What's AI's go-to athlete's name⁠?

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

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Explore by prompt

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Athlete's name

Choose exactly one name for an athlete. Return only the name, with no punctuation or explanation.

Most common response

Serena Williams

94 of 300 answers, or 31.3%.

Response distribution

35 unique
Serena Williams 94 · 31.3%
Michael Jordan 56 · 18.7%
Usain Bolt 55 · 18.3%
LeBron James 24 · 8%
Simone Biles 16 · 5.3%
Lionel Messi 13 · 4.3%
Jordan 8 · 2.7%
Serena 4 · 1.3%
Alex 2 · 0.7%
Lebron 2 · 0.7%

Showing the 10 most common answers.

Result weighting of most common answer

Each completed model response has equal influence.

Model Run 1 Run 2 Run 3
Claude Fable 5 anthropic/claude-fable-5 Serena Williams Serena Williams Serena Williams
DeepSeek V4 Pro deepseek/deepseek-v4-pro Serena Williams Michael Jordan Usain Bolt
Gemini 3.1 Pro Preview google/gemini-3.1-pro-preview Usain Bolt Michael Jordan Usain Bolt
GLM 5.2 z-ai/glm-5.2 Michael Jordan Michael Jordan Michael Jordan
GPT-5.6 Sol Pro openai/gpt-5.6-sol-pro Serena Williams Serena Williams Serena Williams
Grok 4.5 x-ai/grok-4.5 Lionel Messi Serena Williams Michael Jordan
Hy3 tencent/hy3 Serena Williams Serena Williams Lionel Messi
Kimi K3 moonshotai/kimi-k3 Serena Williams Serena Williams Serena Williams
Laguna XS 2.1 poolside/laguna-xs-2.1 Lionel Messi Michael Jordan Michael Jordan
Ling-2.6-flash inclusionai/ling-2.6-flash SerenaWilliams Usain Bolt Usain Bolt
Llama 4 Maverick meta-llama/llama-4-maverick LeBron James LeBron James LeBron James
MiMo-V2.5-Pro xiaomi/mimo-v2.5-pro Michael Jordan Jordan Serena Williams
MiniMax M3 minimax/minimax-m3 Michael Jordan I need a list of names to choose from. Could you provide some athlete names you'd like me to pick from? Lance Armstrong
Mistral Small 4 mistralai/mistral-small-2603 LeBron James Serena Williams LeBron
Nemotron 3 Ultra nvidia/nemotron-3-ultra-550b-a55b Usain Bolt Simone Biles Usain Bolt
Qwen3.7 Max qwen/qwen3.7-max Usain Bolt Usain Bolt Usain Bolt
Step 3.7 Flash stepfun/step-3.7-flash Usain Bolt Michael Jordan Usain Bolt
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