Evaluating LLM Coding Capabilities: A Practical Selection Framework

1. Background: Why We Need to Reassess LLM Coding Capabilities

Over the past few weeks, I have been continuously using multiple large language models to assist development on a medium-complexity project. The project combines a systems-level language with mainstream frontend build tooling, involving module decomposition, type design, API integration, state management, and some low-level data structure operations. Overall, the functional requirements are not extremely complex, but there are clear expectations around code quality, maintainability, compilation success rate, and ease of future iteration.

During more than a week of hands-on testing, I focused on three dimensions: code generation quality, natural language expression style, and how the reasoning process affects actual development efficiency. I singled out these dimensions because in real engineering work, whether a model “can write code” is no longer the only criterion. What matters more is whether the code it produces is easy to maintain, whether it aligns with existing project conventions, and whether the interaction is efficient and controllable.

This article is not a head-to-head review of any single product. Instead, it aims to abstract observations from hands-on testing into general judgments that can help developers avoid pitfalls when selecting and integrating LLMs into their engineering workflows.

2. Code Generation Quality: Everyone Can Deliver, but Details Tell the Story

Early in the project, I asked different models to implement the same set of feature modules, including basic data structure operations, API request wrappers, component decomposition, and some build configuration adjustments. The results showed that as long as the prompts were clear enough, most models could produce a runnable solution. In other words, at the level of “functional completeness,” the gap between models was not significant.

What truly separated them was the maintenance cost after code generation.

Some models produced very clean code structures with clear responsibility boundaries, consistent naming conventions, and no unnecessary coupling between modules. This kind of code could go straight into the project repository without major modifications, and ongoing maintenance was relatively painless. Other models, while capable of achieving the same functionality, tended to exhibit the following problems:

• Over-abstraction or under-abstraction, reducing code readability;
• Arbitrary variable naming that lacked business semantics;
• Error handling logic stacked together without proper layering;
• Type design that was either too conservative or too aggressive, hindering future extension;
• Comments that were inconsistent with the implementation, misleading maintainers.

A particularly common issue was so-called “spaghetti code.” This typically manifested as overly long logic chains, deeply nested conditionals, and functions with unclear responsibilities. While the model could correct these issues in follow-up conversations, each correction meant additional communication overhead.

I also observed that some models more reliably followed ownership and lifetime constraints when generating systems-level language code, and were more cautious about error type handling. In frontend build tooling code, some models tended to generate more verbose configurations that required manual trimming. This suggests that models do not all have the same depth of understanding when it comes to language ecosystems and engineering conventions.

Therefore, if “does it run” is the only evaluation criterion, long-term maintenance costs may be overlooked. I recommend adding the following checks to any practical evaluation:

1. Can the code be merged directly without large-scale refactoring?
2. Are module boundaries clear, and are there circular dependencies?
3. Is error handling consistent and traceable?
4. Are types and interface designs stable and easy to extend?
5. Does the code follow existing project style and directory conventions?

3. Text Expression: How Model “Tone” Affects Collaboration

Beyond code, generative models handle a significant amount of text work in the development process—writing commit messages, organizing API documentation, drafting technical proposals, responding to review comments, and more. As a result, text expression style directly affects the collaboration experience.

Without special prompting, different models show markedly different expression styles. Some tend to use abundant abbreviations and industry jargon, coming across as a “seasoned engineer,” but this is not appropriate for formal documentation or external communication. Others adopt an overly enthusiastic tone, frequently using affirmations and praise that actually undermine the objectivity of technical judgment.

More notably, the ability to adjust expression style based on context deserves attention. In my tests, I asked models to switch to a “formal technical documentation style,” a “concise weekly report style,” or a “newcomer-friendly explanation style.” The vast majority of models understood these instructions, but the execution varied. Some adjusted thoroughly, with noticeable changes in sentence structure, word choice, and paragraph rhythm. Others merely swapped a few words while retaining their original tone throughout.

For teams that frequently collaborate with models on writing tasks, the importance of this capability may be underestimated. A stable, adjustable expression style can significantly reduce manual editing time and allow generated content to flow directly into documentation systems or knowledge bases.

I recommend explicitly specifying style constraints during use, for example:

• Avoid unnecessary abbreviations;
• Maintain an objective, restrained technical tone;
• Avoid emotional embellishment and excessive praise;
• Clearly flag uncertain content;
• Keep paragraph length within a readable range.

These constraints may seem simple, but they can substantially improve the usability of generated output.

4. Reasoning Efficiency: How Chain-of-Thought Length Affects Real Development Rhythm

The most noticeable experiential difference in this round of testing came from the reasoning process. Different models varied enormously in internal reasoning length when answering complex questions. Some models would output extremely long thought processes before generating a final answer—sometimes several times longer than the answer itself. This “inner monologue” can indeed improve accuracy in certain scenarios, but it also introduces practical efficiency problems.

Take API request layer development as an example. The same task could take more than 50% longer on one model compared to another. This stems from two factors: lengthy reasoning processes and generally average API response speeds. When combined, the time from receiving a prompt to returning usable code is significantly extended.

In interactive development scenarios, the impact of this delay on experience is very apparent. Developers often need to rapidly iterate, ask follow-up questions, and verify results in real time. If every step involves a long wait, the overall development rhythm gets disrupted. This is especially true when handling functionality that is not particularly complex—excessively long reasoning seems unnecessary.

Of course, reasoning length does not always equal waste. For problems involving multi-step dependencies, complex boundary conditions, or design trade-off decisions, a longer reasoning chain can indeed produce a more complete answer. But the question is whether a model can dynamically adjust reasoning intensity based on task difficulty, rather than applying the same “deep deliberation” to every problem.

From an engineering perspective, efficiency evaluation cannot look only at single-generation quality. It must also consider effective output per unit of time. Even if a model produces slightly higher code quality, if its response time is noticeably longer, its overall value in a real project may be diluted.

5. Selection Recommendations: Bring Model Capabilities into an Engineering Framework

Based on the observations above, I believe the value assessment of LLMs in the development process should shift from “point-by-point capability comparison” to “engineering fit evaluation.” The following points can serve as a reference framework for model selection:

1. Clarify Scenario Priorities

If a project demands extremely high code quality and the team has strong code review capabilities, prioritize models that generate more stable code. If the project moves fast with frequent interactions, response speed and reasoning efficiency should be given higher weight.

2. Build Quantifiable Evaluation Samples

Do not judge based on impressions alone. Prepare a set of test cases covering common development tasks—API development, component decomposition, type design, error handling, documentation generation—and evaluate each model uniformly, recording metrics such as pass rate, number of revision rounds, and average time consumed.

3. Recognize How Prompt Engineering Amplifies the Experience

Many models perform averagely under default settings, but their output quality improves noticeably after proper system prompts and role definitions. When evaluating models, test both “bare usage” and “configured prompting” scenarios to avoid underestimating a model’s potential.

4. Incorporate Reasoning Efficiency into the Cost Model

Beyond direct API costs, time costs and developer attention drain are equally important. For high-frequency interaction scenarios, response speed may affect the overall experience more than occasional differences in code quality.

6. Conclusion: No Perfect Model, Only Better Engineering Combinations

After this round of testing, my biggest takeaway is this: the new generation of large language models has made clear progress in code generation, text expression, and complex reasoning, but the differences between them increasingly show up in details and style rather than a simple “usable vs. unusable” divide.

For developers, rather than searching for a model that is “the best across the board,” it is more practical to build an executable, iterable evaluation mechanism based on your own project characteristics and team habits. Bringing code quality, expression stability, reasoning efficiency, response speed, and prompt controllability into a unified framework will lead to judgments that are closer to real engineering needs.

As model capabilities continue to evolve, this kind of observation and accumulation based on real project scenarios will be more valuable than any single-point performance leaderboard.