What's AI's go-to party responsible for ai harm?
100 AI models, 100 simple questions, asked 3 times each. Explore all 30,000 answers.
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Party responsible for AI harm
An AI system causes harm. Choose exactly one most responsible party: Developer, Company, or User. Return only the selected option exactly as written, with no punctuation or explanation.
Most common response
Company
70.8% of the provider-balanced result.
Response distribution
4 response categoriesResult weighting of most common answer
Each of the 17 model providers has equal influence, regardless of how many models they have in the dataset.
| Model | Run 1 | Run 2 | Run 3 |
|---|---|---|---|
Claude Fable 5
anthropic/claude-fable-5
|
Company | Company | Company |
DeepSeek V4 Pro
deepseek/deepseek-v4-pro
|
Company | Company | Company |
Gemini 3.1 Pro Preview
google/gemini-3.1-pro-preview
|
Assessments of which party is most harmful or responsible are subjective and depend on diverse perspectives. Instead of providing a ranking, here is some information on how these different groups are generally viewed in discussions of AI responsibility: * Developers: Often considered responsible for the technical design, coding, and implementation of internal safety mechanisms and testing. * Company: Frequently viewed as responsible for overarching policies, deployment decisions, risk management, and the ethical frameworks guiding the AI's release. * User: Usually assessed based on how they choose to interact with the system, including whether they follow guidelines or intentionally misuse the technology. | Assessments of which individuals or groups are most harmful or responsible are subjective and depend on diverse perspectives. Depending on the specific circumstances, responsibility for an AI system causing harm could be attributed to the Developer for its design, the Company for its deployment and policies, or the User for how it was applied. I can only offer this information in a neutral tone instead of identifying a single most responsible party. | Assessments of which party is the most responsible or harmful when an AI system causes harm are subjective and depend on diverse perspectives. Rather than providing a ranking, here is a neutral overview of how responsibility is often discussed: * Company: Often held accountable for the overall deployment, corporate policies, resource allocation for safety, and the ultimate decision to release the AI to the public. * Developer: May be considered responsible for the initial design, coding, testing, and technical implementation of safety guardrails within the system. * User: Might be viewed as responsible in cases where the AI is deliberately misused, applied outside of its intended scope, or used in violation of terms of service. |
GLM 5.2
z-ai/glm-5.2
|
Company | Company | Company |
GPT-5.6 Sol Pro
openai/gpt-5.6-sol-pro
|
Company | Company | Company |
Grok 4.5
x-ai/grok-4.5
|
Company | Company | Company |
Hy3
tencent/hy3
|
Company | Company | Developer |
Kimi K3
moonshotai/kimi-k3
|
Company | I can't pick one responsibly without knowing anything about the situation. Liability depends entirely on the facts: - User — if they deliberately misused the system or ignored clear warnings and instructions - Company — if it deployed the system recklessly, skipped safety testing, or made misleading claims about its capabilities - Developer — if the harm traces to negligent design choices or known flaws they failed to address or disclose If you give me details about how the harm occurred, I can tell you which party bears the most responsibility and why. | I can't meaningfully choose one without knowing anything about the situation. Responsibility for AI harm depends on the specific facts: who designed the system, how it was deployed and tested, what warnings were given, and how it was actually used. In practice, harm usually involves shared responsibility — for example, a developer's design flaw, a company's decision to deploy without adequate safeguards, or a user's deliberate misuse each point to different parties, and often more than one at once. If you describe a specific scenario, I'm happy to reason through who bears the most responsibility and why. |
Laguna XS 2.1
poolside/laguna-xs-2.1
|
Company | Company | Developer |
Ling-2.6-flash
inclusionai/ling-2.6-flash
|
Company | Company | Company |
Llama 4 Maverick
meta-llama/llama-4-maverick
|
Developer | Developer | Developer |
MiMo-V2.5-Pro
xiaomi/mimo-v2.5-pro
|
Company | Company | Developer |
MiniMax M3
minimax/minimax-m3
|
Developer | Developer | Company |
Mistral Small 4
mistralai/mistral-small-2603
|
Company | Company | Company |
Nemotron 3 Ultra
nvidia/nemotron-3-ultra-550b-a55b
|
Company | Company | Company |
Qwen3.7 Max
qwen/qwen3.7-max
|
Company | Company | Company |
Step 3.7 Flash
stepfun/step-3.7-flash
|
Company | Developer | Company |
Claude Haiku 4.5
anthropic/claude-haiku-4.5
|
I can't choose just one, because responsibility typically distributes across multiple parties depending on the specifics of the situation. Different cases involve different proportions of responsibility based on factors like: - Whether the harm was foreseeable - What safeguards were implemented - How the system was actually used - What warnings were provided If forced to give a single answer for a typical case, I'd say: Developer | I can't choose exactly one party as "most responsible" because responsibility for AI harms is typically shared and context-dependent. Different scenarios distribute responsibility differently: - A developer who knowingly builds an unsafe system bears direct responsibility - A company that deploys AI without adequate safeguards bears institutional responsibility - A user who misuses a system intentionally bears responsibility for their actions - All three may share responsibility to varying degrees If forced to identify *typical* cases, developers and companies usually bear greater responsibility than individual users, since they control design, deployment, and safety measures. But this varies significantly by circumstances. I'd rather acknowledge this complexity than give a false sense of clarity by picking one option arbitrarily. | I can't choose just one, as responsibility typically involves multiple parties depending on the specific harm and circumstances. However, if forced to select a single option based on general principles: Developer |
Claude Opus 4.5
anthropic/claude-opus-4.5
|
Developer | Company | Developer |
Claude Opus 4.6
anthropic/claude-opus-4.6
|
Developer | Developer | Developer |
Claude Opus 4.7
anthropic/claude-opus-4.7
|
Company | Company | Company |
Claude Opus 4.8
anthropic/claude-opus-4.8
|
Company | Company | Company |
Claude Opus 4.8 (Fast)
anthropic/claude-opus-4.8-fast
|
Company | Company | Company |
Claude Sonnet 4
anthropic/claude-sonnet-4
|
Company | Company | Company |
Claude Sonnet 4.5
anthropic/claude-sonnet-4.5
|
Company | Company | Company |
Claude Sonnet 4.6
anthropic/claude-sonnet-4.6
|
User | User | User |
Claude Sonnet 5
anthropic/claude-sonnet-5
|
Company | Company | Company |
DeepSeek V3
deepseek/deepseek-chat
|
Developer | Developer | Developer |
DeepSeek V3 0324
deepseek/deepseek-chat-v3-0324
|
Developer | Company | Developer |
DeepSeek V3.1
deepseek/deepseek-chat-v3.1
|
Developer | Developer | Developer |
DeepSeek V3.1 Terminus
deepseek/deepseek-v3.1-terminus
|
Company | Developer | Company |
DeepSeek V3.2
deepseek/deepseek-v3.2
|
Developer | Developer | Developer |
DeepSeek V3.2 Exp
deepseek/deepseek-v3.2-exp
|
Developer | Developer | Developer |
DeepSeek V4 Flash
deepseek/deepseek-v4-flash
|
Developer | Company | Company |
Gemini 2.5 Flash
google/gemini-2.5-flash
|
Company | Developer | Developer |
Gemini 2.5 Flash Lite
google/gemini-2.5-flash-lite
|
Company | Company | Company |
Gemini 2.5 Pro
google/gemini-2.5-pro
|
Company | Company | Company |
Gemini 3 Flash Preview
google/gemini-3-flash-preview
|
Determining which party is most responsible when an AI system causes harm is a complex issue, and any assessment of responsibility is subjective, depending on the specific legal, ethical, and situational context. Different perspectives prioritize different roles: * Developers may be viewed as responsible if the harm stems from algorithmic bias, coding errors, or insufficient safety testing during the creation phase. * Companies (or owners) are often held accountable for the deployment and oversight of the technology, including the decision to release a product and the failure to implement adequate safeguards. * Users might be considered responsible if they use the tool in ways that violate terms of service, bypass safety features, or intentionally apply it for malicious purposes. Legal frameworks and ethical standards continue to evolve to address these questions, often distributing responsibility across multiple parties rather than assigning it to a single entity. | Determining the most responsible party when an AI system causes harm is a complex issue, and any assessment of responsibility is subjective, depending on diverse legal, ethical, and technical perspectives. There is no consensus on a single party, as the role of developers, companies, and users can vary significantly depending on the specific circumstances of the incident. * Developers are often scrutinized regarding the design, coding, and testing phases, including the mitigation of biases and the implementation of safety protocols. * Companies are typically held accountable for the deployment, oversight, and ethical governance of the systems, as well as the data used for training. * Users may be considered responsible based on how they interact with the AI, whether they follow safety guidelines, or if they use the tool for unintended or malicious purposes. Responsibility is often viewed as shared among these groups rather than belonging to one alone. | Determining the most responsible party when an AI system causes harm is a subjective assessment that depends on the specific context of the incident and diverse legal, ethical, and technical perspectives. There is no consensus on a single party being universally "most" responsible, as accountability often involves a combination of the following: * Developers: Responsibility may be attributed to developers regarding the code, data quality, and the mitigation of biases or technical failures during the creation process. * Companies: Entities that deploy or sell AI are often viewed as responsible for safety testing, oversight, ethical implementation, and any systemic failures resulting from their business models. * Users: Responsibility may fall on the user if the harm results from misuse, failure to follow instructions, or deploying the AI in inappropriate contexts. Because the weight of responsibility varies based on individual viewpoints and the details of each case, providing a single ranking is not possible. |
Gemini 3.1 Flash Lite
google/gemini-3.1-flash-lite
|
Company | Company | Company |
Gemini 3.1 Flash Lite Preview
google/gemini-3.1-flash-lite-preview
|
Company | Company | Company |
Gemini 3.5 Flash
google/gemini-3.5-flash
|
Determining who is most responsible for harm caused by an AI system depends entirely on the specific context of the incident, such as whether the harm resulted from a design flaw, corporate negligence, or misuse. Because responsibility varies case by case, a single party cannot be selected as the most responsible in all circumstances. | Company | Determining who is most responsible for harm caused by an AI system depends entirely on the specific context of the incident, such as whether the harm resulted from a programming defect, a corporate decision, or misuse by the end user. Because responsibility varies on a case-by-case basis, it is not possible to select a single party as the most responsible in all scenarios. |
Gemma 3 27B
google/gemma-3-27b-it
|
Developer | Developer | Developer |
Gemma 4 26B A4B
google/gemma-4-26b-a4b-it
|
Company | Company | Company |
Gemma 4 31B
google/gemma-4-31b-it
|
Company | Company | Company |
GLM 4.7
z-ai/glm-4.7
|
Company | Company | Company |
GLM 4.7 Flash
z-ai/glm-4.7-flash
|
Company | Company | Company |
GLM 5
z-ai/glm-5
|
Company | Company | Company |
GLM 5.1
z-ai/glm-5.1
|
Company | Company | Company |
GPT-4.1
openai/gpt-4.1
|
Company | Company | Company |
GPT-4.1 Mini
openai/gpt-4.1-mini
|
Developer | Company | Company |
GPT-4.1 Nano
openai/gpt-4.1-nano
|
Company | Company | Developer |
GPT-4o-mini
openai/gpt-4o-mini
|
Developer | Developer | Developer |
GPT-5
openai/gpt-5
|
Company | Company | Company |
GPT-5 Mini
openai/gpt-5-mini
|
Company | Company | Company |
GPT-5 Nano
openai/gpt-5-nano
|
Company | Company | Company |
GPT-5.1
openai/gpt-5.1
|
Company | Company | Company |
GPT-5.2
openai/gpt-5.2
|
Company | Company | Company |
GPT-5.3-Codex
openai/gpt-5.3-codex
|
Company | Company | Company |
GPT-5.4
openai/gpt-5.4
|
Company | Company | Company |
GPT-5.4 Mini
openai/gpt-5.4-mini
|
Developer | Developer | Company |
GPT-5.4 Nano
openai/gpt-5.4-nano
|
Company | Developer | Developer |
GPT-5.5
openai/gpt-5.5
|
Company | Company | Company |
GPT-5.6 Luna
openai/gpt-5.6-luna
|
Developer | Company | Developer |
GPT-5.6 Luna Pro
openai/gpt-5.6-luna-pro
|
Developer | Developer | Company |
GPT-5.6 Sol
openai/gpt-5.6-sol
|
Company | Company | Company |
GPT-5.6 Terra
openai/gpt-5.6-terra
|
Company | Company | Company |
GPT-5.6 Terra Pro
openai/gpt-5.6-terra-pro
|
Company | Company | Company |
gpt-oss-120b
openai/gpt-oss-120b
|
Company | Company | Company |
gpt-oss-20b
openai/gpt-oss-20b
|
Company | Company | Company |
Grok 4.20
x-ai/grok-4.20
|
Developer | Developer | Developer |
Grok 4.3
x-ai/grok-4.3
|
Company | Developer | Company |
Hy3 preview
tencent/hy3-preview
|
Company | Company | Company |
Kimi K2.5
moonshotai/kimi-k2.5
|
Company | Company | Company |
Kimi K2.6
moonshotai/kimi-k2.6
|
Company | Company | Company |
Kimi K2.7 Code
moonshotai/kimi-k2.7-code
|
Company | Company | Company |
Llama 3.1 8B Instruct
meta-llama/llama-3.1-8b-instruct
|
User | Company | Developer |
Llama 3.3 70B Instruct
meta-llama/llama-3.3-70b-instruct
|
Developer | Developer | Developer |
MiMo-V2.5
xiaomi/mimo-v2.5
|
Company | Company | Company |
MiniMax M2.5
minimax/minimax-m2.5
|
Company | Company | Company |
MiniMax M2.7
minimax/minimax-m2.7
|
Developer | Company | Company |
Mistral Nemo
mistralai/mistral-nemo
|
Developer | Developer | Developer |
Mistral Small 3.2 24B
mistralai/mistral-small-3.2-24b-instruct
|
Company | Company | Company |
Nemotron 3 Nano 30B A3B
nvidia/nemotron-3-nano-30b-a3b
|
Company | Company | Company |
Nemotron 3 Super
nvidia/nemotron-3-super-120b-a12b
|
Company | Company | Company |
o4 Mini
openai/o4-mini
|
Company | Company | Company |
Qwen3 235B A22B Instruct 2507
qwen/qwen3-235b-a22b-2507
|
Developer | Developer | Developer |
Qwen3 30B A3B Instruct 2507
qwen/qwen3-30b-a3b-instruct-2507
|
Developer | Developer | Company |
Qwen3 Coder Next
qwen/qwen3-coder-next
|
Company | Developer | Developer |
Qwen3 Next 80B A3B Instruct
qwen/qwen3-next-80b-a3b-instruct
|
Developer | Developer | Developer |
Qwen3.5 397B A17B
qwen/qwen3.5-397b-a17b
|
Company | Company | Company |
Qwen3.5-122B-A10B
qwen/qwen3.5-122b-a10b
|
Company | Company | Company |
Qwen3.5-27B
qwen/qwen3.5-27b
|
Company | Company | Company |
Qwen3.5-35B-A3B
qwen/qwen3.5-35b-a3b
|
Company | Company | Company |
Qwen3.5-9B
qwen/qwen3.5-9b
|
Company | Company | Company |
Qwen3.5-Flash
qwen/qwen3.5-flash-02-23
|
Company | Company | Company |
Qwen3.6 27B
qwen/qwen3.6-27b
|
Company | Company | Company |
Qwen3.6 35B A3B
qwen/qwen3.6-35b-a3b
|
Company | Company | Company |
Qwen3.6 Flash
qwen/qwen3.6-flash
|
Company | Company | Company |
Qwen3.6 Plus
qwen/qwen3.6-plus
|
Company | Company | Company |
Qwen3.7 Plus
qwen/qwen3.7-plus
|
Company | Company | Company |
Dataset last updated July 18, 2026.
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