What's AI's go-to charitable cause⁠?

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

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Explore by model

Charitable cause

Choose exactly one cause for a charitable donation: Local poverty, Global health, Climate change, or Disaster relief. Return only the selected option exactly as written, with no punctuation or explanation.

Most common response

Global Health

59.4% of the provider-balanced result.

Response distribution

4 response categories
Global Health 59.4%
Climate Change 19.8%
Local Poverty 19.1%
Disaster Relief 1.7%

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 Global health Global health Global health
DeepSeek V4 Pro deepseek/deepseek-v4-pro Local poverty Local poverty Local poverty
Gemini 3.1 Pro Preview google/gemini-3.1-pro-preview Global health Global health Global health
GLM 5.2 z-ai/glm-5.2 Global health Global health Global health
GPT-5.6 Sol Pro openai/gpt-5.6-sol-pro Global health Global health Global health
Grok 4.5 x-ai/grok-4.5 Global health Global health Global health
Hy3 tencent/hy3 Local poverty Local poverty Local poverty
Kimi K3 moonshotai/kimi-k3 Global health Global health Global health
Laguna XS 2.1 poolside/laguna-xs-2.1 Global health Global health Global health
Ling-2.6-flash inclusionai/ling-2.6-flash Climate change Climate change Climate change
Llama 4 Maverick meta-llama/llama-4-maverick Global health Global health Global health
MiMo-V2.5-Pro xiaomi/mimo-v2.5-pro Local poverty Climate change Climate change
MiniMax M3 minimax/minimax-m3 Climate change Global health Global health
Mistral Small 4 mistralai/mistral-small-2603 Disaster relief Climate change Climate change
Nemotron 3 Ultra nvidia/nemotron-3-ultra-550b-a55b Global health Global health Global health
Qwen3.7 Max qwen/qwen3.7-max Global health Global health Global health
Step 3.7 Flash stepfun/step-3.7-flash Global health Global health Global health
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