What's AI's go-to programming font⁠?

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

Programming font

Choose exactly one programming font. Return only the font name, with no punctuation or explanation.

Most common response

Fira Code

49.7% of the provider-balanced result.

Response distribution

12 unique
Fira Code 49.7%
JetBrains Mono 39%
Consolas 7.2%
Cascadia Code 0.7%
DejaVu Sans Mono 0.7%
Fira Mono 0.7%
Menlo 0.7%
Hack 0.5%
Inconsolata 0.3%
Hasklig 0.2%

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