The LLM Stress Test: Research Exposes Hidden Character Differences amongst LLMs

New stress-testing methodology reveals that leading AI models from Anthropic, OpenAI, Google, and xAI exhibit distinct behavioral patterns and value preferences when faced with ethical dilemmas

In a world increasingly shaped by artificial intelligence, a fundamental question has emerged: Do AI models truly follow the rules they’re supposed to? A groundbreaking new study from researchers at Anthropic, Thinking Machines Lab, and Constellation has developed a systematic method to stress-test the behavioral guidelines that govern large language models. The results are eye-opening.

The research reveals that even the most advanced AI systems exhibit striking differences in their “character” showing distinct value preferences and behavioral patterns when confronted with scenarios that force difficult tradeoffs between competing ethical principles.

The Problem with AI Specifications

AI companies rely on model specifications to define how their systems should behave. These specs are essentially rulebooks that establish behavioral guidelines and ethical principles during training and evaluation. Think of them as constitutions for AI written documents that alignment systems try to enforce.

But here’s the catch: if a specification is truly complete and precise, models trained to follow it shouldn’t diverge widely when given the same input. The reality, as this research demonstrates, is far more complex.

The research team, led by Jifan Zhang along with Henry Sleight, Andi Peng, John Schulman, and Esin Durmus, identified critical challenges facing current model specifications. These include internal conflicts between principles and insufficient coverage of nuanced scenarios. Their published paper presents a methodology that automatically identifies numerous cases of principle contradictions and interpretive ambiguities in current model specs.

How They Stress-Tested AI Models

The researchers developed an innovative approach to expose weaknesses in model specifications. They generated scenarios that force explicit tradeoffs between competing value-based principles situations where models must choose between pairs of legitimate principles that cannot be simultaneously satisfied.

The scale of this research is impressive. Starting from a taxonomy of 3,307 fine-grained values observed in natural Claude traffic, the team generated more than 300,000 scenarios. This taxonomy is notably more granular than typical model specifications, allowing for a deeper examination of AI behavior.

For each pair of values, researchers created a neutral query and two biased variants that lean toward one value or the other. They then built what they call “value spectrum rubrics” that map positions on a scale from 0 to 6. A score of 0 means strongly opposing a particular value, while 6 means strongly favoring it.

The team evaluated responses from twelve frontier large language models across major providers: Anthropic, OpenAI, Google, and xAI. They measured behavioral disagreement through value classification scores and defined disagreement as the maximum standard deviation across the two value dimensions.

To ensure they captured the most revealing cases, researchers used a disagreement-weighted k-center selection with Gemini embeddings and a 2-approximation greedy algorithm. This removed near-duplicates while keeping the hard cases that truly test model specifications.

A Massive Public Dataset

The research team didn’t just publish their findings they released a comprehensive public dataset on Hugging Face for independent auditing and reproduction. The dataset includes three subsets: a default split with approximately 132,000 rows, a complete split with about 411,000 rows, and a judge evaluations split with roughly 24,600 rows.

The dataset is formatted as parquet files and released under the Apache 2.0 license, making it freely available for researchers and developers who want to examine AI behavior or test their own models.

What the Results Reveal

The findings paint a complex picture of how frontier AI models actually behave when their specifications are put to the test.

Disagreement Predicts Specification Violations

One of the most significant discoveries is that high disagreement among models strongly predicts underlying problems in specifications. When researchers tested five OpenAI models against the public OpenAI model spec, they found that high-disagreement scenarios showed 5 to 13 times higher rates of non-compliance.

This pattern suggests something crucial: the disagreements aren’t just quirks of individual models. Instead, they point to contradictions and ambiguities in the specification text itself. When models trained on the same spec behave differently, it’s a red flag that the spec needs clarification.

Missing Guidance on Quality Standards

The research uncovered another important gap: specifications lack granularity on quality within the “safe region.” Some scenarios produce responses that all pass compliance checks, yet differ significantly in helpfulness.

For example, when faced with a potentially sensitive request, one model might refuse and offer safe alternatives, while another simply refuses without elaboration. Both responses technically comply with the specification, but they differ in quality and user experience. This indicates missing guidance on quality standards that go beyond basic safety compliance.

Evaluator Models Disagree Too

Even the AI judges disagree. The researchers used three different LLM evaluators Claude 4 Sonnet, o3, and Gemini 2.5 Pro to assess compliance. These evaluator models showed only moderate agreement, with a Fleiss Kappa score near 0.42.

This moderate agreement exposes interpretive differences in how different AI systems understand the same rules. Conflicts arise around concepts like “conscientious pushback” versus “transformation exceptions” nuanced distinctions that even advanced AI systems interpret differently.

The Character of AI: Provider-Level Patterns

Perhaps the most fascinating finding is that AI models from different providers exhibit distinct “character” patterns consistent value preferences that emerge when aggregating high-disagreement scenarios.

Claude’s Ethical Focus

Models from Anthropic, particularly the Claude family, consistently prioritize ethical responsibility and intellectual integrity and objectivity. These models tend to be the most cautious in their refusal rates and often provide alternative suggestions when declining a request.

OpenAI’s Efficiency Orientation

OpenAI models show a tendency to favor efficiency and resource optimization. Interestingly, the o3 model most often issues direct refusals without elaboration a more efficient but potentially less helpful approach.

Gemini and Grok’s Emotional Depth

Google’s Gemini 2.5 Pro and xAI’s Grok models more frequently emphasize emotional depth and authentic connection in their responses. This represents a different value orientation compared to the efficiency-focused or ethics-focused approaches of other providers.

Mixed Patterns

Some values show mixed patterns across all providers. Business effectiveness, personal growth and wellbeing, and social equity and justice don’t align consistently with any particular provider, suggesting these are areas where specifications may be less clear or where different design choices lead to varied implementations.

Refusals and False Positives

The analysis revealed topic-sensitive refusal spikes across all models, with some concerning patterns of false positives. Models sometimes refuse legitimate requests that pose no actual harm.

Examples of false positive refusals include legitimate synthetic biology study plans and standard Rust programming language “unsafe” types which, despite the name, are often safe in context. These over-cautious refusals suggest that models may be applying overly broad safety filters.

All models showed appropriately high refusal rates on genuinely risky content, such as child grooming scenarios. However, the variation in how models refuse with Claude providing alternatives and o3 offering direct refusals highlights different approaches to the same safety goal.

Outliers Reveal Misalignment

Outlier analysis proved particularly valuable for identifying both safety gaps and excessive filtering. The researchers defined outliers as cases where one model diverges from at least 9 of the other 11 models tested.

Grok 4 and Claude 3.5 Sonnet produced the most outlier responses, but for very different reasons. Grok tends to be more permissive on requests that other models consider harmful, potentially indicating safety gaps. Claude 3.5, on the other hand, sometimes over-rejects benign content, suggesting excessive caution.

This outlier mining provides a useful lens for locating both ends of the spectrum: models that may need stronger safety measures and models that may be filtering too aggressively.

Why This Matters

This research represents a significant advance in how we understand and evaluate AI systems. Rather than relying on subjective assessments or “vibes,” the methodology turns disagreement into a measurable diagnostic for specification quality.

The implications are substantial for AI development and deployment. If model specifications contain contradictions and ambiguities, models trained on those specs will exhibit unpredictable behavior in edge cases. This unpredictability becomes increasingly problematic as AI systems take on more consequential tasks.

The research also challenges the assumption that all frontier models are essentially equivalent. The clear provider-level value patterns suggest that different AI companies are making different choices whether intentionally or not about what their models prioritize. Users and organizations deploying these models should be aware of these differences.

A Tool for Better AI Development

The researchers position their methodology as a tool that should be deployed to debug specifications before deployment, not after. By generating value tradeoff scenarios and measuring cross-model disagreement, developers can identify specification gaps early in the development process.

The public dataset enables independent researchers, auditors, and developers to conduct their own analyses. This transparency is crucial for building trust in AI systems and ensuring that multiple perspectives can examine how these powerful models behave.

Looking Forward

As large language models become more capable and more widely deployed, the need for clear, comprehensive specifications becomes increasingly urgent. This research provides both a wake-up call and a practical methodology for addressing specification problems.

The finding that high disagreement predicts non-compliance by 5 to 13 times under existing specs suggests that current approaches to AI alignment may need significant refinement. The moderate agreement among evaluator models (Fleiss Kappa near 0.42) indicates that even assessing compliance is more complex than it might appear.

The distinct character patterns across providers raise important questions about AI diversity and standardization. Should we expect all AI models to behave similarly, or is there value in having models with different value orientations? How do we balance consistency with the benefits of diverse approaches?

The Path Ahead

The researchers have provided the AI community with powerful tools: a systematic methodology for stress-testing specifications, a massive public dataset, and clear evidence that current specs need improvement. The next step is for AI developers to use these tools to create more robust, clearer specifications.

For users and organizations deploying AI systems, this research offers important insights. Understanding that different models have different value orientations can inform model selection. Knowing that specifications contain ambiguities can help set appropriate expectations and implement necessary oversight.

The research also highlights the importance of ongoing evaluation. As AI models evolve and take on new tasks, continuous stress-testing can help identify emerging specification problems before they lead to harmful outcomes.

Conclusion

This groundbreaking research from Anthropic, Thinking Machines Lab, and Constellation has pulled back the curtain on how frontier AI models actually behave when their specifications are rigorously tested. The results over 70,000 cases of significant behavioral divergence, clear character differences among providers, and measurable specification problems demonstrate that AI alignment is more complex than simple rule-following.

By turning disagreement into a diagnostic tool, the researchers have provided a practical path forward for improving AI specifications. The public release of their dataset ensures that the broader community can participate in this crucial work.

As we continue to integrate AI systems into critical applications, this kind of rigorous, systematic evaluation becomes essential. The research shows us not only where current specifications fall short but also provides the methodology to do better. That’s a significant step forward in the ongoing challenge of building AI systems that reliably behave as intended.