Evaluating LLM Code Generation and Inference Efficiency: From Code Quality to CoT Costs

Horizontal evaluations of multiple models in real-world projects provide far more practical value than standard benchmark leaderboards. Based on over a week of empirical testing using a medium-complexity engineering project (featuring a modern frontend build tool paired with a strongly-typed, compiled backend service), this article conducts a structured review of several mainstream LLMs. The evaluation focuses on four dimensions: code generation quality, text articulation, Chain-of-Thought (CoT) length, and end-to-end response efficiency, offering reusable model selection and tuning recommendations. To maintain neutrality, the models evaluated are referred to as “Model A / B / C / D / E.”

1. Evaluation Design and Task Background

The evaluation载体 is a real business project with a tech stack consisting of a “compiled system language + modern frontend build tool.” The overall complexity is medium, covering standard API layers, data models, state management, component encapsulation, and minor build configuration modifications. All participating models possess the capability to fulfill the functional requirements—at the level of “can it be built,” no model significantly fell behind. The differences primarily manifested in three areas: the quality of the generated code, the clarity of explanations, and the duration of the reasoning process.

Methodologically, the same set of requirement descriptions and acceptance criteria was provided to each model to generate implementations, followed by manual code review and runtime verification. To control variables, no additional preset prompts were attached except for the writing style test.

2. Code Generation Quality: Structural Clarity is the Main Divider

In terms of code quality, the evaluated models generally fell into two tiers.

Tier 1: Clear structure, reasonable abstraction. Model A and Model B generated noticeably higher quality code than the rest. Specifically, module responsibility boundaries were clear, function granularity was appropriate, type definitions were well-consolidated, and error-handling paths were complete. Instances of “a single function doing five different things” were rare. In scenarios involving asynchronous flows and state synchronization, these two models tended to establish a clear data flow before filling in the implementation details, thereby reducing future maintenance costs.

Tier 2: Functionally correct but stylistically rough. The remaining models correctly implemented the requirements but lacked engineering aesthetics. Model E, for instance, produced typical “AI-style spaghetti code” in several localized areas—featuring deeply nested conditional branches, redundant intermediate variables, and logic crammed into a single function body that should have been abstracted. The impact of these issues is usually minimal and can be fixed with an extra round of conversation, but they do consume extra manual effort during code review.

A key conclusion to emphasize is that code quality differences do not manifest in “whether it compiles and passes tests,” but rather in “whether it remains readable to your future self six months down the line.” For long-term maintained projects, the ROI of Tier 1 models goes beyond saving a few lines of code; it fundamentally reduces cognitive overhead.

3. Text Articulation: Default Style and Controllability

Without any preset prompts, the default writing styles of the models varied significantly:

• Model A: Technically accurate, but the default style leans toward “seasoned colloquial,” frequently using abbreviations and shorthand. It reads like an experienced engineer who can’t be bothered to elaborate. The good news is that the style is highly controllable—once explicitly required to be “formal” or “documentation-oriented,” the output style noticeably converges.
• Model E: The default style is quite appealing among the evaluated models, featuring natural expression and a comfortable pace, making it suitable for comments, documentation, and explanatory content.
• Model B: Also performs well stylistically, striking a balance between formal and natural.
• Model C series: The default style carries a strong “corporate PR tone,” characterized by buzzword stacking and formulaic sentence structures, resulting in a poor reading experience for technical audiences.
• Model D: Overly accommodating, tending to use an excessive amount of affirmative and complimentary phrasing with low information density, making it unsuitable for technical documentation.

A practical insight here is that writing style is not a fixed capability of the model, but an output dimension that can be consistently constrained via system prompts. Embedding “style requirements” into the system prompt (e.g., no abbreviations, no colloquialisms, conclusion before argument for each paragraph) is far more efficient than correcting the style on a per-conversation basis.

4. CoT Length: The Underestimated Efficiency Cost

The most notable finding in this evaluation is the significant impact of Chain-of-Thought (CoT) length on end-to-end efficiency.

Model A’s reasoning process was noticeably longer than the other models, reaching more than double the length of others on certain complex tasks. Coupled with its average API response speed, the end result was that the total time to complete the same task was about 50% longer than other models.

This number might seem negligible for a single call, but in engineering scenarios, it scales rapidly:

Assume average single task time: Other models 40s, Model A 60s
Daily API calls 500 → Extra ~2.8 hours consumed daily
For interactive coding assistants, the perceived latency per wait is further amplified

A deeper issue is that an excessively long CoT does not always yield better results. For tasks with clear requirements and well-defined boundaries, verbose reasoning often amounts to repetitive self-confirmation—a classic case of “thought redundancy.” This redundancy offers marginal contribution to final code quality but tangibly consumes latency budgets and token costs.

There are three corresponding engineering countermeasures:

1. Task Tiering: Categorize requirements into “deterministic” and “exploratory.” Deterministic tasks (e.g., implementing a defined interface, writing unit tests, modifying configs) should be routed to models with short CoTs and fast response times. Exploratory tasks (e.g., architectural trade-offs, complex refactoring designs) can then be assigned to more capable, albeit slower, reasoning models.
2. Output Budgeting: Explicitly require in the prompt to “provide the conclusion first, then necessary arguments,” “do not restate known information,” or “reason in no more than N steps” to compress ineffective thinking.
3. Asynchronous Pipelining: Route latency-insensitive tasks (e.g., batch code reviews, documentation generation) through asynchronous queues to prevent slower models from blocking interactive pipelines.

5. Selection Recommendations: Combine by Scenario, Don’t Bet on a Single Model

Synthesizing the four dimensions, it is clear that a single model rarely excels across all scenarios. A more rational approach is scenario-based composition:

Scenario	Primary Consideration	Recommended Tendency
Core business code implementation	Code structure quality	Tier 1 code quality models
Interactive coding assistance	Response latency	Models with short CoTs and fast responses
Technical docs & comments	Style naturalness	Models with a comfortable default style
Complex solution design	Reasoning depth	Highly capable reasoning models, accepting higher latency
Batch offline tasks	Unit cost	Low-cost models + async pipelines

Additionally, it is recommended to implement two things at the integration layer: First, establish a unified prompt template layer to centrally manage constraints like style, output format, and reasoning budgets, preventing individual teams from repeatedly tweaking prompts. Second, implement latency and quality telemetry based on task types, using real data to drive the iteration of model routing strategies rather than relying on one-off evaluation conclusions.

6. Conclusion

The core findings of this empirical test can be distilled into three points: The gap in code quality lies in maintainability, not correctness; writing style is a controllable dimension, not a fixed model trait; and CoT length is a real engineering cost that must be factored into selection decisions. Treating models as composable engineering components—routing and constraining them based on task characteristics—is more realistic than searching for an “all-around champion” and better aligns with production environments’ dual demands for stability and cost efficiency. For enterprise development scenarios, leveraging universal model integration and orchestration capabilities—such as those provided by Baidu AI Cloud—enables grayscale comparisons and cost governance across multiple models on a unified platform. This ensures that selection decisions are built on continuous measurement rather than one-off subjective impressions.