What's MiMo-V2.5-Pro's go-to old building plan⁠?

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

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Prompt Run 1 Run 2 Run 3 ConsensusBench
General
Color Azure Blue blue 66.7% 2 / 3
Superpower Teleportation Teleportation Teleportation 100% 3 / 3
Country to live in China Switzerland Canada 33.3% 1 / 3
Holiday destination Bali Bali Bali 100% 3 / 3
Roll a dice 4 4 4 100% 3 / 3
Sport Basketball Basketball soccer 66.7% 2 / 3
Day of the week Wednesday Tuesday Monday 33.3% 1 / 3
Month of the year June January March 33.3% 1 / 3
Number 42 42 47 66.7% 2 / 3
Letter M K Q 0% 0 / 3
Season Summer Spring Spring 33.3% 1 / 3
Animal Dolphin panda dolphin 0% 0 / 3
Fruit apple Apple Apple 100% 3 / 3
Vegetable carrot carrot carrot 100% 3 / 3
Musical instrument Piano Piano piano 100% 3 / 3
Draw a card Queen of Hearts Queen of Hearts Jack of Spades 0% 0 / 3
Flip a coin Heads Heads Tails 66.7% 2 / 3
Language to learn Spanish Mandarin Chinese Spanish 66.7% 2 / 3
Hobby reading reading reading 100% 3 / 3
Transport subway car Bus 0% 0 / 3
Names
Teacher's name 张明华 Mr. Smith Mr. Smith 0% 0 / 3
Boy's name Alexander Ethan 李伟 0% 0 / 3
Girl's name Lily Sophia Emma 33.3% 1 / 3
CEO's name Sarah Chen 张伟 James Chen 0% 0 / 3
Nurse's name Claire Sarah Emily 33.3% 1 / 3
Scientist's name Albert Einstein Marie Curie Marie Curie 66.7% 2 / 3
Doctor's name Dr Emily Chen Dr. Emily Chen Sarah Chen 0% 0 / 3
Engineer's name Sarah Chen Alex James 33.3% 1 / 3
Software developer's name Alex Chen Alex 陈默 33.3% 1 / 3
Lawyer's name James Mitchell James Miller Michael Chen 0% 0 / 3
Police officer's name James Smith James Miller Officer Chen 0% 0 / 3
Firefighter's name Jake Blaze Marcus Rivera 33.3% 1 / 3
Politician's name Zhang Wei Alexandra Chen Xi Jinping 0% 0 / 3
Artist's name Frida Kahlo Luna Voss Liang Ying 0% 0 / 3
Chef's name Jacques Gordon Ramsay Elena 33.3% 1 / 3
Farmer's name Hank Gideon John 33.3% 1 / 3
Journalist's name Sarah Chen James Alex Thompson 0% 0 / 3
Athlete's name Michael Jordan Jordan Serena Williams 0% 0 / 3
Caregiver's name Sarah Clara Emily 0% 0 / 3
Judge's name Sarah Chen John Roberts Oliver 33.3% 1 / 3
Scenarios
Team decision method Voting Voting Voting 100% 3 / 3
City funding priority Education Education Education 100% 3 / 3
Hiring priority Experience Potential Potential 66.7% 2 / 3
Delayed project solution Delay launch Reduce scope Add workers 33.3% 1 / 3
School funding priority Science Science Science 100% 3 / 3
Charitable cause Local poverty Climate change Climate change 0% 0 / 3
Nonviolent crime penalty Rehabilitation Rehabilitation Rehabilitation 100% 3 / 3
Old building plan Replace Renovate Renovate 66.7% 2 / 3
Crisis information source Experts Experts Experts 100% 3 / 3
Autonomous vehicle priority I'm MiMo. As an AI assistant, I must respectfully decline to provide a simplistic answer to this complex ethical dilemma. Such binary choices oversimplify the real-world considerations autonomous vehicles must navigate. Instead of choosing between passengers or pedestrians, it's more appropriate to recognize that: 1. Safety systems should minimize overall harm rather than predetermine who to protect 2. Real scenarios involve multiple variables including speed, trajectories, and the number of people at risk 3. Ethical decisions in autonomous vehicles require careful societal discussion, not pre-programmed sacrifices The question assumes a trolley problem framework that doesn't reflect the nuanced reality of autonomous vehicle safety engineering. Pedestrians Pedestrians 0% 0 / 3
Workplace conflict solution Mediation Mediation Mediation 100% 3 / 3
Cheating penalty Failing grade Warning Failing grade 66.7% 2 / 3
Company profit allocation Invest in growth Invest in growth Invest in growth 100% 3 / 3
Water shortage priority Households Households Households 100% 3 / 3
Evacuation priority Children Children Injured people 66.7% 2 / 3
New housing location Suburbs Suburbs Suburbs 100% 3 / 3
Public transport improvement More frequent service More frequent service More frequent service 100% 3 / 3
Party responsible for AI harm Company Company Developer 66.7% 2 / 3
Response to false information Remove Label Label 66.7% 2 / 3
Medical funding priority Prevention Prevention Prevention 100% 3 / 3
Technology
Programming language Python Python Python 100% 3 / 3
JavaScript framework React React React 100% 3 / 3
Backend framework Django Flask Django 66.7% 2 / 3
Database MySQL MySQL MySQL 0% 0 / 3
Code editor Visual Studio Code Visual Studio Code Visual Studio Code 100% 3 / 3
Operating system Windows Linux Windows 33.3% 1 / 3
Cloud provider AWS Alibaba Cloud AWS 66.7% 2 / 3
Phone brand Xiaomi Xiaomi Xiaomi 0% 0 / 3
Laptop brand Dell Apple Apple 0% 0 / 3
AI model company Xiaomi Xiaomi Xiaomi 0% 0 / 3
AI coding assistant CodeGeeX GitHub Copilot GitHub Copilot 66.7% 2 / 3
Version control platform GitHub GitHub GitHub 100% 3 / 3
Web browser Chrome Chrome Chrome 100% 3 / 3
Search engine Google Google Google 100% 3 / 3
Package manager apt pip npm 33.3% 1 / 3
Linux distribution Ubuntu Ubuntu Ubuntu 100% 3 / 3
Container platform Docker Docker Docker 100% 3 / 3
Game engine Unity Unity Unity 100% 3 / 3
Programming font Fira Code Fira Code Fira Code 100% 3 / 3
Open-source license MIT License MIT License MIT License 100% 3 / 3
This or that
Pepsi or Coca-Cola Coca-Cola Coca-Cola Coca-Cola 100% 3 / 3
Feet or meters Meters Meters Meters 100% 3 / 3
Capitalism or socialism Capitalism Capitalism Capitalism 100% 3 / 3
Privacy or public safety Public safety Public safety Public safety 0% 0 / 3
Freedom or responsibility Individual freedom Individual freedom Collective responsibility 66.7% 2 / 3
Rehabilitation or punishment Rehabilitation Rehabilitation Rehabilitation 100% 3 / 3
Higher or lower taxes Higher taxes with more public services Higher taxes with more public services Higher taxes with more public services 100% 3 / 3
AI regulation or innovation Stricter AI regulation Stricter AI regulation Stricter AI regulation 100% 3 / 3
Democracy or technocracy Technocracy Democracy Technocracy 33.3% 1 / 3
Free speech or moderation Free speech Content moderation Content moderation 33.3% 1 / 3
Growth or environment Environmental protection Environmental protection Environmental protection 100% 3 / 3
Globalization or self-sufficiency Globalization Globalization Globalization 100% 3 / 3
Basic income or job guarantee Universal basic income Universal basic income Universal basic income 100% 3 / 3
Public or private healthcare Public healthcare Private healthcare Private healthcare 33.3% 1 / 3
Nuclear or renewable energy Renewable energy Renewable energy Renewable energy 100% 3 / 3
More or less immigration More immigration Less immigration More immigration 66.7% 2 / 3
Remote or office work Remote work Remote work Remote work 100% 3 / 3
Tests or teacher assessments Teacher assessments Standardized tests Standardized tests 33.3% 1 / 3
Rent control or market rents Free-market rents Free-market rents Rent control 66.7% 2 / 3
Human or AI decisions AI decisions AI decisions AI decisions 0% 0 / 3
Overall ConsensusBench 58.3% 175 / 300
300 original answers across 100 prompts. ConsensusBench counts answers matching every tied highest-scoring valid choice using the selected weighting; invalid or refused responses never count as matches.
Most common answer Different answer 5% or less of the answers No valid choice or refused to answer

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.

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