on multi-step logical reasoning and domain knowledge retrieval. Build a small evaluation set from real questions in your own business domain instead of trusting public leaderboards directly.
• Code generation and understanding: For developer tooling scenarios, code completion is a high-frequency need. Focus on the model’s actual performance on function-level generation, cross-file context understanding, and bug fixing.
• Long-context handling: A model’s nominal context window and its effective utilization are two different things. Test information recall at lengths close to the window limit and watch for “lost-in-the-middle” behavior.

A pragmatic approach: shortlist a few top-ranked candidates from public leaderboards, then run a second-round evaluation with 50–100 real business samples. Leaderboards narrow the field; your own evaluation makes the final call.

API Compatibility and Ecosystem Maturity

Whether a model exposes interfaces compatible with industry-standard specifications directly determines migration cost. The current mainstream practice is to provide HTTP interfaces compatible with the common chat completion specification, so developers can reuse existing SDKs and toolchains. When evaluating, check:

• Whether the API specification aligns with industry standards and supports streaming output;
• Whether multi-language SDKs are available and the documentation is complete;
• Whether version management is clear, and whether model upgrades come with canary release and rollback mechanisms.

Deployment Options: Public API vs. Private Deployment

Different businesses have very different requirements for how data flows. Public-facing applications can usually accept public API calls, while scenarios involving sensitive data call for private deployment or dedicated instances. During selection, confirm:

• Whether the model is offered in both public API and private deployment forms;
• What the minimum hardware requirements for private deployment are, and whether quantized versions are supported;
• Whether the inference platform can bring the model under unified scheduling and monitoring.

Cost Structure

Cost is more than unit price. A complete cost model should include input/output token pricing, tiered pricing tied to context length, hardware depreciation and operations staffing for private deployment, and the retry and degradation costs caused by unstable model behavior. Compare options by the total cost per thousand typical requests rather than by nominal unit price alone.

Iteration Cadence and Long-Term Maintainability

Large language models are still evolving quickly, and selection is not a one-time decision. Evaluate the provider’s release frequency, backward compatibility commitments, and deprecation policy. A model that iterates actively but offers no compatibility guarantees can impose a continuous migration burden on long-lived projects.

The Engineering Value of Inference Service Platforms

Returning to inference platforms themselves: bringing models under unified platform management delivers engineering benefits on three levels.

Unified Access to Reduce Integration Complexity

If every model requires its own integration code, the adapter layer balloons. A unified platform typically provides a consistent request/response structure and authentication mechanism, so developers maintain only one set of client logic. The following is illustrative pseudocode:

# Unified invocation wrapper (pseudocode; refer to the actual API documentation for the exact interface)
from unified_client import Client
client = Client(endpoint="https://api.example.com/v1", api_key="YOUR_KEY")
resp = client.chat.completions.create(
    model="model-a",          # Switch backends via the model parameter without changing the calling logic
    messages=[{"role": "user", "content": "Explain the benefits of batched inference"}],
    stream=True,
    max_tokens=1024,
)
for chunk in resp:
    print(chunk.delta, end="")

Switching backends through the model parameter makes A/B testing and canary releases straightforward: route a small share of traffic to the new model, compare quality and latency metrics, and only then decide whether to roll out fully.

Elastic Scheduling and Resource Utilization

Inference workloads tend to arrive in spikes. The platform layer can smooth the resource curve through request queuing, dynamic batching, and on-demand scaling. For teams running their own inference clusters, whether the platform integrates with container platforms and monitoring/alerting systems is an important indicator of its maturity.

Observability

Production environments need to answer questions like “has model performance degraded?” A platform should expose basic metrics such as request volume, latency percentiles, error rates, and token consumption, and support feeding inference logs into a unified logging service and alerting system. A model service without observability is extremely expensive to troubleshoot.

From Evaluation to Production: A Practical Path

Build a Layered Evaluation Dataset

Structure the evaluation dataset in three layers: a general capability set (sampled from public questions), a business scenario set (constructed from anonymized real user questions), and an edge-case set (stress cases such as overlong inputs, multi-turn conversations, and conflicting instructions). Score the three layers with weights of 23 so that no single dimension dominates the conclusion.

Canary Rollout with Limited Traffic

After choosing a candidate model, start with 5%–10% of traffic and focus on: time to first token, full response latency, error rate, and quality scores from manual sampling. The canary period should last at least two weeks and cover at least one full business traffic peak.

Fallback and Fault-Tolerance Design

Any single model can be rate-limited or fail. The production architecture should include a fallback chain: when the primary model is unavailable, switch to a backup model while keeping the request structure compatible at the API layer, so the switch is transparent to upstream services. Also set timeouts and retry limits to prevent cascading failures.

Cost Monitoring and Capacity Planning

After launch, track token consumption and per-request cost weekly and plan capacity against the business growth curve. For long-context scenarios, pay special attention to the long tail of the input length distribution—it is often the main source of cost overruns.

Common Pitfalls and How to Avoid Them

• Leaderboard-only selection: The question distribution of public leaderboards often differs greatly from your business distribution; always calibrate with your own evaluation set.
• Ignoring effective context utilization: A large nominal window does not mean a large effective window; always run long-text recall tests.
• Overlooking version iteration risk: Confirm the deprecation policy during selection and keep model-switching capability in your adapter layer.
• Underestimating operations cost: The hardware and staffing costs of private deployment are frequently underestimated and should be built into the total cost model.
• Missing fallback design: Single-model dependency is an availability risk; multi-model redundancy is the safer engineering choice.

Conclusion

Chinese LLMs have entered a stage with multiple comparable options, and the rise of inference service platforms has further lowered the barrier to adoption. The essence of selection is not finding “the strongest model” but building an evaluation methodology that matches your business scenarios: use public leaderboards to narrow the field, use your own evaluations to make decisions, use a unified access layer to preserve flexibility, and use canary rollouts and fallbacks to safeguard stability. Within this framework, specific model names will change with each iteration, but the methodology remains reusable. For teams building reliable applications on Chinese LLMs, doing the engineering preparation up front is worth more than chasing each new benchmark score.