What's AI's go-to doctor's name⁠?

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

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Doctor's name

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

Most common response

John Smith

8.4% of the provider-balanced result.

Response distribution

148 unique
John Smith 8.4%
Sarah Chen 7.7%
Dr. Smith 5.9%
Gregory House 4.1%
James Wilson 3.6%
Dr. Emily Carter 3%
Smith 3%
Emily Carter 2.9%
Dr. Emily Chen 2.1%
James Carter 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 Dr Sarah Chen Dr Sarah Mitchell Amara Okafor
DeepSeek V4 Pro deepseek/deepseek-v4-pro Katherine Hartwell John Smith Smith
Gemini 3.1 Pro Preview google/gemini-3.1-pro-preview Doctor Jane Smith Alice Carter John Watson
GLM 5.2 z-ai/glm-5.2 James Smith Gregory House Gregory House
GPT-5.6 Sol Pro openai/gpt-5.6-sol-pro Amelia Chen Amelia Chen Amelia
Grok 4.5 x-ai/grok-4.5 Smith Smith Gregory House
Hy3 tencent/hy3 Dr. Emily Carter Gregory House Dr. Smith
Kimi K3 moonshotai/kimi-k3 Sarah Chen Dr Maya Patel Elena Vasquez
Laguna XS 2.1 poolside/laguna-xs-2.1 James Carter John Smith Michael Thompson
Ling-2.6-flash inclusionai/ling-2.6-flash Alexander Sarah Chen DrAlexander
Llama 4 Maverick meta-llama/llama-4-maverick Emily Waters Emily Waters Emily Waters
MiMo-V2.5-Pro xiaomi/mimo-v2.5-pro Dr Emily Chen Dr. Emily Chen Sarah Chen
MiniMax M3 minimax/minimax-m3 Dr Smith Benjamin Harrison Sarah Mitchell
Mistral Small 4 mistralai/mistral-small-2603 Jonas Dr. Elias Carter Dr. Alexander Carter
Nemotron 3 Ultra nvidia/nemotron-3-ultra-550b-a55b John Smith John Smith James Anderson
Qwen3.7 Max qwen/qwen3.7-max Arthur Sterling Samuel Bennett David Chen
Step 3.7 Flash stepfun/step-3.7-flash Olivia Johnson Emily Carter James Wilson
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