WDCD Selection Guide: When Choosing Models, Stop Asking 'Who's Number One'

When enterprises choose a model, the most common question is "Who's number one now?" This question is simple and direct, but it often leads to serious misguidance. Data from WDCD Run#105 provides a clear counterintuitive answer: In the compliance dimension, there is no absolute number one—only scenario adaptation. The model with the highest total score may perform worse than lower-ranked contenders in certain critical scenarios.

Top Total Score ≠ Top in Everything

In Run#105, Qwen3-Max ranked first with a total score of 2.6 (R1:1.0, R2:0.9, R3:0.7). But that doesn't mean Qwen3-Max should be chosen for all scenarios. Four models also scored 2.5—Claude Sonnet 4.6, DeepSeek V4 Pro, ERNIE 4.5, and GPT-o3—yet their score structures are completely different. Claude Sonnet 4.6 achieved a perfect R2 of 1.0, making it the strongest in resisting interference in long documents; ERNIE 4.5 had an R3 as high as 0.8, unmatched among all models in steadfastness under pressure. If an enterprise's core risk is constraint forgetting in long-document scenarios, Claude Sonnet 4.6 is a better fit than the higher-scoring Qwen3-Max; if the core risk is users pressuring the model to overstep boundaries, ERNIE 4.5 is the best choice.

Lowest Rank ≠ Not Worth Using

Grok-4 ranked 11th with a total score of 2.0, but its R1 score was a perfect 1.0—in constraint comprehension ability, it is exactly the same as the top-ranked Qwen3-Max. Grok-4's problem lies in R3 (0.2), meaning its steadfastness under pressure is extremely poor. However, if the enterprise's use case involves single-turn Q&A or auxiliary analysis without multi-turn pressure induction, Grok-4's comprehension is actually fully sufficient. Excluding it outright from the selection is a waste of resources.

Conversely, ERNIE 4.5's R1 is only 0.8—the lowest among all 11 models. Judging solely by first-round performance, it even falls short of most competitors. But its R3 is as high as 0.8, giving it a total score of 2.5, tying for second place. This characteristic of "starting slow but being most stable under pressure" is precisely what enterprises value most when they need a model to perform in high-pressure scenarios (such as customer complaint handling and compliance review assistance).

Five Types of Scenarios, Five Selection Methods

WDCD covers five types of enterprise scenarios: Data Boundary (db), Resource Limitation (rl), Business Rule (br), Security Compliance (sec), and Engineering Convention (eng). Data from Run#105 shows that security compliance scenarios have the best defense (e.g., Q237 HTTPS constraint failed only 4 out of 11), while engineering convention scenarios suffer the worst breaches (e.g., Q239 framework constraint failed 11 out of 11). This means the selection logic differs completely across industries:

The financial industry cares most about data boundaries and business rules—discount constraints (Q227: 8/11 failed) and approval processes are core risks. SaaS products are most concerned about tenant isolation and resource limitations—concurrency control (Q223: 7/11 failed) and retry constraints (Q226: 9/11 failed) directly affect system stability. AI coding products care most about engineering conventions—adherence to framework selection and code standards is the baseline for code quality.

When choosing a model, enterprises are not selecting a champion—they are selecting a scenario partner that is least likely to be persuaded to cross boundaries in their most critical risk scenarios.

Beyond the Leaderboard: A Methodology for Model Selection

Going a step further, an enterprise's own constraints are often not reflected in public leaderboards. Every company has its unique red lines: specific approval processes, specific data desensitization requirements, specific technology stack limitations. Run#105 tests general constraint scenarios, but what enterprises truly need is to write their own rules into stress tests. The correct approach when selecting a model is not to look at total score rankings, but to: First, identify the most frequent constraint types in your own enterprise; second, compare model performance in scenarios of that type; third, conduct customized R3 stress tests based on your own unique rules.

So, stop asking "Who's number one?" Instead, ask: In my industry, my processes, my permission boundaries, and my budget constraints, who is still the most reliable when pressured? Qwen3-Max with a total score of 2.6 may not be as suitable for your scenario as ERNIE 4.5 with a total score of 2.5—that is why WDCD data is more valuable than traditional rankings.