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