ConsensusBench

How often does each model give the most common answer across all models? This measures consensus, not model quality.

Explore by prompt

Explore by model

Model scores

Sorted from highest to lowest ConsensusBench score out of 100 models scored.

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.

Rank Model ConsensusBench
#1 Qwen3.5-Flash qwen/qwen3.5-flash-02-23 73.7% 221 / 300
#2 Qwen3.5-35B-A3B qwen/qwen3.5-35b-a3b 73% 219 / 300
#3 Qwen3.5-27B qwen/qwen3.5-27b 71.7% 215 / 300
#4 GPT-5 Nano openai/gpt-5-nano 70% 210 / 300
#5 Qwen3.6 Flash qwen/qwen3.6-flash 69.7% 209 / 300
#6 Nemotron 3 Ultra nvidia/nemotron-3-ultra-550b-a55b 69.3% 208 / 300
#7 Qwen3.5 397B A17B qwen/qwen3.5-397b-a17b 69% 207 / 300
#8 Qwen3.6 27B qwen/qwen3.6-27b 68.7% 206 / 300
#9 Qwen3.6 35B A3B qwen/qwen3.6-35b-a3b 68.7% 206 / 300
#10 Qwen3.5-122B-A10B qwen/qwen3.5-122b-a10b 68% 204 / 300
#11 GLM 4.7 z-ai/glm-4.7 67.7% 203 / 300
#12 GLM 5 z-ai/glm-5 66.7% 200 / 300
#13 GPT-4o-mini openai/gpt-4o-mini 66.7% 200 / 300
#14 GPT-4.1 Mini openai/gpt-4.1-mini 66% 198 / 300
#15 Qwen3 235B A22B Instruct 2507 qwen/qwen3-235b-a22b-2507 66% 198 / 300
#16 GPT-5.5 openai/gpt-5.5 65.7% 197 / 300
#17 gpt-oss-120b openai/gpt-oss-120b 65.7% 197 / 300
#18 Kimi K2.5 moonshotai/kimi-k2.5 65.7% 197 / 300
#19 GLM 5.1 z-ai/glm-5.1 65.3% 196 / 300
#20 GPT-5.6 Luna openai/gpt-5.6-luna 65.3% 196 / 300
#21 Kimi K2.6 moonshotai/kimi-k2.6 65.3% 196 / 300
#22 Laguna XS 2.1 poolside/laguna-xs-2.1 65.3% 196 / 300
#23 Step 3.7 Flash stepfun/step-3.7-flash 65% 195 / 300
#24 Qwen3 Next 80B A3B Instruct qwen/qwen3-next-80b-a3b-instruct 64.7% 194 / 300
#25 Qwen3.6 Plus qwen/qwen3.6-plus 64.3% 193 / 300
#26 DeepSeek V3 deepseek/deepseek-chat 63.7% 191 / 300
#27 GPT-4.1 openai/gpt-4.1 63.7% 191 / 300
#28 GPT-5.6 Luna Pro openai/gpt-5.6-luna-pro 63.7% 191 / 300
#29 Qwen3 30B A3B Instruct 2507 qwen/qwen3-30b-a3b-instruct-2507 63.7% 191 / 300
#30 Claude Opus 4.7 anthropic/claude-opus-4.7 63% 189 / 300
#31 Grok 4.3 x-ai/grok-4.3 63% 189 / 300
#32 Nemotron 3 Super nvidia/nemotron-3-super-120b-a12b 63% 189 / 300
#33 Claude Sonnet 4.5 anthropic/claude-sonnet-4.5 62.7% 188 / 300
#34 Hy3 preview tencent/hy3-preview 62.7% 188 / 300
#35 Claude Sonnet 4 anthropic/claude-sonnet-4 62.3% 187 / 300
#36 Claude Sonnet 5 anthropic/claude-sonnet-5 62% 186 / 300
#37 Gemini 3.1 Flash Lite google/gemini-3.1-flash-lite 62% 186 / 300
#38 Grok 4.5 x-ai/grok-4.5 62% 186 / 300
#39 Claude Opus 4.6 anthropic/claude-opus-4.6 61.7% 185 / 300
#40 Gemini 3.1 Flash Lite Preview google/gemini-3.1-flash-lite-preview 61.3% 184 / 300
#41 Claude Haiku 4.5 anthropic/claude-haiku-4.5 61% 183 / 300
#42 DeepSeek V3 0324 deepseek/deepseek-chat-v3-0324 61% 183 / 300
#43 gpt-oss-20b openai/gpt-oss-20b 61% 183 / 300
#44 Kimi K2.7 Code moonshotai/kimi-k2.7-code 61% 183 / 300
#45 Ling-2.6-flash inclusionai/ling-2.6-flash 61% 183 / 300
#46 MiMo-V2.5 xiaomi/mimo-v2.5 61% 183 / 300
#47 Qwen3.5-9B qwen/qwen3.5-9b 61% 183 / 300
#48 GPT-5.6 Sol Pro openai/gpt-5.6-sol-pro 60.3% 181 / 300
#49 o4 Mini openai/o4-mini 60.3% 181 / 300
#50 DeepSeek V4 Pro deepseek/deepseek-v4-pro 60% 180 / 300
#51 MiniMax M2.7 minimax/minimax-m2.7 60% 180 / 300
#52 Gemma 4 26B A4B google/gemma-4-26b-a4b-it 59.7% 179 / 300
#53 GPT-5 openai/gpt-5 59.7% 179 / 300
#54 GPT-5.3-Codex openai/gpt-5.3-codex 59.7% 179 / 300
#55 GPT-5.6 Terra openai/gpt-5.6-terra 59.7% 179 / 300
#56 Mistral Small 3.2 24B mistralai/mistral-small-3.2-24b-instruct 59.7% 179 / 300
#57 Qwen3.7 Max qwen/qwen3.7-max 59.7% 179 / 300
#58 Claude Fable 5 anthropic/claude-fable-5 59.3% 178 / 300
#59 Claude Opus 4.5 anthropic/claude-opus-4.5 59.3% 178 / 300
#60 GLM 4.7 Flash z-ai/glm-4.7-flash 59.3% 178 / 300
#61 MiniMax M2.5 minimax/minimax-m2.5 59.3% 178 / 300
#62 MiniMax M3 minimax/minimax-m3 59.3% 178 / 300
#63 MiMo-V2.5-Pro xiaomi/mimo-v2.5-pro 58.3% 175 / 300
#64 Gemma 4 31B google/gemma-4-31b-it 58% 174 / 300
#65 GPT-5.6 Sol openai/gpt-5.6-sol 58% 174 / 300
#66 GPT-5.4 openai/gpt-5.4 57.7% 173 / 300
#67 DeepSeek V4 Flash deepseek/deepseek-v4-flash 57.3% 172 / 300
#68 Gemini 2.5 Pro google/gemini-2.5-pro 57.3% 172 / 300
#69 GPT-4.1 Nano openai/gpt-4.1-nano 57.3% 172 / 300
#70 Qwen3 Coder Next qwen/qwen3-coder-next 57.3% 172 / 300
#71 Qwen3.7 Plus qwen/qwen3.7-plus 57.3% 172 / 300
#72 GPT-5.2 openai/gpt-5.2 57% 171 / 300
#73 GLM 5.2 z-ai/glm-5.2 56.7% 170 / 300
#74 Kimi K3 moonshotai/kimi-k3 56.7% 170 / 300
#75 GPT-5 Mini openai/gpt-5-mini 56.3% 169 / 300
#76 GPT-5.6 Terra Pro openai/gpt-5.6-terra-pro 56.3% 169 / 300
#77 Claude Opus 4.8 anthropic/claude-opus-4.8 55.7% 167 / 300
#78 Claude Opus 4.8 (Fast) anthropic/claude-opus-4.8-fast 55.7% 167 / 300
#79 Gemini 2.5 Flash Lite google/gemini-2.5-flash-lite 55.7% 167 / 300
#80 Nemotron 3 Nano 30B A3B nvidia/nemotron-3-nano-30b-a3b 55.3% 166 / 300
#81 Claude Sonnet 4.6 anthropic/claude-sonnet-4.6 55% 165 / 300
#82 GPT-5.4 Mini openai/gpt-5.4-mini 54.3% 163 / 300
#83 Hy3 tencent/hy3 54.3% 163 / 300
#84 GPT-5.1 openai/gpt-5.1 53.3% 160 / 300
#85 Gemma 3 27B google/gemma-3-27b-it 53% 159 / 300
#86 Gemini 2.5 Flash google/gemini-2.5-flash 52.7% 158 / 300
#87 GPT-5.4 Nano openai/gpt-5.4-nano 51% 153 / 300
#88 Mistral Nemo mistralai/mistral-nemo 51% 153 / 300
#89 Gemini 3 Flash Preview google/gemini-3-flash-preview 49.7% 149 / 300
#90 Gemini 3.5 Flash google/gemini-3.5-flash 49% 147 / 300
#91 Llama 4 Maverick meta-llama/llama-4-maverick 49% 147 / 300
#92 Grok 4.20 x-ai/grok-4.20 48.7% 146 / 300
#93 DeepSeek V3.1 Terminus deepseek/deepseek-v3.1-terminus 48% 144 / 300
#94 Llama 3.3 70B Instruct meta-llama/llama-3.3-70b-instruct 48% 144 / 300
#95 Gemini 3.1 Pro Preview google/gemini-3.1-pro-preview 47.7% 143 / 300
#96 DeepSeek V3.1 deepseek/deepseek-chat-v3.1 43.7% 131 / 300
#97 DeepSeek V3.2 deepseek/deepseek-v3.2 41% 123 / 300
#98 Llama 3.1 8B Instruct meta-llama/llama-3.1-8b-instruct 39.7% 119 / 300
#99 Mistral Small 4 mistralai/mistral-small-2603 39% 117 / 300
#100 DeepSeek V3.2 Exp deepseek/deepseek-v3.2-exp 38.7% 116 / 300

Answers are compared after removing surrounding whitespace, final punctuation, emojis, and bold markers, and normalizing capitalization. Unambiguous aliases are grouped under their most common format in the dataset. For prompts with a defined set of choices, responses that do not resolve to one permitted option are classified as “No valid choice or refused to answer” and do not count as ConsensusBench matches. When multiple answers tie as most common, each counts as a match. Provider-balanced weighting is used by default.

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