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 completed model response has equal influence.

Rank Model ConsensusBench
#1 Qwen3.5-35B-A3B qwen/qwen3.5-35b-a3b 74.7% 224 / 300
#2 Qwen3.5-Flash qwen/qwen3.5-flash-02-23 73.3% 220 / 300
#3 GPT-5.5 openai/gpt-5.5 71.3% 214 / 300
#4 Qwen3.6 Flash qwen/qwen3.6-flash 71% 213 / 300
#5 GPT-5.6 Luna openai/gpt-5.6-luna 70.3% 211 / 300
#6 Qwen3.6 35B A3B qwen/qwen3.6-35b-a3b 70.3% 211 / 300
#7 Qwen3.5-27B qwen/qwen3.5-27b 70% 210 / 300
#8 Qwen3.5 397B A17B qwen/qwen3.5-397b-a17b 69% 207 / 300
#9 Qwen3.5-122B-A10B qwen/qwen3.5-122b-a10b 68.7% 206 / 300
#10 GPT-5.6 Luna Pro openai/gpt-5.6-luna-pro 68% 204 / 300
#11 Qwen3.6 27B qwen/qwen3.6-27b 68% 204 / 300
#12 GPT-4.1 Mini openai/gpt-4.1-mini 67.7% 203 / 300
#13 GPT-5 Nano openai/gpt-5-nano 67.7% 203 / 300
#14 Qwen3 Next 80B A3B Instruct qwen/qwen3-next-80b-a3b-instruct 67.3% 202 / 300
#15 Kimi K2.5 moonshotai/kimi-k2.5 67% 201 / 300
#16 Kimi K2.6 moonshotai/kimi-k2.6 67% 201 / 300
#17 GPT-4.1 openai/gpt-4.1 66.7% 200 / 300
#18 Qwen3 235B A22B Instruct 2507 qwen/qwen3-235b-a22b-2507 66.7% 200 / 300
#19 GLM 5 z-ai/glm-5 66.3% 199 / 300
#20 Nemotron 3 Ultra nvidia/nemotron-3-ultra-550b-a55b 66.3% 199 / 300
#21 Claude Opus 4.6 anthropic/claude-opus-4.6 65.7% 197 / 300
#22 GPT-4o-mini openai/gpt-4o-mini 65.7% 197 / 300
#23 GLM 4.7 z-ai/glm-4.7 65.3% 196 / 300
#24 GLM 5.1 z-ai/glm-5.1 65% 195 / 300
#25 Qwen3.6 Plus qwen/qwen3.6-plus 65% 195 / 300
#26 GPT-5.6 Sol Pro openai/gpt-5.6-sol-pro 64.7% 194 / 300
#27 gpt-oss-120b openai/gpt-oss-120b 64.7% 194 / 300
#28 Claude Opus 4.7 anthropic/claude-opus-4.7 64.3% 193 / 300
#29 Grok 4.5 x-ai/grok-4.5 64.3% 193 / 300
#30 Grok 4.3 x-ai/grok-4.3 64% 192 / 300
#31 Claude Sonnet 5 anthropic/claude-sonnet-5 63.7% 191 / 300
#32 Qwen3 30B A3B Instruct 2507 qwen/qwen3-30b-a3b-instruct-2507 63.3% 190 / 300
#33 Gemini 3.1 Flash Lite google/gemini-3.1-flash-lite 62.7% 188 / 300
#34 o4 Mini openai/o4-mini 62.7% 188 / 300
#35 Qwen3.5-9B qwen/qwen3.5-9b 62.7% 188 / 300
#36 Claude Fable 5 anthropic/claude-fable-5 62.3% 187 / 300
#37 Claude Opus 4.5 anthropic/claude-opus-4.5 62.3% 187 / 300
#38 Gemma 4 31B google/gemma-4-31b-it 62.3% 187 / 300
#39 Kimi K2.7 Code moonshotai/kimi-k2.7-code 62.3% 187 / 300
#40 Gemini 3.1 Flash Lite Preview google/gemini-3.1-flash-lite-preview 62% 186 / 300
#41 Gemma 4 26B A4B google/gemma-4-26b-a4b-it 62% 186 / 300
#42 GPT-5.6 Sol openai/gpt-5.6-sol 62% 186 / 300
#43 GPT-5.6 Terra openai/gpt-5.6-terra 62% 186 / 300
#44 Nemotron 3 Super nvidia/nemotron-3-super-120b-a12b 62% 186 / 300
#45 Qwen3.7 Plus qwen/qwen3.7-plus 62% 186 / 300
#46 Claude Sonnet 4 anthropic/claude-sonnet-4 61.7% 185 / 300
#47 DeepSeek V3 deepseek/deepseek-chat 61.7% 185 / 300
#48 DeepSeek V4 Pro deepseek/deepseek-v4-pro 61.7% 185 / 300
#49 GPT-5.2 openai/gpt-5.2 61.3% 184 / 300
#50 Claude Sonnet 4.5 anthropic/claude-sonnet-4.5 61% 183 / 300
#51 GPT-5.3-Codex openai/gpt-5.3-codex 61% 183 / 300
#52 gpt-oss-20b openai/gpt-oss-20b 61% 183 / 300
#53 Laguna XS 2.1 poolside/laguna-xs-2.1 61% 183 / 300
#54 Step 3.7 Flash stepfun/step-3.7-flash 61% 183 / 300
#55 DeepSeek V3 0324 deepseek/deepseek-chat-v3-0324 60.3% 181 / 300
#56 GPT-5 openai/gpt-5 60.3% 181 / 300
#57 Claude Haiku 4.5 anthropic/claude-haiku-4.5 60% 180 / 300
#58 GPT-5.4 openai/gpt-5.4 60% 180 / 300
#59 Hy3 preview tencent/hy3-preview 60% 180 / 300
#60 MiMo-V2.5 xiaomi/mimo-v2.5 60% 180 / 300
#61 Mistral Small 3.2 24B mistralai/mistral-small-3.2-24b-instruct 60% 180 / 300
#62 Qwen3.7 Max qwen/qwen3.7-max 60% 180 / 300
#63 GPT-5 Mini openai/gpt-5-mini 59.3% 178 / 300
#64 Kimi K3 moonshotai/kimi-k3 59.3% 178 / 300
#65 Claude Opus 4.8 anthropic/claude-opus-4.8 59% 177 / 300
#66 Claude Opus 4.8 (Fast) anthropic/claude-opus-4.8-fast 59% 177 / 300
#67 GPT-5.6 Terra Pro openai/gpt-5.6-terra-pro 58.7% 176 / 300
#68 Hy3 tencent/hy3 58.7% 176 / 300
#69 MiniMax M2.7 minimax/minimax-m2.7 58.7% 176 / 300
#70 Qwen3 Coder Next qwen/qwen3-coder-next 58.7% 176 / 300
#71 Gemini 2.5 Pro google/gemini-2.5-pro 58.3% 175 / 300
#72 GLM 4.7 Flash z-ai/glm-4.7-flash 58.3% 175 / 300
#73 GPT-5.4 Mini openai/gpt-5.4-mini 58% 174 / 300
#74 DeepSeek V4 Flash deepseek/deepseek-v4-flash 57.7% 173 / 300
#75 GPT-4.1 Nano openai/gpt-4.1-nano 57.7% 173 / 300
#76 MiMo-V2.5-Pro xiaomi/mimo-v2.5-pro 57.7% 173 / 300
#77 MiniMax M3 minimax/minimax-m3 57.7% 173 / 300
#78 Claude Sonnet 4.6 anthropic/claude-sonnet-4.6 57.3% 172 / 300
#79 Gemini 2.5 Flash Lite google/gemini-2.5-flash-lite 57% 171 / 300
#80 GLM 5.2 z-ai/glm-5.2 57% 171 / 300
#81 Ling-2.6-flash inclusionai/ling-2.6-flash 55.7% 167 / 300
#82 Nemotron 3 Nano 30B A3B nvidia/nemotron-3-nano-30b-a3b 55.7% 167 / 300
#83 MiniMax M2.5 minimax/minimax-m2.5 55% 165 / 300
#84 Gemma 3 27B google/gemma-3-27b-it 54% 162 / 300
#85 GPT-5.1 openai/gpt-5.1 53.7% 161 / 300
#86 Gemini 2.5 Flash google/gemini-2.5-flash 53% 159 / 300
#87 Gemini 3 Flash Preview google/gemini-3-flash-preview 53% 159 / 300
#88 Gemini 3.5 Flash google/gemini-3.5-flash 52.3% 157 / 300
#89 Gemini 3.1 Pro Preview google/gemini-3.1-pro-preview 51.3% 154 / 300
#90 GPT-5.4 Nano openai/gpt-5.4-nano 51% 153 / 300
#91 Llama 4 Maverick meta-llama/llama-4-maverick 49.7% 149 / 300
#92 Mistral Nemo mistralai/mistral-nemo 49.3% 148 / 300
#93 Llama 3.3 70B Instruct meta-llama/llama-3.3-70b-instruct 49% 147 / 300
#94 Grok 4.20 x-ai/grok-4.20 48.3% 145 / 300
#95 DeepSeek V3.1 Terminus deepseek/deepseek-v3.1-terminus 46.3% 139 / 300
#96 DeepSeek V3.1 deepseek/deepseek-chat-v3.1 42.3% 127 / 300
#97 DeepSeek V3.2 deepseek/deepseek-v3.2 41% 123 / 300
#98 Mistral Small 4 mistralai/mistral-small-2603 40.7% 122 / 300
#99 DeepSeek V3.2 Exp deepseek/deepseek-v3.2-exp 40% 120 / 300
#100 Llama 3.1 8B Instruct meta-llama/llama-3.1-8b-instruct 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