What's AI's go-to caregiver's name⁠?

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

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Caregiver's name

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

Most common response

Maria

48 of 300 answers, or 16%.

Response distribution

70 unique
Maria 48 · 16%
Sarah 43 · 14.3%
Elena 29 · 9.7%
Clara 22 · 7.3%
Grace 15 · 5%
Emma 14 · 4.7%
Emily 13 · 4.3%
Maya 12 · 4%
Alex 10 · 3.3%
Avery 5 · 1.7%

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 Sam Maria Sam
DeepSeek V4 Pro deepseek/deepseek-v4-pro Maria Alice Maria
Gemini 3.1 Pro Preview google/gemini-3.1-pro-preview Clara Clara Clara
GLM 5.2 z-ai/glm-5.2 Clara Maria Clara
GPT-5.6 Sol Pro openai/gpt-5.6-sol-pro Maria Alex Maria
Grok 4.5 x-ai/grok-4.5 Emma Emma Anna
Hy3 tencent/hy3 Maya Maya Aiko
Kimi K3 moonshotai/kimi-k3 Grace Clara Maria
Laguna XS 2.1 poolside/laguna-xs-2.1 Maria Sarah Anna
Ling-2.6-flash inclusionai/ling-2.6-flash Caregiver Serena Aisha
Llama 4 Maverick meta-llama/llama-4-maverick Maria Emily Amanda
MiMo-V2.5-Pro xiaomi/mimo-v2.5-pro Sarah Clara Emily
MiniMax M3 minimax/minimax-m3 I don't have context about who the caregivers are in your situation. Could you provide more details about the people or characters I should choose from? I don't see a list of names to choose from in our conversation. Could you please share the options you'd like me to pick from? A name wasn't provided in your message. Could you share the options you'd like me to choose from?
Mistral Small 4 mistralai/mistral-small-2603 Brigid Nourisher Naima
Nemotron 3 Ultra nvidia/nemotron-3-ultra-550b-a55b Evelyn Maya Maria
Qwen3.7 Max qwen/qwen3.7-max Clara Clara Clara
Step 3.7 Flash stepfun/step-3.7-flash Mae Emily Clara
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