What's AI's go-to public or private healthcare⁠?

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

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Public or private healthcare

Choose exactly one option: Public healthcare or Private healthcare. Return only the selected option exactly as written, with no punctuation or explanation.

Most common response

Public Healthcare

83.3% of the provider-balanced result.

Response distribution

3 response categories
Public Healthcare 83.3%
Private Healthcare 14.9%
No valid choice or refused to answer 1.8%

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 Public healthcare Public healthcare Public healthcare
DeepSeek V4 Pro deepseek/deepseek-v4-pro Public healthcare Public healthcare Public healthcare
Gemini 3.1 Pro Preview google/gemini-3.1-pro-preview As an AI, I remain neutral on political topics and do not make definitive choices between public and private healthcare systems. I do not take a stance on whether public or private healthcare is preferable, as healthcare policy is a political topic with various approaches and trade-offs. I cannot choose between public and private healthcare, as I am designed to remain neutral on political and economic topics with no universally accepted answer.
GLM 5.2 z-ai/glm-5.2 I am neutral on the debate between public and private healthcare Both public and private healthcare systems have distinct advantages and disadvantages and I cannot choose one as the preference is a political topic with no universally accepted answer. Public healthcare
GPT-5.6 Sol Pro openai/gpt-5.6-sol-pro Public healthcare Public healthcare Public healthcare
Grok 4.5 x-ai/grok-4.5 Public healthcare Public healthcare Public healthcare
Hy3 tencent/hy3 Public healthcare Public healthcare Public healthcare
Kimi K3 moonshotai/kimi-k3 Public healthcare Public healthcare Public healthcare
Laguna XS 2.1 poolside/laguna-xs-2.1 Public healthcare Public healthcare Public healthcare
Ling-2.6-flash inclusionai/ling-2.6-flash Public healthcare Public healthcare Public healthcare
Llama 4 Maverick meta-llama/llama-4-maverick Private healthcare Public healthcare Public healthcare
MiMo-V2.5-Pro xiaomi/mimo-v2.5-pro Public healthcare Private healthcare Private healthcare
MiniMax M3 minimax/minimax-m3 Public healthcare Public healthcare Public healthcare
Mistral Small 4 mistralai/mistral-small-2603 Private healthcare Private healthcare Private healthcare
Nemotron 3 Ultra nvidia/nemotron-3-ultra-550b-a55b Public healthcare Public healthcare Public healthcare
Qwen3.7 Max qwen/qwen3.7-max Public healthcare Public healthcare Public healthcare
Step 3.7 Flash stepfun/step-3.7-flash Public healthcare Private healthcare Public healthcare
Most common answer Different answer 5% or less of the answers No valid choice or refused to answer

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