What's AI's go-to journalist's name⁠?

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

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Journalist's name

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

Most common response

Bob Woodward

12.1% of the provider-balanced result.

Response distribution

148 unique
Bob Woodward 12.1%
Walter Cronkite 6.2%
Anderson Cooper 5.9%
Sarah Chen 2.7%
Christiane Amanpour 2.5%
Alex Johnson 2%
Chrishedges 2%
David Harris 2%
Ethan Cole 2%
Jordanross 2%

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 Maria Alvarez Maya Torres Alex Morgan
DeepSeek V4 Pro deepseek/deepseek-v4-pro Walter Cronkite Christiane Amanpour Mona Chalabi
Gemini 3.1 Pro Preview google/gemini-3.1-pro-preview Marcus Vance Marcus Reid Elias Thorne
GLM 5.2 z-ai/glm-5.2 Walter Cronkite Bob Woodward Anderson Cooper
GPT-5.6 Sol Pro openai/gpt-5.6-sol-pro Maya Chen Maya Chen Maya Chen
Grok 4.5 x-ai/grok-4.5 Tom Brokaw Walter Cronkite Bob Woodward
Hy3 tencent/hy3 Lena Morales MariaFernandez Marcus Thompson
Kimi K3 moonshotai/kimi-k3 Anderson Cooper Walter Cronkite Elena Vasquez
Laguna XS 2.1 poolside/laguna-xs-2.1 Sarah Johnson David Harris Ethan Cole
Ling-2.6-flash inclusionai/ling-2.6-flash JordanRoss ChrisHedges Alex Johnson
Llama 4 Maverick meta-llama/llama-4-maverick Ava Moreno Ava Moreno Evelyn Rodriguez
MiMo-V2.5-Pro xiaomi/mimo-v2.5-pro Sarah Chen James Alex Thompson
MiniMax M3 minimax/minimax-m3 Anderson Cooper Anderson Anderson Cooper
Mistral Small 4 mistralai/mistral-small-2603 Ada Lovelace Alex Thompson Elise Hu
Nemotron 3 Ultra nvidia/nemotron-3-ultra-550b-a55b Laura Mitchell Alex Morgan Jordan Lee
Qwen3.7 Max qwen/qwen3.7-max Bob Woodward Carl Bernstein Anderson Cooper
Step 3.7 Flash stepfun/step-3.7-flash Bob Woodward Bob Woodward Bob Woodward
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