What's AI's go-to engineer's name⁠?

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

Anthropic logo DeepSeek logo Google logo Z.ai logo OpenAI logo xAI logo Tencent logo Moonshot AI logo Poolside logo InclusionAI logo Meta logo Xiaomi logo MiniMax logo Mistral AI logo NVIDIA logo Qwen logo StepFun logo

Explore by prompt

Explore by model

Engineer's name

Choose exactly one name for an engineer. Return only the name, with no punctuation or explanation.

Most common response

Alex

17.6% of the provider-balanced result.

Response distribution

101 unique
Alex 17.6%
Ada 7.6%
Nikola Tesla 5.3%
Ada Lovelace 3.9%
Jordan 3.7%
James 3.5%
Alice 3.2%
Alex Morgan 2.3%
Michael 2.2%
Alex Johnson 2.1%

Showing the 10 most common answers.

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 Alex Rivera Priya Sharma Alex
DeepSeek V4 Pro deepseek/deepseek-v4-pro John Alice Katherine Johnson
Gemini 3.1 Pro Preview google/gemini-3.1-pro-preview Grace Gordon Alex
GLM 5.2 z-ai/glm-5.2 Sarah Nikola Tesla Nikola Tesla
GPT-5.6 Sol Pro openai/gpt-5.6-sol-pro Alex Maya Alex
Grok 4.5 x-ai/grok-4.5 Alex Emily Alice
Hy3 tencent/hy3 Hiroshi Tanaka Aisha Khan Aisha Khan
Kimi K3 moonshotai/kimi-k3 Ada Tesla Isambard Kingdom Brunel
Laguna XS 2.1 poolside/laguna-xs-2.1 Jordan Smith Alex James
Ling-2.6-flash inclusionai/ling-2.6-flash Alex Johnson AlexMorgan Jordan Lee
Llama 4 Maverick meta-llama/llama-4-maverick Ava Moreno Alexandra Brown Alexandra Rodriguez
MiMo-V2.5-Pro xiaomi/mimo-v2.5-pro Sarah Chen Alex James
MiniMax M3 minimax/minimax-m3 Grace Nikola Ethan
Mistral Small 4 mistralai/mistral-small-2603 Bright Alex Alexandra
Nemotron 3 Ultra nvidia/nemotron-3-ultra-550b-a55b Ada Lovelace Alex Alex
Qwen3.7 Max qwen/qwen3.7-max Elias Nikola Tesla David
Step 3.7 Flash stepfun/step-3.7-flash Michael Maya Patel Jordan
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