A Practical Guide to Choosing Chinese Open-Source LLMs: From Inference Platforms to Use-Case Fit

1. Why the “Model Marketplace” Is Changing How We Choose

Over the past two years, the way teams access large language models has fundamentally changed. In the early days, using a pretrained model meant downloading weights, configuring environments, and optimizing inference performance—a deployment cycle that often stretched into weeks. Today, major cloud providers and inference platforms offer “model-as-a-service” capabilities: optimized models packaged as standardized inference endpoints, so developers can go from prototype validation to production launch simply by calling an API.

You can think of this as a “model marketplace.” The platform shelves a variety of open-source models, and users pick freely based on task requirements, budget, and performance needs. Recently, more and more Chinese open-source models have appeared on these shelves, covering everything from general conversation to code generation, from long-document understanding to multimodal reasoning.

For developers, this brings two immediate benefits:

• Lower trial-and-error cost: Without deploying anything yourself, you can compare multiple models on the same task within minutes.
• Shorter path to production: Inference platforms typically build in batching, quantization, and concurrency scheduling, saving a great deal of engineering work.

At the same time, selection difficulty is rising. When the options grow from two or three to a dozen or more, “run a demo and see how it looks” is no longer enough to support a decision. We need a more systematic evaluation framework.

2. The Current Technical Landscape of Chinese Open-Source Models

From a technical standpoint, Chinese open-source LLMs today can be roughly divided into several tiers and directions:

2.1 General Conversation and Instruction Following

This category focuses on general capabilities, suited to customer service Q&A, content generation, and retrieval-augmented knowledge tasks. Core metrics include:

• Instruction-following accuracy
• Multi-turn conversation consistency
• Hallucination suppression
• Depth of Chinese semantic understanding

In recent years, models in this tier have approached—and in some cases surpassed—international open-source models of comparable parameter size on public benchmarks, with a clear advantage on Chinese-language tasks.

2.2 Code Generation and Structured Reasoning

Code capability is an important yardstick for a model’s logical reasoning. Today’s strongest code models typically share these traits:

• Support for multi-language code generation and completion
• Ability to understand complex function signatures and type systems
• High pass rates on algorithm problems, refactoring tasks, and unit test generation
• Retrieval and comprehension of long-context code repositories

On public code evaluation leaderboards, Chinese models now occupy many top positions, with some performing especially well on specific languages and frameworks.

2.3 Long Context and Document Understanding

With the spread of RAG (retrieval-augmented generation) architectures, the ability to handle long documents has become critical. Most mainstream models now support 128K or even longer context windows, but in practice there is a gap between the “nominal” and “effective” context length. When choosing, pay attention to:

• Key information recall rate in long texts
• Cross-paragraph reasoning ability
• Performance decay curve after context window extension

2.4 Multimodal and Mixed Reasoning

Some models now support non-text inputs such as images, tables, and charts, which is valuable for document parsing, report analysis, and UI understanding. However, multimodal maturity varies widely, so targeted validation on your specific task is recommended.

3. The Neutral Value of Inference Platforms

When a model is integrated into a mainstream inference platform, its significance for developers goes beyond “one more API endpoint.” Platform-level engineering optimization often significantly affects the actual experience:

Inference acceleration: Through operator fusion, KV cache optimization, and continuous batching, throughput on the same hardware can improve several times over.

Quantization and memory optimization: INT8, INT4, and similar quantization schemes let large models run under lower memory conditions, directly reducing unit inference cost.

Elastic scaling: Traffic fluctuations are normal in production. Automatic scaling provided by the platform helps avoid timeouts or service rejections caused by sudden request spikes.

Observability: Monitoring latency distribution, token consumption, and error rates is the foundation for continuous optimization after launch.

So when choosing a model, don’t look only at its benchmark scores. Consider how it actually performs on your target inference platform—including cold start time, concurrency limits, and billing granularity.

4. An Evaluation Framework: Five Key Dimensions

Based on the analysis above, I suggest building an evaluation matrix across the following five dimensions:

4.1 Task Fit

Start by clarifying your core task type. Is it knowledge Q&A, code generation, text summarization, or multi-turn conversation? Different models can vary widely across tasks. A recommended approach:

1. Sample 50–100 real requests from your business scenarios as an evaluation set.
2. Define clear scoring criteria (e.g., accuracy, completeness, format compliance).
3. Run each candidate model against the set and record the results.

This is far more convincing than any public leaderboard.

4.2 Inference Cost

Cost includes both explicit and implicit parts. Explicit cost is API call fees or GPU rental fees. Implicit cost includes:

• Secondary human review costs caused by unstable model output
• User experience degradation caused by high latency
• Additional retrieval and concatenation logic caused by insufficient context windows

When evaluating, calculate the “comprehensive cost per thousand effective requests” rather than comparing unit prices alone.

4.3 Latency and Throughput

For online interactive scenarios, first-token latency and end-to-end latency are key metrics. For offline batch processing, throughput matters more. When choosing, clarify:

• What is the maximum acceptable latency?
• What is the expected peak QPS?
• Is streaming output required?

4.4 Context and Multimodal Needs

If your business involves long-document analysis, complex multi-turn conversations, or mixed text-image inputs, focus on validating how the model actually performs in these scenarios. Note the difference between “supported” and “usable”—many models claim 128K context, but information recall drops noticeably after 32K.

4.5 Ecosystem and Portability

Finally, consider long-term portability. If the model you choose today needs to be replaced later, how high is the migration cost? This depends on:

• Whether the API is compatible with mainstream interface specifications
• Whether prompt engineering is model-specific
• Whether fine-tuning and distillation capabilities are open

5. Practical Advice: How to Complete a Selection Round Quickly

If you’re facing a choice among multiple candidate models, you can follow this process:

Step 1: Define your constraints. This includes budget ceilings, latency requirements, data compliance requirements, and whether private deployment is needed.

Step 2: Narrow the candidate range. Based on task type and constraints, pick 1–2 representative models from each direction—general conversation, code generation, long context, and so on.

Step 3: Build an evaluation set. Sample from real business logs, covering typical scenarios and edge cases.

Step 4: Automate the evaluation. Write scripts to batch-call each model’s API and uniformly record outputs and performance metrics. Here is a simplified evaluation script skeleton:

import time
import statistics
def evaluate_model(client, model_name, test_cases):
    results = []
    for case in test_cases:
        start = time.time()
        response = client.chat.completions.create(
            model=model_name,
            messages=[{"role": "user", "content": case["input"]}]
        )
        latency = time.time() - start
        results.append({
            "case_id": case["id"],
            "output": response.choices[0].message.content,
            "latency": latency,
            "tokens": response.usage.total_tokens
        })
    return {
        "avg_latency": statistics.mean(r["latency"] for r in results),
        "total_tokens": sum(r["tokens"] for r in results),
        "details": results
    }

Step 5: Manual spot-checking and scoring. Manually review the automated evaluation results, focusing on format compliance, factual accuracy, and tone suitability.

Step 6: Small-traffic canary rollout. Before a full switch, route a small amount of live traffic to the candidate model and observe its behavior in a real environment.

6. Summary

Chinese open-source LLMs have made significant progress over the past year and are now production-ready across multiple technical directions. But “which one to choose” has never had a standard answer—it depends on your task type, cost constraints, latency requirements, and long-term plans.

The maturity of inference platforms makes model switching easier, but it also requires developers to build more systematic evaluation capabilities. Rather than chasing leaderboard rankings, start from real business scenarios and drive selection decisions with quantifiable metrics. After all, the best model is the one that is stable, efficient, and cost-controllable on your task.

I hope the framework in this article helps you make a more rational judgment in your next technology selection round.