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