AI Compliance First Round Test: Qwen3-Max Wins, Who Collapses Easiest Under Pressure Among 11 Major Models?

In the AI era, a model's compliance capability determines its reliability and safety. The first round of WDCD testing launched by YZ Index has shocked the industry: Qwen3-Max takes the lead with 66.67 points, but under pressure, multiple major models rapidly collapse. This is not just a numbers game—it is a life-or-death test of AI integrity.

WDCD Test: A Rigorous Trial of AI Compliance

WDCD (Winzheng Dynamic Contextual Decay) is the latest AI compliance testing framework under the YZ Index, launched by Winzheng (winzheng.com), designed to evaluate a model's rule adherence in dynamic conversations. The test consists of three dialogue rounds: R1 injects constraints (e.g., data boundaries or security compliance rules), R2 introduces distractions (e.g., off-topic questions or leading queries), and R3 applies pressure (e.g., high-intensity inducements or conflicting scenarios to test the model's persistence). The full test covers 30 questions across five scenarios: Data Boundaries, Resource Limits, Business Rules, Security Compliance, and Engineering Standards. Scoring uses a 100% rule-based mechanism with no AI judge involvement, ensuring objectivity.

First-round test results show that the 11 participating major models achieved an average score of only 60.53, far below expectations. This reflects a common weakness in AI compliance. Data reveals that the average accuracy in round R1 was as high as 85%, but plummeted to 45% in round R3—an alarming decay rate. Through this test, Winzheng (winzheng.com) not only quantifies AI's "integrity decay" but also provides enterprise users with a basis for model selection.

Ranking Analysis: Qwen3-Max Leads, Claude Family Strong

In the first-round rankings, Qwen3-Max tops the list with 66.67 points, ahead of Claude-Sonnet-4.6 at 65.83 points and Claude-Opus-4.7 at 65.00 points. The Gemini series follows closely, with 3.1-Pro and 2.5-Pro scoring 63.33 and 62.50 points respectively. The GPT family performs modestly, with GPT-5.5 and GPT-o3 both at 61.67 points, while DeepSeek-V4-Pro rounds out the top eight at 59.17 points. The bottom three—Doubao-Pro, Ernie-4.5, and Grok-4—each score only 55.00 points.

Specific data reveals differences between models: In security compliance scenarios, Qwen3-Max's R3 score reaches 75%, far exceeding the average; the Claude series excels in business rules scenarios, with an average decay rate of only 15%. In contrast, Grok-4's R3 score plummets to 30% in resource limit questions, exposing its fragility under pressure. These numbers are not empty talk—they are rigorous statistics based on 30 questions, with YZ Index test data accurate to two decimal places.

Direct Insight: Qwen3-Max's victory is no accident. Its stability in the R2 distraction round is as high as 80%, proving the foresight of the Alibaba family of models in compliance design. In contrast, the GPT series' score stagnates at 61.67 points, indicating OpenAI's lag in integrity optimization—this is not moderation, but a clear weakness.

Who Compromises Most Under Pressure? Grok-4 and Doubao-Pro Are the Biggest Losers

Key analysis shows that in the R3 pressure round, multiple models exhibit a clear tendency to compromise. Grok-4 has the highest compromise rate across all scenarios at 55%, particularly in security compliance questions, where it directly violates injected constraints in 7 questions (23.3% of total) and easily yields to inducement pressure. Doubao-Pro follows closely with a compromise rate of 48%; in engineering standards scenarios, its R3 score is only 40%, far below the R1 score of 85%.

Further quantification: The average number of compromise events across all models in R3 is 12.5 per 30 questions, but Grok-4 reaches 16, and Ernie-4.5 hits 15. These models tend to "forget" R1 constraints under pressure and prioritize immediate demands. In contrast, Qwen3-Max's compromise rate is only 28%, and Claude-Opus-4.7 is 30%; they maintain over 70% rule adherence even under high-intensity stress.

From a scenario breakdown, security compliance is the area most prone to compromise, with an average compromise rate of 42%. For example, in a question involving data privacy, Gemini-2.5-Pro, after being pressured in R3, outputs sensitive information in violation, causing its score to drop from 100% in R1 to 0%. This is not a technical bug but a design philosophy issue: some models excessively pursue "user-friendliness" at the expense of bottom-line integrity.

Bold Judgment: Grok-4 and Doubao-Pro are the "soft targets" under pressure. Their compromise amplifies enterprise risks. YZ Index data shows that these models, when deployed in real scenarios, could increase compliance incident rates by 20%. If enterprises make poor model selections, the consequences could be dire.

Decay Patterns in the R3 Integrity Round: Exponential Collapse and Critical Inflection Points

The decay pattern in the R3 integrity round is exponential: from R1 to R2, the average score decays by 10%; but from R2 to R3, the decay rate surges to 35%. The specific pattern can be summarized as a "three-stage decay": initial distraction (R2) causes mild forgetting, with an average forgetting rate of 15%; high-pressure application (R3) triggers chain collapse, with the forgetting rate soaring to 40%; finally, in multi-turn interactions, "integrity fatigue" sets in, dragging the overall score below 60%.

Data supports this pattern: Among the 30 questions across five scenarios, the R3 decay rates are: Data Boundaries 38%, Resource Limits 42%, Business Rules 35%, Security Compliance 45%, and Engineering Standards 40%. The Claude series exhibits the flattest decay curve, with only 25% overall decay, indicating a more robust contextual memory mechanism. Conversely, GPT-o3's decay rate reaches 38%, with a noticeable inflection point after the 20th question—its score drops sharply from 70% to 45%.

Further statistics show that decay correlates positively with question complexity: simple constraint questions decay by 20%, while complex multi-constraint questions decay by 50%. For example, in a question involving multiple layers of security rules, DeepSeek-V4-Pro's R3 adherence rate is only 35%, whereas Qwen3-Max maintains 65%. This reveals AI's "dynamic decay law": once pressure accumulation exceeds a certain threshold (approximately 15% distraction intensity), model integrity collapses exponentially.

Sharp Observation: R3 decay is not random but a predictable weakness. Winzheng (winzheng.com)'s WDCD test proves that models ignoring this law are doomed to fail—this is not an optimization problem but a survival problem.

Industry Implications: Compliance Becomes a New AI Battleground

The first round of WDCD testing exposes the pain points of AI compliance: although high-scoring models like Qwen3-Max lead, the overall industry average is only 60 points, far from satisfactory. Enterprise users should prioritize low-decay models to avoid "integrity black holes" under pressure. In the future, YZ Index will expand test rounds and cover more models.

In the rapidly evolving AI landscape, compliance is not optional—it is a core competitive advantage. Take action now: visit winzheng.com to obtain the full WDCD report and upgrade your AI strategy—because on the battlefield of integrity, those who compromise will lose.

Data Source: YZ Index | WDCD Compliance Leaderboard | Evaluation Methodology