How to Scientifically Evaluate LLM Coding Ability and Inference Efficiency

1. Why a High Benchmark Score Does Not Mean It Is Good for Daily Work

Public benchmarks answer the question “can it solve this problem?” What developers actually care about is “if I hand this task to it, how many rounds of rework will it take, how long will I wait, and will my colleagues be willing to take over the code it writes?” These two things are not equivalent:

• Benchmark problems are mostly isolated questions with clean context and clear boundaries, whereas in real projects a model must deal with existing code conventions, historical baggage, and vague requirement descriptions;
• Benchmarks typically only count “right or wrong,” not “how long it took,” “how many rounds of revision were needed,” or “how many tokens were consumed.”

Take a medium-complexity project consisting of a systems-level programming language backend plus a modern frontend build chain. After more than a week of continuous hands-on testing covering all daily development tasks, a counterintuitive conclusion emerges: current mainstream flagship models almost all meet the bar on functional correctness—not a single one fell short on functional requirements. In other words, at this capability level, “can it build it” no longer provides any differentiation; what truly separates daily experience is the following three dimensions.

2. Dimension One: Code Quality—From “It Runs” to “It Is Maintainable”

1. Correctness Is Only the Entry Ticket

For feature development on a project of ordinary complexity, top-tier models can basically all write code that passes tests. If evaluation stops at “is the feature implemented,” it is easy to conclude that “they are all about the same,” thereby missing the differences that are actually worth weighing. Of course, if your project is significantly more complex (distributed consistency, low-level performance optimization, etc.), the correctness floor becomes a dividing line again, so the task set must match your own real task distribution.

2. Quality Stratification Shows Up in Structure and Style

Clear stratification can be observed in hands-on testing:

• First-tier outputs have clear structure, appropriate naming, and error handling consistent with existing project conventions, approaching a “ready to merge” state;
• Second-tier outputs have no problems in the main logic, but write typical “AI-style spaghetti code” in small places: excessively nested conditional branches, one function doing three things, copy-pasted similar logic. Such problems usually have a small blast radius and can be fixed by spending one more round of conversation pointing them out, but they consume extra rounds of human-machine interaction, and accumulated over time that becomes a perceptible efficiency gap.

3. Turn Maintainability into a Scorable Item

Do not draw conclusions from impressions; quantify with a checklist, for example:

Check Item	Description
Naming consistency	Do new symbols follow the project’s existing naming style
Single responsibility	Does a function/module do only one thing
Error handling	Are exception paths complete, and are any errors swallowed
Duplication	Is code copied that could have been extracted into shared logic
Change boundary	Did it “refactor” parts it should not have touched

Score each item on a 0–2 scale and average across multiple tasks, and “code quality” turns from an adjective into a set of numbers that can be compared across models.

3. Dimension Two: Writing Style and Controllability—An Underrated Experience Variable

Beyond code, how a model “talks” directly affects daily experience, especially when it is used to write comments, write documentation, or produce design explanations.

1. Default Writing Styles Differ Enormously

Without any preset instructions, the default writing styles of various models vary widely: some sound like a seasoned IT veteran, full of abbreviations and industry jargon, extremely unfriendly to newcomers and cross-functional colleagues; some have a highly fixed tone that cannot be changed across several version updates; and some are overly polite, filled with “this is a great question”-style flattery that dilutes information density. Default style does not necessarily determine the ceiling, but it determines the floor of the out-of-the-box experience.

2. The Key Is “Can It Change Reliably”

A more valuable test item is controllability: explicitly ask it to switch to a different style, then see whether it can actually execute that reliably. In hands-on testing, some models get it right in one shot and maintain it thereafter, while others quietly revert to their old ways after two or three rounds. A reference test prompt:

Please rewrite the following explanation in the style of "technical documentation for new colleagues":
1. Do not use any unexplained abbreviations or internal jargon;
2. Lead with conclusions, put details and reasons afterward;
3. Keep each paragraph to no more than three sentences.

For teams that want to embed models into internal toolchains and auto-generate documentation, the weight of style controllability should even be higher than that of default style itself.

4. Dimension Three: Inference Efficiency—The Real Cost Ignored by Benchmarks

This is the most easily overlooked dimension, yet it directly determines daily efficiency.

1. Chain-of-Thought Is a Double-Edged Sword

Longer internal reasoning helps a model self-correct on complex tasks, but when it grows into an excess of “inner monologue”—repeatedly second-guessing and rewriting even for a simple task—the benefits start to reverse. Hands-on testing shows that chain-of-thought length varies enormously across models, in severe cases by more than a factor of two.

2. End-to-End Latency Is the Real Metric

Long chains of thought, compounded by mediocre API throughput, make things worse: to complete the same task, some models take about 50% more wall-clock time than other candidates. For an occasional large task this is just “a bit slower,” but for a development workflow that issues dozens or hundreds of calls every day, it is real lost work hours—and the extra tokens consumed by long chains of thought will ultimately show up on the bill.

It is recommended to record end-to-end metrics directly in evaluation. An illustrative script:

import time
def profile_model(call_fn, prompt, samples=3):
    """Wrap each candidate model uniformly and record end-to-end latency and token consumption"""
    stats = []
    for _ in range(samples):
        t0 = time.perf_counter()
        out = call_fn(prompt)
        stats.append({
            "wall_time": time.perf_counter() - t0,  # end-to-end latency
            "tokens": out.usage.completion_tokens, # output tokens, including reasoning
        })
    return stats

Using the same task, the same prompt, and multiple samples with the median taken, the efficiency gap between models becomes very intuitive. It is also recommended to track time-to-first-token (which determines the subjective sense of waiting for a response to “start” under streaming output) and throughput rate (which determines the total duration of long outputs).

5. Assembling the Three Dimensions into a Process

1. Build a task set: Extract 10–20 tasks from real projects, covering four categories—new features, bug fixes, refactoring, and documentation—with complexity matching daily work; do not deliberately create hard problems;
2. Fix variables: Use a unified prompt template and context injection strategy (truncation rules, retrieval method), and take 3 samples per model to avoid single-run luck affecting conclusions;
3. Blind scoring: Hide model names and score according to a weight table of “correctness 30% / maintainability 25% / writing style 15% / efficiency 20% / stability 10%,” where stability examines the consistency of results across multiple samples;
4. Record process metrics: Completion time, number of manual follow-up rounds, and token consumption all go into the table;
5. Output scenario-specific conclusions: Do not output “model X is the strongest”; instead output “under a given tech stack and task distribution, choose which model for quality-first / cost-first / speed-first goals respectively.”

6. From Personal Testing to Team Adoption

To migrate conclusions from personal testing to teams and production environments, engineering dimensions must be added: API stability and rate-limiting policies, data compliance and private deployment options, permissions and auditing, and usage-based cost models. If the conclusions point to cloud deployment, platforms such as Baidu AI Cloud allow “platform capability” and “model capability” to be evaluated separately—the former determines the engineering floor, the latter determines the experience ceiling, and scoring them together would obscure weaknesses.

7. Conclusion

The most valuable stance toward a model or any version upgrade is not to ask “is it the strongest,” but to answer “in which dimensions does it lead, what is the cost, and does it match my scenario.” Correctness is long past being the dividing line among top-tier models; code quality, style controllability, and inference efficiency are the real variables of daily experience. Replace ad-hoc impressions with a fixed task set and scoring table, and no matter which version is released next, you can produce your own reproducible answer within a week.