Claude Opus 5.5 takes lead in new data analysis reliability benchmark

The new leaders in data analysis

The latest round of the EDA Benchmark has identified a new elite group of models capable of handling complex data tasks. Claude Opus 5.5 has taken the top spot, showing significant progress over previous measurements. It outperformed the previous leader by more than 0.16 points in its reliability-adjusted score.

Following closely behind is GPT-6 Astra, which secured the second position. While it maintains a high level of performance, it shows more instability than the leading Claude model. These two models now form a distinct tier of high-performing systems for exploratory data analysis.

The benchmark is designed to help teams determine if AI can be trusted in real-world workflows. These tasks involve inspecting files, cleaning inconsistent records, and reconstructing events from messy data sources. As deepsense.ai reported, the goal is to see if a model can reach the right outcome repeatedly and within specific boundaries.

Measuring consistency over accuracy

A key takeaway from this report is that a model's average score does not always tell the full story of its utility. The EDA Benchmark uses two different metrics to evaluate performance: a mean score and a reliability-adjusted score. The mean score represents the average analytical quality across multiple attempts at the same task.

The reliability-adjusted score is more rigorous because it rewards models that produce consistent results. It uses a specific formula to penalize the coefficient of variation, which measures how much the results fluctuate between runs. A model with a high mean but high variability will see its score drop significantly in the adjusted ranking.

This distinction is vital for production environments where reliability is paramount. For example, a model might occasionally produce a brilliant answer but fail on the same prompt the next time it is run. In a business setting, such volatility makes it difficult to integrate the AI into automated systems of record.

Comparative performance trends

The data reveals interesting patterns when comparing different model families. GPT-6 Sol, for instance, has a higher mean score than the older GPT-5.6 Sol, but it actually ranks lower in reliability. This happens because GPT-6 Sol reaches higher peaks but is more volatile across the five repeated trajectories per problem.

Conversely, some models show the opposite trend where their average quality is offset by instability. Gemini 3.8 Flash has a higher mean than Claude Fable 5, yet both ended up with the same reliability-adjusted score of 0.42. This indicates that the extra analytical power of the Gemini model is negated by its lack of consistency.

Even some of the weakest models in the test showed that stability can be a competitive advantage. DeepSeek Reasoner had the lowest mean score in the benchmark but achieved a higher reliability-adjusted score than several models with better averages. This confirms that a model's usefulness depends heavily on the predictability of its output.

What this means for AI agents

The focus on reliability is particularly important for the development of agentic systems. Unlike a standard text-generation model that provides an answer for a human to inspect, an agent can take actions. An agent might issue a refund, update a support ticket, or write directly to a company's primary database.

Before a company allows an AI to perform these actions, they need evidence of dependable execution. The EDA Benchmark provides this evidence by testing models on 10 synthetic data-analysis tasks across diverse domains. Each task is completed five times to ensure the results are not just lucky guesses but repeatable successes.

By identifying models like Claude Opus 5.5 that maintain high scores across all 50 total trajectories, developers can make safer choices. This data helps teams move away from