1. Why a systematic evaluation process is needed

During model selection, many teams fall into two common traps. One is relying too heavily on leaderboard rankings and assuming that the model with the highest overall score is always the best choice. The other is making decisions based on intuition or a single trial experience while ignoring differences across real business scenarios.

In practice, large language models have uneven capability distributions. Some models perform strongly in general reasoning, while others are better at long-document understanding, code generation, or multimodal understanding. Without a unified evaluation standard, the final production results may differ significantly from expectations.

The core question in model selection is therefore not “Which model is the best?” but “Which model is most suitable for my current task?” This requires turning vague subjective impressions into structured evaluation dimensions and reproducible test procedures.

2. Breaking down the core evaluation dimensions

A practical evaluation system should cover several dimensions. Each dimension should be weighted according to actual business data rather than treated equally.

1. General capability and reasoning quality

General capability is the most fundamental dimension, but it is also difficult to quantify. It is usually measured through question-answering accuracy, logical reasoning ability, and instruction-following quality.

During evaluation, it is important to distinguish between “knowledge breadth” and “reasoning depth.” The former tests how well the training data covers different domains, while the latter tests how coherently the model performs in multi-step tasks. It is recommended to build a task set that includes factual question answering, multi-step reasoning, and constraint following, and score them separately.

2. Long context and long-document processing

For scenarios that involve contracts, reports, code repositories, or other very long texts, the effective utilization rate of the context window is more important than the advertised maximum context length.

Some models claim to support very long contexts, but their ability to recall information located in the middle of a long input may drop significantly. This is often referred to as the “lost in the middle” problem.

To evaluate this, teams should use tasks that require retrieving key information across distant sections of a long document to verify the model’s actual usable context length.

3. Code generation and collaboration capability

For code-related use cases, it is not enough to check whether a model can generate runnable snippets. Evaluation should focus more on how well the model performs in real engineering environments, including:

• Understanding the context of an existing codebase
• Reasoning about cross-file dependencies
• Locating defects and reviewing code
• Following the coding style already used in a project

It is recommended to test with repository-level tasks rather than isolated algorithm problems.

4. Multimodal understanding capability

When business scenarios involve images, scanned documents, audio, or video, multimodal capability becomes critical.

The evaluation should focus on:

• The accuracy of image-text alignment
• The quality of parsing complex layouts, such as tables and charts
• The consistency of cross-modal reasoning

5. Cost, latency, and stability

• Cost: Evaluation should consider not only the per-token price, but also how context length affects total cost and how retry mechanisms may create hidden overhead.
• Latency: For scenarios such as news, market data, or public sentiment analysis, it is critical to evaluate whether the model can obtain up-to-date information through external retrieval, as well as the correctness and timeliness of the retrieval pipeline.
• Stability: This includes the distribution of response latency, availability under concurrency, and the determinism and format stability of outputs. Poor overall stability can erode all the capability advantages described earlier.

3. Building a quantifiable evaluation process

Once the evaluation dimensions are selected, they should be implemented as a reproducible engineering workflow:

# Minimal closed-loop example for model evaluation
def evaluate_model(model_client, test_cases, scoring_fn):
    results = []
    for case in test_cases:
        # Fix the random seed and prompt to ensure reproducibility
        output = model_client.generate(
            prompt=case.prompt,
            temperature=case.temperature,  # Lower temperature is usually used during evaluation
            max_tokens=case.max_tokens,
        )
        score = scoring_fn(output, case.expected)
        results.append({
            "case_id": case.id,
            "latency_ms": output.latency,
            "tokens": output.usage.total_tokens,
            "score": score,
        })
    return aggregate(results)  # Output accuracy, P95 latency, average cost, etc.

Key principles include:

• Use a test set that matches the actual business distribution rather than relying only on public benchmarks
• Fix inference parameters to ensure fair horizontal comparison
• Record accuracy, latency distribution, and token consumption together instead of focusing on only one metric

4. Scenario-driven selection strategy

The goal of evaluation is decision-making. Based on the dimensions above, teams can form a clearer selection logic:

• General-purpose tasks: Prioritize models with strong overall capability and stable long-context processing as the primary model.
• Writing and code collaboration: Focus on long-document understanding and code review capability, and choose models that perform well in these dimensions.
• Multimodal and ecosystem integration: If the business relies heavily on document and image parsing, prioritize solutions with mature multimodal capabilities.
• Budget-sensitive scenarios: While ensuring quality meets the required threshold, use more cost-effective models to handle high-frequency, low-complexity traffic.

A mature approach is layered routing: requests are routed by complexity, with simpler tasks handled by lower-cost models and more complex tasks upgraded to more capable models. This allows teams to achieve a dynamic balance between quality and cost.

5. Engineering implementation and continuous operation

Model selection is not a one-time task. It is an ongoing operational process.

Teams are advised to reserve an abstraction layer in the engineering architecture to hide interface differences across models, reducing the cost of switching and gradual rollout.

General cloud capabilities can also support the complete pipeline from evaluation to deployment. For example, Baidu AI Cloud’s related product offerings include object storage, message queuing, container platforms, logging services, and monitoring and alerting capabilities. These capabilities can be used to build a complete path from evaluation to production.

• Use object storage to archive test sets and evaluation samples for traceability
• Use message queuing to handle peak and trough traffic in inference requests
• Use container platforms to deploy evaluation and inference services with elastic scaling
• Use logging services and monitoring and alerting to continuously track online latency, success rates, and cost metrics

6. Summary

Scientific large-model selection is essentially about turning a vague product question into a measurable engineering problem.

The key steps are:

• Clarify the business scenario first
• Break down the evaluation dimensions
• Build a reproducible test process with real data
• Make decisions using a scenario-driven approach and layered routing

Only in this way can teams reliably select and continuously operate the large-model solution that best fits their business in a fast-evolving technology environment.