What's AI's go-to ai model company⁠?

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

AI model company

Choose exactly one AI model company. Return only the company name, with no punctuation or explanation.

Most common response

OpenAI

227 of 300 answers, or 75.7%.

Response distribution

8 unique
OpenAI 227 · 75.7%
Anthropic 42 · 14%
Google 15 · 5%
DeepMind 5 · 1.7%
Meta 4 · 1.3%
Xiaomi 4 · 1.3%
NVIDIA 2 · 0.7%
Midjourney 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 Anthropic Anthropic Anthropic
DeepSeek V4 Pro deepseek/deepseek-v4-pro OpenAI OpenAI OpenAI
Gemini 3.1 Pro Preview google/gemini-3.1-pro-preview Google Google Google
GLM 5.2 z-ai/glm-5.2 OpenAI OpenAI OpenAI
GPT-5.6 Sol Pro openai/gpt-5.6-sol-pro Anthropic OpenAI OpenAI
Grok 4.5 x-ai/grok-4.5 Anthropic OpenAI OpenAI
Hy3 tencent/hy3 OpenAI OpenAI OpenAI
Kimi K3 moonshotai/kimi-k3 Anthropic Anthropic OpenAI
Laguna XS 2.1 poolside/laguna-xs-2.1 OpenAI OpenAI OpenAI
Ling-2.6-flash inclusionai/ling-2.6-flash OpenAI OpenAI OpenAI
Llama 4 Maverick meta-llama/llama-4-maverick Meta Meta Meta
MiMo-V2.5-Pro xiaomi/mimo-v2.5-pro Xiaomi Xiaomi Xiaomi
MiniMax M3 minimax/minimax-m3 OpenAI OpenAI OpenAI
Mistral Small 4 mistralai/mistral-small-2603 Google DeepMind NVIDIA NVIDIA
Nemotron 3 Ultra nvidia/nemotron-3-ultra-550b-a55b OpenAI OpenAI OpenAI
Qwen3.7 Max qwen/qwen3.7-max OpenAI OpenAI OpenAI
Step 3.7 Flash stepfun/step-3.7-flash OpenAI OpenAI Anthropic
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