AI Model Benchmarks: Where Top Performers Excel and Struggle

By shrijeetverma13 · June 27, 2026

We analyzed performance across 1,276 benchmark tests spanning multiple AI organizations and task types. The data reveals stark differences: coding tasks…

The analysis produced a horizontal bar chart and data tables showing the top 10 models ranked by their average score pct across all benchmarks. The visualization clearly displays each model's average performance, making it easy to compare which models lead the rankings.

Average score pct varies significantly across benchmark types. The bar chart shows that 'commonsense' benchmarks achieve the highest average score at 92.4% (across 113 records), while 'coding agentic' benchmarks score the lowest at 42.6% (across 52 records). This represents a nearly 50 percentage point gap between the best and worst performing benchmark categories.

The analysis reveals which organizations achieve the highest average score pct across all benchmarks. xAI tops the list with an average of 79.99%, followed closely by DeepSeek at 79.54% and Anthropic at 78.20%. A horizontal bar chart shows the top 15 organizations ranked by their average benchmark scores.

The benchmarks with the lowest average score pct — meaning they are the most difficult — are led by SWE-Bench Verified at just 42.6%, followed by LiveCodeBench (51.13%), GPQA Diamond (55.32%), AIME 2024 (59.09%), and MATH (61.33%). A horizontal bar chart and data tables have been generated showing the top 10 hardest benchmarks ranked by average score percentage.

A line chart has been generated showing how the average benchmark score (score pct) has changed across release years. The visualization plots each year on the x-axis against the average score percentage on the y-axis, with markers at each data point so you can clearly see year-over-year changes.

The analysis reveals which organizations released the most models and how their benchmark scores evolved over time. Two charts were generated: a bar chart showing model counts per organization, and a line chart tracking average benchmark score (%) over time for the top organizations. OpenAI leads with 19 models, followed closely by Anthropic (18), Google (17), Meta (16), and DeepSeek (7). Impressively, every top organization showed significant score improvement from their earliest to latest models.

The analysis compared how different organizations have grown their AI model portfolios over time. Two charts were generated: a grouped bar chart showing distinct models released per organization per year, and a velocity chart ranking organizations by their average annual release rate. Anthropic stands out as the top performer with the highest release velocity at 4.50 models per year on average.

A color-coded heatmap has been generated showing the average score pct for every organization-benchmark type combination. The heatmap uses a Viridis color scale where brighter (yellow) cells indicate higher average scores and darker (purple) cells indicate lower scores, making it easy to spot which organizations excel in which benchmark categories. Supporting data tables are also available for a detailed numerical breakdown.

The IQR-based outlier analysis successfully identified 58 outlier model-benchmark combinations across all benchmark types. Two visualizations were generated: a box plot showing score distributions per benchmark type with outlier points highlighted, and a horizontal bar chart ranking the top 10 outlier combinations by how far they deviate beyond the IQR bounds. Several data tables were also produced with detailed outlier information.

The analysis of score pct across 1,276 benchmark records reveals a left-skewed distribution (skewness = -1.00), meaning most models perform well but a tail of lower-scoring models pulls the mean down. The mean score is 73.36% while the median is higher at 79.88%, which is characteristic of left-skewed data. Two visualizations were generated: a histogram with a KDE curve showing the shape of the distribution with mean and median lines annotated, and a bar chart breaking down models by score range.