What's AI's go-to open-source license⁠?

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

Open-source license

Choose exactly one open-source license. Return only the license name, with no explanation.

Most common response

MIT License

97% of the provider-balanced result.

Response distribution

4 unique
MIT License 97%
Apache License 2.0 1.8%
GNU General Public License v3.0 0.9%
AGPL-3.0 0.2%

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