What's AI's go-to ceo's name⁠?

100 AI models, 100 simple questions, asked 3 times each. Explore all 30,000 answers.

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CEO's name

Choose exactly one name for a CEO. Return only the name, with no punctuation or explanation.

Most common response

Elon Musk

9.1% of the provider-balanced result.

Response distribution

126 unique
Elon Musk 9.1%
Tim Cook 6.8%
Sarah Chen 5.8%
Satya Nadella 5.4%
John Smith 3.3%
Sundar Pichai 2.9%
Alex Johnson 2.7%
Alex Morgan 2.2%
James Anderson 2.2%
Jordan Lee 2.1%

Showing the 10 most common answers.

Result weighting of most common answer

Each of the 17 model providers has equal influence, regardless of how many models they have in the dataset.

Model Run 1 Run 2 Run 3
Claude Fable 5 anthropic/claude-fable-5 Alex Morgan Alex Morgan Morgan Reyes
DeepSeek V4 Pro deepseek/deepseek-v4-pro Elon Musk Satya Nadella Arjun Mehta
Gemini 3.1 Pro Preview google/gemini-3.1-pro-preview Eleanor Vance Alexander Pierce Marcus Vance
GLM 5.2 z-ai/glm-5.2 Satya Nadella Tim Cook Tim Cook
GPT-5.6 Sol Pro openai/gpt-5.6-sol-pro Alex Morgan Jordan Lee Alex Morgan
Grok 4.5 x-ai/grok-4.5 Elon Musk Jeff Bezos Tim Cook
Hy3 tencent/hy3 Satjiv Chatteredjee Satjiv Chatterjee Sundar Pichai
Kimi K3 moonshotai/kimi-k3 Elena Marsh Alexandra Bennett Alexandra Chen
Laguna XS 2.1 poolside/laguna-xs-2.1 David Miller Sundar Pichai James Anderson
Ling-2.6-flash inclusionai/ling-2.6-flash Jordan Lee WarrenBuffett Alex Johnson
Llama 4 Maverick meta-llama/llama-4-maverick Alexandra Thompson Samantha Rodriguez Samantha Rodriguez
MiMo-V2.5-Pro xiaomi/mimo-v2.5-pro Sarah Chen 张伟 James Chen
MiniMax M3 minimax/minimax-m3 Sarah Mitchell Tim Cook John Anderson
Mistral Small 4 mistralai/mistral-small-2603 Evan Spiegel Linda Yaccarino Mary Barra
Nemotron 3 Ultra nvidia/nemotron-3-ultra-550b-a55b Alex Morgan Morgan Lee Jordan Smith
Qwen3.7 Max qwen/qwen3.7-max Richard Sterling Victor Sterling Marcus Vance
Step 3.7 Flash stepfun/step-3.7-flash Emily Carter Sarah Chen James Carter
Most common answer Different answer 5% or less of the answers

Dataset last updated July 18, 2026.

About

ModelBias.ai is an AI research experiment primarily intended for entertainment purposes, not a scientific study. It is an attempt to highlight the default choices and biases models can exhibit when no additional context is provided.

Methodology

Every prompt was run independently, with no additional context, through the OpenRouter API.

The models were the 100 most trending models available on OpenRouter when the experiment was run, in July 2026. Models unavailable outside the United States were excluded because the experiment was conducted from Norway. Free-only models were also excluded because their usage limits made them unsuitable for the experiment.

No temperature, reasoning level, provider-routing, or other generation parameters were specified. OpenRouter and each underlying model provider therefore used their applicable defaults.

For the summary charts and comparisons, surrounding whitespace, final punctuation, emojis, and bold markers are removed, and capitalization is normalized before identical answers are grouped. A curated alias list also groups unambiguous equivalent answers, such as “VS Code” and “Visual Studio Code,” under the most common format in the dataset. The downloadable dataset preserves every model's original output.

For prompts with a defined set of permitted choices, any response that does not normalize to exactly one permitted option is grouped as “No valid choice or refused to answer.” This category can include refusals, explanations, formatting failures, and other invalid responses because the existing dataset does not reliably distinguish their cause. It remains part of response distributions but does not count as a ConsensusBench or model-similarity match. Open-ended prompts are not classified this way.

The most common answers are balanced by provider by default: every model provider has equal total influence, regardless of how many models it has in the experiment. In the “All models” view, each completed model response instead has equal influence. Tied answers are shown jointly.

Tech Stack

This project was built with Codex using GPT-5.6 Sol.

Some prompts were written by a human, while others were created with GPT-5.6 Sol.

The backend is built in PHP using the Laravel framework. Prompts were run with the Laravel queue system.

Download the data

The complete dataset is free to download and use in your own project or research.

View and download the dataset on GitHub