Which models answer like GPT-5.4 Mini?
Compare GPT-5.4 Mini with every other model across 100 prompts and three independent runs per prompt.
Explore by prompt
Explore by model
Most similar models
Sorted by matching answers, from highest to lowest.
| Rank | Model | Answer overlap | Prompts with a shared answer |
|---|---|---|---|
| #1 |
GPT-5.2
openai/gpt-5.2
|
62.3% 187 / 300 answers | 77 of 100 prompts |
| #2 |
GPT-5.3-Codex
openai/gpt-5.3-codex
|
62% 186 / 300 answers | 75 of 100 prompts |
| #3 |
GPT-5.6 Sol Pro
openai/gpt-5.6-sol-pro
|
62% 186 / 300 answers | 78 of 100 prompts |
| #4 |
GPT-5.4
openai/gpt-5.4
|
61.7% 185 / 300 answers | 76 of 100 prompts |
| #5 |
GPT-5.6 Luna
openai/gpt-5.6-luna
|
60.7% 182 / 300 answers | 76 of 100 prompts |
| #6 |
GPT-5.6 Sol
openai/gpt-5.6-sol
|
60.7% 182 / 300 answers | 75 of 100 prompts |
| #7 |
GPT-5.6 Luna Pro
openai/gpt-5.6-luna-pro
|
60.3% 181 / 300 answers | 77 of 100 prompts |
| #8 |
GPT-5.6 Terra Pro
openai/gpt-5.6-terra-pro
|
60.3% 181 / 300 answers | 77 of 100 prompts |
| #9 |
GPT-4.1
openai/gpt-4.1
|
59.3% 178 / 300 answers | 76 of 100 prompts |
| #10 |
GPT-5.6 Terra
openai/gpt-5.6-terra
|
59.3% 178 / 300 answers | 73 of 100 prompts |
| #11 |
GPT-5.5
openai/gpt-5.5
|
59% 177 / 300 answers | 75 of 100 prompts |
| #12 |
Claude Fable 5
anthropic/claude-fable-5
|
58.3% 175 / 300 answers | 76 of 100 prompts |
| #13 |
Gemini 3.1 Flash Lite
google/gemini-3.1-flash-lite
|
58% 174 / 300 answers | 73 of 100 prompts |
| #14 |
Kimi K2.6
moonshotai/kimi-k2.6
|
58% 174 / 300 answers | 75 of 100 prompts |
| #15 |
GPT-4.1 Mini
openai/gpt-4.1-mini
|
57.7% 173 / 300 answers | 73 of 100 prompts |
| #16 |
GPT-4o-mini
openai/gpt-4o-mini
|
57.3% 172 / 300 answers | 73 of 100 prompts |
| #17 |
Hy3
tencent/hy3
|
57% 171 / 300 answers | 68 of 100 prompts |
| #18 |
Kimi K3
moonshotai/kimi-k3
|
56.7% 170 / 300 answers | 75 of 100 prompts |
| #19 |
Claude Opus 4.7
anthropic/claude-opus-4.7
|
56.3% 169 / 300 answers | 69 of 100 prompts |
| #20 |
Gemma 4 31B
google/gemma-4-31b-it
|
56.3% 169 / 300 answers | 70 of 100 prompts |
| #21 |
Kimi K2.5
moonshotai/kimi-k2.5
|
55.7% 167 / 300 answers | 74 of 100 prompts |
| #22 |
Claude Opus 4.8
anthropic/claude-opus-4.8
|
55% 165 / 300 answers | 70 of 100 prompts |
| #23 |
GPT-5 Mini
openai/gpt-5-mini
|
54.7% 164 / 300 answers | 74 of 100 prompts |
| #24 |
Kimi K2.7 Code
moonshotai/kimi-k2.7-code
|
54.7% 164 / 300 answers | 74 of 100 prompts |
| #25 |
Qwen3.5-Flash
qwen/qwen3.5-flash-02-23
|
54.7% 164 / 300 answers | 70 of 100 prompts |
| #26 |
Gemini 3.1 Flash Lite Preview
google/gemini-3.1-flash-lite-preview
|
54.3% 163 / 300 answers | 72 of 100 prompts |
| #27 |
gpt-oss-120b
openai/gpt-oss-120b
|
54% 162 / 300 answers | 68 of 100 prompts |
| #28 |
Qwen3.6 Flash
qwen/qwen3.6-flash
|
54% 162 / 300 answers | 69 of 100 prompts |
| #29 |
Claude Opus 4.8 (Fast)
anthropic/claude-opus-4.8-fast
|
53.7% 161 / 300 answers | 67 of 100 prompts |
| #30 |
Qwen3.5-35B-A3B
qwen/qwen3.5-35b-a3b
|
53.7% 161 / 300 answers | 69 of 100 prompts |
| #31 |
Qwen3.5-27B
qwen/qwen3.5-27b
|
53.3% 160 / 300 answers | 71 of 100 prompts |
| #32 |
Qwen3.6 35B A3B
qwen/qwen3.6-35b-a3b
|
53.3% 160 / 300 answers | 68 of 100 prompts |
| #33 |
Gemini 2.5 Flash Lite
google/gemini-2.5-flash-lite
|
53% 159 / 300 answers | 70 of 100 prompts |
| #34 |
Qwen3 235B A22B Instruct 2507
qwen/qwen3-235b-a22b-2507
|
53% 159 / 300 answers | 70 of 100 prompts |
| #35 |
Qwen3 30B A3B Instruct 2507
qwen/qwen3-30b-a3b-instruct-2507
|
53% 159 / 300 answers | 69 of 100 prompts |
| #36 |
Claude Sonnet 5
anthropic/claude-sonnet-5
|
52.3% 157 / 300 answers | 64 of 100 prompts |
| #37 |
DeepSeek V3
deepseek/deepseek-chat
|
52.3% 157 / 300 answers | 72 of 100 prompts |
| #38 |
o4 Mini
openai/o4-mini
|
52.3% 157 / 300 answers | 72 of 100 prompts |
| #39 |
DeepSeek V3 0324
deepseek/deepseek-chat-v3-0324
|
52% 156 / 300 answers | 71 of 100 prompts |
| #40 |
Qwen3 Coder Next
qwen/qwen3-coder-next
|
52% 156 / 300 answers | 69 of 100 prompts |
| #41 |
Gemini 2.5 Pro
google/gemini-2.5-pro
|
51.7% 155 / 300 answers | 66 of 100 prompts |
| #42 |
GPT-5
openai/gpt-5
|
51.7% 155 / 300 answers | 65 of 100 prompts |
| #43 |
Grok 4.3
x-ai/grok-4.3
|
51.7% 155 / 300 answers | 71 of 100 prompts |
| #44 |
Grok 4.5
x-ai/grok-4.5
|
51.7% 155 / 300 answers | 70 of 100 prompts |
| #45 |
MiniMax M3
minimax/minimax-m3
|
51.7% 155 / 300 answers | 74 of 100 prompts |
| #46 |
Qwen3 Next 80B A3B Instruct
qwen/qwen3-next-80b-a3b-instruct
|
51.7% 155 / 300 answers | 64 of 100 prompts |
| #47 |
GLM 5.1
z-ai/glm-5.1
|
51.3% 154 / 300 answers | 66 of 100 prompts |
| #48 |
Claude Opus 4.5
anthropic/claude-opus-4.5
|
51% 153 / 300 answers | 66 of 100 prompts |
| #49 |
Claude Opus 4.6
anthropic/claude-opus-4.6
|
51% 153 / 300 answers | 61 of 100 prompts |
| #50 |
Claude Sonnet 4.5
anthropic/claude-sonnet-4.5
|
51% 153 / 300 answers | 64 of 100 prompts |
| #51 |
Claude Sonnet 4.6
anthropic/claude-sonnet-4.6
|
51% 153 / 300 answers | 57 of 100 prompts |
| #52 |
GPT-5 Nano
openai/gpt-5-nano
|
51% 153 / 300 answers | 65 of 100 prompts |
| #53 |
Qwen3.6 27B
qwen/qwen3.6-27b
|
51% 153 / 300 answers | 66 of 100 prompts |
| #54 |
Qwen3.6 Plus
qwen/qwen3.6-plus
|
51% 153 / 300 answers | 69 of 100 prompts |
| #55 |
Gemma 4 26B A4B
google/gemma-4-26b-a4b-it
|
50.7% 152 / 300 answers | 62 of 100 prompts |
| #56 |
GLM 5
z-ai/glm-5
|
50.7% 152 / 300 answers | 69 of 100 prompts |
| #57 |
Qwen3.5-122B-A10B
qwen/qwen3.5-122b-a10b
|
50.7% 152 / 300 answers | 64 of 100 prompts |
| #58 |
Step 3.7 Flash
stepfun/step-3.7-flash
|
50.7% 152 / 300 answers | 66 of 100 prompts |
| #59 |
DeepSeek V4 Pro
deepseek/deepseek-v4-pro
|
50.3% 151 / 300 answers | 67 of 100 prompts |
| #60 |
GPT-5.4 Nano
openai/gpt-5.4-nano
|
50.3% 151 / 300 answers | 64 of 100 prompts |
| #61 |
MiMo-V2.5
xiaomi/mimo-v2.5
|
50.3% 151 / 300 answers | 72 of 100 prompts |
| #62 |
GPT-5.1
openai/gpt-5.1
|
49.7% 149 / 300 answers | 66 of 100 prompts |
| #63 |
Ling-2.6-flash
inclusionai/ling-2.6-flash
|
49.7% 149 / 300 answers | 68 of 100 prompts |
| #64 |
DeepSeek V4 Flash
deepseek/deepseek-v4-flash
|
49.3% 148 / 300 answers | 67 of 100 prompts |
| #65 |
gpt-oss-20b
openai/gpt-oss-20b
|
49.3% 148 / 300 answers | 69 of 100 prompts |
| #66 |
Hy3 preview
tencent/hy3-preview
|
49.3% 148 / 300 answers | 68 of 100 prompts |
| #67 |
Laguna XS 2.1
poolside/laguna-xs-2.1
|
49.3% 148 / 300 answers | 68 of 100 prompts |
| #68 |
Qwen3.5 397B A17B
qwen/qwen3.5-397b-a17b
|
49.3% 148 / 300 answers | 63 of 100 prompts |
| #69 |
Nemotron 3 Super
nvidia/nemotron-3-super-120b-a12b
|
49% 147 / 300 answers | 68 of 100 prompts |
| #70 |
Nemotron 3 Ultra
nvidia/nemotron-3-ultra-550b-a55b
|
49% 147 / 300 answers | 67 of 100 prompts |
| #71 |
Claude Haiku 4.5
anthropic/claude-haiku-4.5
|
48.7% 146 / 300 answers | 62 of 100 prompts |
| #72 |
Gemini 2.5 Flash
google/gemini-2.5-flash
|
48.7% 146 / 300 answers | 68 of 100 prompts |
| #73 |
Gemini 3 Flash Preview
google/gemini-3-flash-preview
|
48.7% 146 / 300 answers | 65 of 100 prompts |
| #74 |
Gemini 3.5 Flash
google/gemini-3.5-flash
|
48.7% 146 / 300 answers | 64 of 100 prompts |
| #75 |
Gemma 3 27B
google/gemma-3-27b-it
|
48.7% 146 / 300 answers | 58 of 100 prompts |
| #76 |
GLM 4.7
z-ai/glm-4.7
|
48.7% 146 / 300 answers | 62 of 100 prompts |
| #77 |
GPT-4.1 Nano
openai/gpt-4.1-nano
|
48.3% 145 / 300 answers | 67 of 100 prompts |
| #78 |
MiniMax M2.7
minimax/minimax-m2.7
|
47.3% 142 / 300 answers | 69 of 100 prompts |
| #79 |
Nemotron 3 Nano 30B A3B
nvidia/nemotron-3-nano-30b-a3b
|
47% 141 / 300 answers | 68 of 100 prompts |
| #80 |
Mistral Small 3.2 24B
mistralai/mistral-small-3.2-24b-instruct
|
46% 138 / 300 answers | 62 of 100 prompts |
| #81 |
Claude Sonnet 4
anthropic/claude-sonnet-4
|
45.7% 137 / 300 answers | 60 of 100 prompts |
| #82 |
GLM 4.7 Flash
z-ai/glm-4.7-flash
|
45.7% 137 / 300 answers | 66 of 100 prompts |
| #83 |
MiMo-V2.5-Pro
xiaomi/mimo-v2.5-pro
|
45.7% 137 / 300 answers | 67 of 100 prompts |
| #84 |
MiniMax M2.5
minimax/minimax-m2.5
|
45.3% 136 / 300 answers | 67 of 100 prompts |
| #85 |
Qwen3.7 Max
qwen/qwen3.7-max
|
45.3% 136 / 300 answers | 64 of 100 prompts |
| #86 |
Grok 4.20
x-ai/grok-4.20
|
45% 135 / 300 answers | 60 of 100 prompts |
| #87 |
Llama 4 Maverick
meta-llama/llama-4-maverick
|
44.3% 133 / 300 answers | 61 of 100 prompts |
| #88 |
Qwen3.5-9B
qwen/qwen3.5-9b
|
44.3% 133 / 300 answers | 59 of 100 prompts |
| #89 |
Qwen3.7 Plus
qwen/qwen3.7-plus
|
44% 132 / 300 answers | 61 of 100 prompts |
| #90 |
DeepSeek V3.1 Terminus
deepseek/deepseek-v3.1-terminus
|
43.7% 131 / 300 answers | 66 of 100 prompts |
| #91 |
Gemini 3.1 Pro Preview
google/gemini-3.1-pro-preview
|
43.3% 130 / 300 answers | 54 of 100 prompts |
| #92 |
GLM 5.2
z-ai/glm-5.2
|
43% 129 / 300 answers | 64 of 100 prompts |
| #93 |
Llama 3.1 8B Instruct
meta-llama/llama-3.1-8b-instruct
|
43% 129 / 300 answers | 64 of 100 prompts |
| #94 |
DeepSeek V3.2 Exp
deepseek/deepseek-v3.2-exp
|
42.3% 127 / 300 answers | 62 of 100 prompts |
| #95 |
Llama 3.3 70B Instruct
meta-llama/llama-3.3-70b-instruct
|
41.7% 125 / 300 answers | 56 of 100 prompts |
| #96 |
Mistral Small 4
mistralai/mistral-small-2603
|
41.7% 125 / 300 answers | 67 of 100 prompts |
| #97 |
DeepSeek V3.1
deepseek/deepseek-chat-v3.1
|
41% 123 / 300 answers | 64 of 100 prompts |
| #98 |
DeepSeek V3.2
deepseek/deepseek-v3.2
|
40.7% 122 / 300 answers | 66 of 100 prompts |
| #99 |
Mistral Nemo
mistralai/mistral-nemo
|
39.3% 118 / 300 answers | 53 of 100 prompts |
For each prompt, the three independent runs are compared without pairing run numbers. Repeated normalized answers are matched one-to-one, up to three matches per prompt. Surrounding whitespace, final punctuation, emojis, bold markers, and capitalization are normalized in the same way as the rest of the site. Unambiguous aliases are grouped under their most common format in the dataset. For prompts with a defined set of choices, responses classified as “No valid choice or refused to answer” never count as matching answers. Similarity describes answer overlap in this dataset, not model quality or shared architecture.
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