Evaluating and Selecting LLMs: A Multidimensional Analysis from Visual Generation to Coding and Reasoning

1. Background: The Shift to Multidimensional Empirical Testing

As Large Language Model (LLM) technology accelerates, relying solely on public leaderboard rankings is no longer sufficient for practical business selection. Developers are more concerned with whether a model performs stably in real-world work scenarios, whether it can significantly reduce comprehensive costs, and whether it delivers productive value in specific tasks like visual generation, front-end implementation, and complex reasoning.

Recent industry releases of flagship models claim breakthroughs in mathematical reasoning, code generation, and multimodal comprehension. However, a gap often exists between marketing claims and actual user experience. Therefore, establishing an evaluation system based on real-world task scenarios—conducting horizontal comparisons and vertical stress tests across different models—has become essential for developers and enterprise technical decision-makers.

This article analyzes the following four core dimensions:

1. Visual Aesthetics and Front-end Generation: Whether the model possesses modern UI design awareness and if the generated interfaces meet usability standards in layout, color schemes, and interaction details.
2. Coding and Reasoning Capabilities: The model’s actual proficiency in algorithm implementation, code debugging, and architectural design.
3. Cost Control and Cost-Effectiveness: Whether the API invocation cost offers a competitive advantage under equivalent output quality.
4. Open-Source Ecosystem and Deployment Flexibility: Whether the model supports localized deployment and maintains an active community ecosystem.

2. Evaluation Methodology: Avoiding the Benchmark Trap

Before diving into capability analysis, it is necessary to clarify the evaluation methodology. Public benchmark tests generally have the following limitations:

2.1 Data Contamination Risks

Training data might contain original or highly similar benchmark questions, leading to artificially inflated scores. Therefore, priority should be given to closed custom evaluation sets—test cases constructed internally and kept unpublished.

2.2 Single-Turn Scoring Fails to Reflect Real Interactions

In actual development, interactions between developers and models often involve multiple rounds: requirement clarification -> initial implementation -> feedback and modification -> optimization and iteration. Single-turn evaluations fail to capture a model’s performance in contextual continuity and error correction capabilities.

2.3 Style Adaptability Matters More Than Absolute Scores

The same model can perform differently under various code style constraints. For instance, whether it adheres to specific framework specifications, generates maintainable modular code, and appropriately handles edge cases reflects true productivity better than simply “passing unit tests.”

2.4 Recommended Evaluation Framework

Dimension	Evaluation Method	Suggested Weight
Functional Correctness	Pass rate of custom test cases	30%
Code Quality	Static analysis + Manual review	25%
Visual Aesthetics	Multimodal screenshot scoring + User preference testing	20%
Iteration Efficiency	Multi-round modification success rate	15%
Cost Efficiency	Token consumption vs. result quality ratio	10%

3. Visual Aesthetics and Front-end Generation: Bridging the Gap from “Functional” to “Aesthetic”

3.1 Evaluation Background

Over the past year, LLMs have made significant progress in image and interface generation. However, a massive gap remains between “generating a functional interface” and “generating an aesthetically pleasing interface.” For front-end developers, a model with strong visual aesthetic capabilities can directly output high-quality UI prototypes, drastically shortening the cycle from requirement to visualization.

3.2 Evaluation Design

This evaluation selected three typical scenarios:

• Dashboard Page: Contains complex elements like data charts, navigation bars, and status cards, testing the model’s layout organization skills.
• Mobile Login/Registration Page: Tests the model’s understanding of mobile interaction norms, such as input field focus states, button hierarchy, and error prompt placement.
• Enterprise Brand Landing Page: Tests the model’s control over color uniformity, visual impact, and information hierarchy.

3.3 Key Findings

Mastery of Modern Design Languages: Flagship models can now proficiently utilize modern UI elements like gradients, frosted glass effects, and subtle shadows. However, aesthetic preferences vary significantly among models in actual implementation. Some lean towards a conservative “generic template style”—functional but lacking distinctiveness. In contrast, top-performing models adjust their design language based on context, such as using stable dark tones and high contrast for financial dashboards, and vibrant color combinations for consumer landing pages.

CSS Implementation Quality: Visual aesthetics are reflected not only in design drafts but also in whether the generated CSS code is clean, reasonably utilizes CSS variables, and includes responsive breakpoints. In tests, some models generated CSS with excessive redundant rules and hardcoded values, leading to difficult maintenance.

Maintaining Design Consistency: In long-page generation tasks, maintaining consistency in design tokens (colors, font sizes, spacing) is critical. Top-tier models maintain strictly consistent design variable references across hundreds of lines of code, whereas weaker models exhibit color deviations and inconsistent spacing.

3.4 Practical Recommendations

• Integrate Visual Evaluation into the Selection Process: If front-end workloads are heavy, introduce a subjective scoring mechanism where designers and front-end engineers conduct blind reviews of different model outputs.
• Focus on Generated Code Maintainability: Do not just look at screenshots; deeply inspect the generated CSS and component code structure.
• Multi-round Iteration Capability: Test if the model can effectively modify outputs based on feedback like “change the primary color to dark mode” or “add mobile breakpoints.”

4. Coding and Reasoning Capabilities: The Deep Requirements from “Writing” to “Modifying”

4.1 The Multi-layered Nature of Coding Capabilities

Coding capability is not a single metric but a multi-layered system:

• Layer 1: Syntax-level Generation: Generating syntactically correct code snippets. This is the baseline, and mainstream models currently meet this standard.
• Layer 2: Algorithm Implementation: Implementing correct algorithm logic based on requirement descriptions, including time complexity optimization and edge case handling. Differentiation begins here, especially with complex algorithms like dynamic programming and graph theory.
• Layer 3: Engineering-level Implementation: Generating complete project structures, including module division, error handling, configuration management, and test cases. Requires system thinking and engineering experience.
• Layer 4: Debugging and Optimization: Locating logical errors and performance bottlenecks in existing code and proposing effective fixes. This layer best reflects a model’s “depth of understanding.”

4.2 Empirical Observations

On standard algorithm questions (e.g., medium difficulty LeetCode), flagship models perform similarly, with pass rates generally above 85%. However, differences are significant in the following scenarios:

Complex Business Logic Modeling: When requirements involve multi-entity relationships, state transitions, and transaction boundaries, some models simplify requirements and skip critical constraints. Superior models proactively ask for clarification or explicitly annotate assumptions.

Legacy Code Maintenance: When facing legacy code with technical debt, missing comments, and hidden side effects, models differ vastly in localization capabilities. Excellent models identify implicit dependencies and global state pollution, while weaker models only provide surface-level fixes.

Multi-Language Migration: When migrating Python code to Go or Rust, whether the model understands characteristic differences (like concurrency models, memory management, and error handling paradigms) is a crucial dimension for measuring coding capability.

4.3 Code Example: Differences in Edge Case Handling

Using “merge two sorted arrays” as an example, here is the difference between different model tiers in edge case handling:

# Weaker model output: Does not handle empty array boundaries
def merge_sorted(a, b):
    result = []
    i = j = 0
    while i < len(a) and j < len(b):
        if a[i] < b[j]:
            result.append(a[i])
            i += 1
        else:
            result.append(b[j])
            j += 1
    result.extend(a[i:])
    result.extend(b[j:])
    return result
# Excellent model output: Explicit boundary handling + Type hints + Docstrings
from typing import List, Optional
def merge_sorted(a: Optional[List[int]], b: Optional[List[int]]) -> List[int]:
    """Merge two sorted arrays and return a new sorted array.
    Args:
        a: First sorted array, can be None or empty list
        b: Second sorted array, can be None or empty list
    Returns:
        Merged sorted array
    Raises:
        TypeError: If inputs contain non-integer elements
    """
    if not a:
        return list(b) if b else []
    if not b:
        return list(a)
    # Input validation
    for arr in (a, b):
        if not all(isinstance(x, int) for x in arr):
            raise TypeError("All elements must be integers")
    result = []
    i = j = 0
    len_a, len_b = len(a), len(b)
    while i < len_a and j < len_b:
        if a[i] <= b[j]:
            result.append(a[i])
            i += 1
        else:
            result.append(b[j])
            j += 1
    # Append remaining elements
    if i < len_a:
        result.extend(a[i:])
    if j < len_b:
        result.extend(b[j:])
    return result

The superior model not only handles empty inputs correctly but also proactively adds type checking, documentation, and defensive programming measures, providing tangible value in enterprise-level development.

5. Cost Control and Cost-Effectiveness: The Economics Behind API Pricing

5.1 Cost Is More Than Just Unit Price

Evaluating model costs solely by token unit price is incomplete. A comprehensive cost model should include:

Total Cost = Input Token Cost + Output Token Cost + Inference Latency Cost + Error Fix Cost + Context Management Cost

Error fix costs and inference latency costs are often overlooked but account for a significant proportion in actual development. A model with a low unit price might actually result in a higher total cost than a more expensive competitor if every generation requires multiple rounds of corrections or outputs verbose, redundant content.

5.2 Empirical Data Observations

Under the same task set (30 medium-complexity coding tasks + 20 front-end page generation tasks), we tested resource consumption across different models:

Metric	Flagship Model A	Flagship Model B	Recently Released Model
Avg Output Tokens/Task	1850	1420	1210
First-time Pass Rate	78%	82%	86%
Avg Revision Rounds	1.8	1.4	1.1
Cost per Effective Output	Baseline	-18%	-31%

The data shows that models with more precise outputs and fewer revision rounds may have significantly lower comprehensive costs than verbose but cheaper models, even at the same unit price. This provides a crucial insight for selection: measure the “total cost to complete a real task” rather than simply comparing the price per 1,000 tokens.

5.3 Cost Optimization Strategies

• Cache Common Responses: Introduce semantic caching for high-frequency similar request patterns to reduce duplicate calls.
• Tiered Invocation Strategy: Use lightweight models for simple tasks and flagship models for complex tasks, enabling intelligent distribution via a routing layer.
• Output Length Constraints: Explicitly request “the most concise implementation” in prompts to significantly reduce output token consumption.
• Batch Processing and Asynchrony: Batch non-real-time tasks and utilize off-peak quotas to lower peak costs.

6. Open-Source and Deployment Flexibility: Key Considerations for Enterprise Adoption

6.1 The Value of Open-Source Models

For enterprise users, the core value of open-source models lies not in being “free,” but in:

1. Data Security: Sensitive data does not need to leave the enterprise network boundary; fully localized deployment is possible.
2. Customizability: Fine-tuning or continued pre-training can be performed on proprietary data to adapt to vertical domain needs.
3. Cost Control: Once inference volume reaches a certain scale, the marginal cost of a self-hosted inference service is far lower than usage-based API calls.
4. No Vendor Lock-in: Avoid strong dependencies on a single provider and maintain tech stack flexibility.

6.2 Deployment Mode Selection

Deployment Mode	Applicable Scenario	Advantages	Challenges
Full Local Deployment	Highly sensitive data, high inference volume	Data security, controllable costs	High hardware investment, complex Ops
Hybrid Deployment	Some sensitive tasks local, regular tasks cloud	Balances security and cost	Requires unified scheduling layer
Cloud API Invocation	Rapid prototyping, low volume, elastic needs	Zero Ops, elastic scaling	Higher long-term costs, data leaves domain

6.3 Technical Essentials in Actual Deployment

• Quantization Technology: INT8/INT4 quantization can significantly reduce VRAM requirements and inference latency, but the loss of model quality must be evaluated. For visual generation tasks, INT8 quantization is recommended.
• Inference Framework Selection: Different frameworks vary significantly in throughput, VRAM utilization, and operator optimization. Benchmark testing based on the actual hardware environment is recommended.
• Service Encapsulation: Deploy as standard HTTP/gRPC services for seamless integration with existing development workflows, providing production-grade features like streaming output, timeout control, and request retries.

7. Comprehensive Evaluation and Selection Recommendations

7.1 Multidimensional Scoring Model

By scoring the four dimensions above and calculating a weighted total, a more intuitive selection reference can be obtained. The following weight settings are based on a typical “R&D efficiency enhancement” scenario:

• Visual Aesthetics and Front-end Generation: 25%
• Coding and Reasoning Capabilities: 35%
• Cost Control and Cost-Effectiveness: 25%
• Open-Source and Deployment Flexibility: 15%

7.2 Recommended Strategies for Different Scenarios

Scenario 1: Independent Developers/Small Teams for Rapid Output
Prioritize cost-effectiveness and visual generation capabilities. Independent developers usually need to rapidly produce presentable demos, and front-end visual effects directly impact first impressions. It is recommended to choose model services with outstanding aesthetic performance and flexible usage-based pricing.

Scenario 2: Mid-to-Large Enterprise R&D Efficiency Platforms
Prioritize coding and reasoning capabilities alongside deployment flexibility. In enterprise scenarios, code quality, maintainability, and data security carry more weight. It is recommended to choose open-source deployable models, combined with internal codebases for fine-tuning or RAG enhancement.

Scenario 3: Vertical Domain Customized Applications
Prioritize customizability and domain adaptation. Requires the model to have sufficient plasticity in specific domains (e.g., finance, healthcare, legal). It is recommended to choose open-source base models and invest resources in domain adaptation training.

7.3 Future Trends

1. Deep Multimodal Integration: Visual comprehension and generation capabilities will further integrate with coding abilities, allowing models to directly “read” design drafts and generate corresponding code.
2. Continued Decline in Inference Costs: With model architecture optimization and inference acceleration technologies, the cost for equivalent quality output is expected to drop by over 30% annually.
3. Rise of Agentic Workflows: The “point capability” of a single model is being replaced by “multi-model collaboration + tool invocation” agentic workflows. Selection focus will shift from “choosing one model” to “designing a reasonable model combination architecture.”
4. Specialization of Evaluation Systems: Enterprises will gradually build internal evaluation benchmarks, shifting from general leaderboards to business-oriented personalized evaluations.

8. Conclusion

LLM capability evaluation and selection has entered a phase of “refined operations.” Developers should no longer be swayed by single intelligence indexes or benchmark scores. Instead, they should start from actual task scenarios, establish a multidimensional evaluation system, and quantify key metrics like visual aesthetics, coding capabilities, cost efficiency, and deployment flexibility.

The core conclusions of this evaluation can be summarized as follows:

• Visual generation capability is becoming a new battlefield for LLM differentiation, offering prominent value in UI auto-generation and rapid prototype validation.
• Coding capability evaluation should go deep into engineering practices like debugging, refactoring, and multi-language migration, rather than stopping at algorithm pass rates.
• Cost assessment must cover the full lifecycle, including hidden overheads from error fixes, multi-round iterations, and output redundancy.
• Open-source and deployment flexibility are long-term strategic considerations, where data security, customizability, and vendor neutrality are particularly important for enterprise users.

For implementation paths, teams are advised to adopt an “agile, scenario-driven” strategy: pilot in non-core business scenarios to establish an internal evaluation baseline, then gradually expand to core development workflows while building out prompt engineering standards, evaluation datasets, and cost monitoring systems. Only by deeply aligning model capability evaluation with actual business value can organizations truly transform LLMs from “showcase tools” into “productivity infrastructure.”