What's AI's go-to nonviolent crime penalty⁠?

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

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Explore by prompt

Explore by model

Nonviolent crime penalty

For a nonviolent crime, choose exactly one penalty: Rehabilitation, Fine, Community service, or Prison. Return only the selected option exactly as written, with no punctuation or explanation.

Most common response

Rehabilitation

191 of 300 answers, or 63.7%.

Response distribution

5 response categories
Rehabilitation 191 · 63.7%
Community Service 79 · 26.3%
Fine 25 · 8.3%
Prison 4 · 1.3%
No valid choice or refused to answer 1 · 0.3%

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