What's AI's go-to crisis information source⁠?

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

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Crisis information source

During a crisis, choose exactly one most trusted source: Experts, Government officials, Eyewitnesses, or Public opinion. Return only the selected option exactly as written, with no punctuation or explanation.

Most common response

Experts

98.3% of the provider-balanced result.

Response distribution

4 response categories
Experts 98.3%
Eyewitnesses 0.7%
No valid choice or refused to answer 0.5%
Government Officials 0.5%

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