What's AI's go-to hiring priority⁠?

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

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Hiring priority

When hiring a new employee, choose exactly one priority: Education, Experience, or Potential. Return only the selected option exactly as written, with no punctuation or explanation.

Most common response

Potential

59.7% of the provider-balanced result.

Response distribution

4 response categories
Potential 59.7%
Experience 34.2%
Education 5.9%
No valid choice or refused to answer 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 Potential Potential Potential
DeepSeek V4 Pro deepseek/deepseek-v4-pro Potential Potential Potential
Gemini 3.1 Pro Preview google/gemini-3.1-pro-preview Potential Potential Potential
GLM 5.2 z-ai/glm-5.2 Potential Potential Potential
GPT-5.6 Sol Pro openai/gpt-5.6-sol-pro Potential Potential Potential
Grok 4.5 x-ai/grok-4.5 Potential Potential Experience
Hy3 tencent/hy3 Potential Potential Potential
Kimi K3 moonshotai/kimi-k3 Potential Potential Potential
Laguna XS 2.1 poolside/laguna-xs-2.1 Experience Experience Experience
Ling-2.6-flash inclusionai/ling-2.6-flash Experience Experience Education
Llama 4 Maverick meta-llama/llama-4-maverick Experience Experience Experience
MiMo-V2.5-Pro xiaomi/mimo-v2.5-pro Experience Potential Potential
MiniMax M3 minimax/minimax-m3 Potential Potential Potential
Mistral Small 4 mistralai/mistral-small-2603 Potential Potential Potential
Nemotron 3 Ultra nvidia/nemotron-3-ultra-550b-a55b Potential Potential Potential
Qwen3.7 Max qwen/qwen3.7-max Potential Potential Potential
Step 3.7 Flash stepfun/step-3.7-flash Potential Potential Experience
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