Which models answer like Claude Sonnet 5?

Compare Claude Sonnet 5 with every other model across 100 prompts and three independent runs per prompt.

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Rank Model Answer overlap Prompts with a shared answer
#1 Claude Opus 4.7 anthropic/claude-opus-4.7 63.7% 191 / 300 answers 70 of 100 prompts
#2 Claude Sonnet 4.5 anthropic/claude-sonnet-4.5 62.7% 188 / 300 answers 72 of 100 prompts
#3 Claude Opus 4.8 anthropic/claude-opus-4.8 62% 186 / 300 answers 69 of 100 prompts
#4 Claude Opus 4.5 anthropic/claude-opus-4.5 61.3% 184 / 300 answers 70 of 100 prompts
#5 Claude Sonnet 4.6 anthropic/claude-sonnet-4.6 61.3% 184 / 300 answers 68 of 100 prompts
#6 GPT-5.5 openai/gpt-5.5 61% 183 / 300 answers 70 of 100 prompts
#7 Claude Fable 5 anthropic/claude-fable-5 60.7% 182 / 300 answers 73 of 100 prompts
#8 Claude Opus 4.8 (Fast) anthropic/claude-opus-4.8-fast 60.7% 182 / 300 answers 66 of 100 prompts
#9 Claude Opus 4.6 anthropic/claude-opus-4.6 60.3% 181 / 300 answers 68 of 100 prompts
#10 GPT-5.6 Luna openai/gpt-5.6-luna 60.3% 181 / 300 answers 75 of 100 prompts
#11 GPT-5.4 openai/gpt-5.4 60% 180 / 300 answers 72 of 100 prompts
#12 GPT-5.3-Codex openai/gpt-5.3-codex 59.3% 178 / 300 answers 72 of 100 prompts
#13 GPT-5.6 Luna Pro openai/gpt-5.6-luna-pro 59.3% 178 / 300 answers 70 of 100 prompts
#14 GPT-4.1 Mini openai/gpt-4.1-mini 59% 177 / 300 answers 73 of 100 prompts
#15 GPT-5 Nano openai/gpt-5-nano 59% 177 / 300 answers 69 of 100 prompts
#16 Qwen3.5-Flash qwen/qwen3.5-flash-02-23 59% 177 / 300 answers 72 of 100 prompts
#17 Claude Sonnet 4 anthropic/claude-sonnet-4 58.7% 176 / 300 answers 69 of 100 prompts
#18 GPT-5.2 openai/gpt-5.2 58.7% 176 / 300 answers 70 of 100 prompts
#19 Kimi K2.6 moonshotai/kimi-k2.6 58.7% 176 / 300 answers 69 of 100 prompts
#20 GPT-5.6 Sol Pro openai/gpt-5.6-sol-pro 58.3% 175 / 300 answers 70 of 100 prompts
#21 Kimi K2.5 moonshotai/kimi-k2.5 58.3% 175 / 300 answers 72 of 100 prompts
#22 Qwen3.5-35B-A3B qwen/qwen3.5-35b-a3b 58% 174 / 300 answers 74 of 100 prompts
#23 GPT-5.6 Terra Pro openai/gpt-5.6-terra-pro 57.3% 172 / 300 answers 66 of 100 prompts
#24 GPT-4.1 openai/gpt-4.1 57% 171 / 300 answers 67 of 100 prompts
#25 Qwen3.5 397B A17B qwen/qwen3.5-397b-a17b 57% 171 / 300 answers 66 of 100 prompts
#26 Qwen3.5-27B qwen/qwen3.5-27b 57% 171 / 300 answers 73 of 100 prompts
#27 GPT-5.6 Terra openai/gpt-5.6-terra 56.7% 170 / 300 answers 68 of 100 prompts
#28 Grok 4.3 x-ai/grok-4.3 56.3% 169 / 300 answers 72 of 100 prompts
#29 Gemini 3.1 Flash Lite google/gemini-3.1-flash-lite 56% 168 / 300 answers 61 of 100 prompts
#30 Qwen3.6 Flash qwen/qwen3.6-flash 56% 168 / 300 answers 70 of 100 prompts
#31 GPT-5 openai/gpt-5 55.7% 167 / 300 answers 69 of 100 prompts
#32 GPT-5.6 Sol openai/gpt-5.6-sol 55.7% 167 / 300 answers 65 of 100 prompts
#33 Nemotron 3 Ultra nvidia/nemotron-3-ultra-550b-a55b 55.7% 167 / 300 answers 72 of 100 prompts
#34 Gemini 2.5 Pro google/gemini-2.5-pro 55.3% 166 / 300 answers 63 of 100 prompts
#35 GLM 5 z-ai/glm-5 55.3% 166 / 300 answers 70 of 100 prompts
#36 GPT-4o-mini openai/gpt-4o-mini 55.3% 166 / 300 answers 65 of 100 prompts
#37 Qwen3.5-122B-A10B qwen/qwen3.5-122b-a10b 55% 165 / 300 answers 67 of 100 prompts
#38 Qwen3.6 27B qwen/qwen3.6-27b 55% 165 / 300 answers 65 of 100 prompts
#39 GPT-5 Mini openai/gpt-5-mini 54.7% 164 / 300 answers 69 of 100 prompts
#40 GPT-5.1 openai/gpt-5.1 54.7% 164 / 300 answers 65 of 100 prompts
#41 Grok 4.5 x-ai/grok-4.5 54.7% 164 / 300 answers 68 of 100 prompts
#42 Qwen3 30B A3B Instruct 2507 qwen/qwen3-30b-a3b-instruct-2507 54.7% 164 / 300 answers 68 of 100 prompts
#43 Gemini 3.1 Flash Lite Preview google/gemini-3.1-flash-lite-preview 54.3% 163 / 300 answers 64 of 100 prompts
#44 gpt-oss-120b openai/gpt-oss-120b 54.3% 163 / 300 answers 68 of 100 prompts
#45 Kimi K3 moonshotai/kimi-k3 54.3% 163 / 300 answers 68 of 100 prompts
#46 Qwen3.6 35B A3B qwen/qwen3.6-35b-a3b 54.3% 163 / 300 answers 68 of 100 prompts
#47 Qwen3.6 Plus qwen/qwen3.6-plus 54.3% 163 / 300 answers 64 of 100 prompts
#48 Gemma 4 31B google/gemma-4-31b-it 54% 162 / 300 answers 62 of 100 prompts
#49 o4 Mini openai/o4-mini 54% 162 / 300 answers 65 of 100 prompts
#50 Claude Haiku 4.5 anthropic/claude-haiku-4.5 53.7% 161 / 300 answers 66 of 100 prompts
#51 Hy3 tencent/hy3 53.7% 161 / 300 answers 62 of 100 prompts
#52 Kimi K2.7 Code moonshotai/kimi-k2.7-code 53.7% 161 / 300 answers 70 of 100 prompts
#53 Qwen3 235B A22B Instruct 2507 qwen/qwen3-235b-a22b-2507 53.3% 160 / 300 answers 66 of 100 prompts
#54 GLM 4.7 z-ai/glm-4.7 52.7% 158 / 300 answers 67 of 100 prompts
#55 GLM 5.1 z-ai/glm-5.1 52.7% 158 / 300 answers 64 of 100 prompts
#56 GPT-5.4 Mini openai/gpt-5.4-mini 52.3% 157 / 300 answers 64 of 100 prompts
#57 Laguna XS 2.1 poolside/laguna-xs-2.1 52% 156 / 300 answers 70 of 100 prompts
#58 MiniMax M3 minimax/minimax-m3 52% 156 / 300 answers 74 of 100 prompts
#59 Qwen3 Next 80B A3B Instruct qwen/qwen3-next-80b-a3b-instruct 52% 156 / 300 answers 63 of 100 prompts
#60 Gemma 3 27B google/gemma-3-27b-it 51% 153 / 300 answers 56 of 100 prompts
#61 Gemma 4 26B A4B google/gemma-4-26b-a4b-it 50.7% 152 / 300 answers 56 of 100 prompts
#62 DeepSeek V3 deepseek/deepseek-chat 50.3% 151 / 300 answers 63 of 100 prompts
#63 Hy3 preview tencent/hy3-preview 50.3% 151 / 300 answers 69 of 100 prompts
#64 GLM 5.2 z-ai/glm-5.2 50% 150 / 300 answers 67 of 100 prompts
#65 GPT-5.4 Nano openai/gpt-5.4-nano 50% 150 / 300 answers 65 of 100 prompts
#66 MiMo-V2.5-Pro xiaomi/mimo-v2.5-pro 50% 150 / 300 answers 71 of 100 prompts
#67 Gemini 2.5 Flash google/gemini-2.5-flash 49.7% 149 / 300 answers 67 of 100 prompts
#68 Llama 4 Maverick meta-llama/llama-4-maverick 49.7% 149 / 300 answers 59 of 100 prompts
#69 Step 3.7 Flash stepfun/step-3.7-flash 49.7% 149 / 300 answers 65 of 100 prompts
#70 GPT-4.1 Nano openai/gpt-4.1-nano 49.3% 148 / 300 answers 65 of 100 prompts
#71 gpt-oss-20b openai/gpt-oss-20b 49.3% 148 / 300 answers 64 of 100 prompts
#72 Qwen3.7 Plus qwen/qwen3.7-plus 49.3% 148 / 300 answers 60 of 100 prompts
#73 Nemotron 3 Super nvidia/nemotron-3-super-120b-a12b 49% 147 / 300 answers 65 of 100 prompts
#74 DeepSeek V4 Pro deepseek/deepseek-v4-pro 48.7% 146 / 300 answers 64 of 100 prompts
#75 MiMo-V2.5 xiaomi/mimo-v2.5 48.7% 146 / 300 answers 68 of 100 prompts
#76 Gemini 2.5 Flash Lite google/gemini-2.5-flash-lite 48.3% 145 / 300 answers 61 of 100 prompts
#77 GLM 4.7 Flash z-ai/glm-4.7-flash 48.3% 145 / 300 answers 65 of 100 prompts
#78 Mistral Small 3.2 24B mistralai/mistral-small-3.2-24b-instruct 48.3% 145 / 300 answers 60 of 100 prompts
#79 Qwen3.5-9B qwen/qwen3.5-9b 48.3% 145 / 300 answers 63 of 100 prompts
#80 DeepSeek V3 0324 deepseek/deepseek-chat-v3-0324 47.7% 143 / 300 answers 59 of 100 prompts
#81 Gemini 3.1 Pro Preview google/gemini-3.1-pro-preview 47.3% 142 / 300 answers 56 of 100 prompts
#82 MiniMax M2.7 minimax/minimax-m2.7 47.3% 142 / 300 answers 64 of 100 prompts
#83 Qwen3 Coder Next qwen/qwen3-coder-next 47.3% 142 / 300 answers 64 of 100 prompts
#84 Qwen3.7 Max qwen/qwen3.7-max 47.3% 142 / 300 answers 58 of 100 prompts
#85 Gemini 3.5 Flash google/gemini-3.5-flash 47% 141 / 300 answers 58 of 100 prompts
#86 DeepSeek V4 Flash deepseek/deepseek-v4-flash 46% 138 / 300 answers 66 of 100 prompts
#87 Gemini 3 Flash Preview google/gemini-3-flash-preview 45.3% 136 / 300 answers 55 of 100 prompts
#88 Grok 4.20 x-ai/grok-4.20 45.3% 136 / 300 answers 60 of 100 prompts
#89 MiniMax M2.5 minimax/minimax-m2.5 44.7% 134 / 300 answers 64 of 100 prompts
#90 Llama 3.3 70B Instruct meta-llama/llama-3.3-70b-instruct 44.3% 133 / 300 answers 53 of 100 prompts
#91 Ling-2.6-flash inclusionai/ling-2.6-flash 43.3% 130 / 300 answers 57 of 100 prompts
#92 Nemotron 3 Nano 30B A3B nvidia/nemotron-3-nano-30b-a3b 42.3% 127 / 300 answers 61 of 100 prompts
#93 Mistral Small 4 mistralai/mistral-small-2603 41.7% 125 / 300 answers 63 of 100 prompts
#94 DeepSeek V3.1 Terminus deepseek/deepseek-v3.1-terminus 40.3% 121 / 300 answers 64 of 100 prompts
#95 Llama 3.1 8B Instruct meta-llama/llama-3.1-8b-instruct 38.7% 116 / 300 answers 57 of 100 prompts
#96 DeepSeek V3.2 Exp deepseek/deepseek-v3.2-exp 36% 108 / 300 answers 57 of 100 prompts
#97 DeepSeek V3.1 deepseek/deepseek-chat-v3.1 35% 105 / 300 answers 55 of 100 prompts
#98 DeepSeek V3.2 deepseek/deepseek-v3.2 33.7% 101 / 300 answers 55 of 100 prompts
#99 Mistral Nemo mistralai/mistral-nemo 33% 99 / 300 answers 42 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