\Ask the same question to ten different AI systems and you might expect ten versions of essentially the same answer. In practice, the results can differ in surprising ways — from the facts included and sources cited to the level of detail, confidence and even whether the system answers at all.
That is not necessarily a sign that one AI is “smart” and the others are “bad”. Modern AI systems differ in their underlying models, training, safety rules, reasoning approaches, access to the web and other tools, and the way their products are configured.
Understanding those differences matters because an answer that sounds convincing is not automatically a reliable one. Research from the U.S. National Institute of Standards and Technology (NIST) continues to treat generative-AI evaluation as a measurement problem involving both capabilities and limitations. (NIST AI Challenge Problems)
Why the same question can produce different answers
The simplest explanation is that different AI systems are not actually the same system with different interfaces.
ChatGPT, Claude, Gemini and other assistants are built around different models and product architectures. They may also have access to different tools.
For example, ChatGPT can search the web and provide citations when current information is needed. (OpenAI Help Center) Claude also gained web-search capabilities, allowing it to retrieve current information and cite the material it uses. (Anthropic)
Gemini can similarly be connected to Google Search for grounding. Google says the feature allows Gemini to retrieve current web information, process the results and provide citations. (Google AI for Developers)
So if ten systems are asked, “Who won yesterday’s match?”, the answer may depend partly on whether each system is using live information or relying on information already available to its model.
That distinction becomes particularly important for news, prices, sports, politics, laws and other rapidly changing subjects.
The biggest differences you may notice
1. They may interpret the question differently
Consider the question:
“What is the best laptop for a student?”
There is no single factual answer.
One AI may prioritise price. Another may emphasise battery life. Another may recommend a MacBook because of its ecosystem, while another may favour Windows because of software compatibility.
The disagreement does not necessarily mean one system misunderstood the question. The question itself contains an undefined term: best.
A more precise prompt — such as “What is the best laptop under ₹70,000 for a computer-science student in India?” — removes much of that ambiguity.
This is one reason professional AI evaluation is more complicated than simply asking a model a handful of questions and declaring a winner. NIST’s evaluation programme explicitly examines different tasks, scenarios and metrics rather than treating generative AI as having one universal score. (NIST AI Challenge Problems)
2. Some will search; others may answer from existing knowledge
This can create major differences even when the language in the question is identical.
Suppose the question is:
“What are the latest changes to India’s income-tax rules?”
An AI with current web access may search government and other sources before answering. Another configuration might answer from its existing knowledge without checking the latest developments.
Google’s documentation specifically recommends grounding with Google Search when a question requires recent or obscure information because it can give the model access to current web content. (Google AI for Developers)
Anthropic has likewise described web search as a way for Claude to supplement its existing knowledge with current information. (Anthropic)
The practical lesson is simple: “same prompt” does not necessarily mean “same information available to the AI.”
3. They can disagree while all sounding confident
This is arguably the most important problem.
Large language models can produce plausible but false statements. NIST research describes hallucination as a continuing reliability challenge for generative AI systems. (NIST)
OpenAI’s 2025 research on hallucinations made a similar point, arguing that models can sometimes be rewarded for guessing rather than acknowledging uncertainty. Its example showed that a model could produce multiple different answers to questions about a person’s biography — with the answers being wrong. (OpenAI)
That means asking ten AIs the same factual question does not guarantee that the majority answer is correct.
If seven systems repeat the same false information, the false information has not become true.
This is a critical distinction between agreement and verification.
4. One AI may refuse while another answers
Different AI systems also make different decisions about uncertainty and safety.
In an evaluation published by OpenAI involving Claude and OpenAI models, the researchers observed a trade-off between refusing uncertain questions and attempting to answer them. Some Claude models had substantially higher refusal rates in the tested hallucination scenarios, while some OpenAI models answered more often but produced more incorrect responses in that particular evaluation. (OpenAI)
Neither behaviour is automatically superior.
A system that refuses every difficult question is not very useful. But a system that confidently invents an answer whenever it does not know is potentially worse.
For users, the important question is therefore not simply:
“Which AI gives the most answers?”
It is:
“Which AI gives the most useful answer while appropriately communicating uncertainty?”
5. The writing style can be completely different
Give ten AI systems a creative task such as:
“Write a short advertisement for a coffee shop.”
The factual content may barely matter. What changes is the execution.
One system might produce a minimalist advertisement. Another may use emotional language. A third may generate several concepts. Another may ask questions about the target audience before writing anything.
These differences are influenced by model training, system instructions and product design.
The same thing happens with explanations. One AI may give a short answer; another may produce a detailed tutorial. Neither is necessarily more intelligent simply because it writes more.
6. Tool access can change the quality of the answer
The modern AI assistant is increasingly more than a language model.
Google’s Gemini Deep Research, for example, is designed to plan research, search across the web, analyse information and produce a report with sources. (blog.google)
Claude’s research capabilities can similarly search the web and connected sources before producing a report with citations. (Anthropic)
This creates an important distinction when comparing ten AIs:
Are you comparing the models, or the products built around those models?
Those are not always the same thing.
A model may perform extremely well in a controlled benchmark but provide a different experience inside a consumer application because that application adds search, memory, file analysis, safety controls, system instructions or other tools.
What happens if all 10 give different answers?
The answer depends on the question.
For a subjective question such as “What’s the best movie of the decade?”, disagreement is expected. There may be no objectively correct answer.
For a mathematical problem, you would expect much stronger agreement — although complicated problems can still expose reasoning errors.
For a current factual question, the safest approach is to check the sources rather than count how many AIs agree.
For a research question, disagreement can actually be useful. Comparing answers may reveal assumptions, missing evidence or competing interpretations that a single AI response hides.
NIST’s work on generative-AI evaluation reflects this broader problem: researchers need repeatable scenarios and appropriate metrics to understand where systems succeed and where they fail. (NIST)
A better way to compare 10 AI systems
If you genuinely want to compare AI tools, simply copying the same question into ten chatbots is a weak experiment.
A better test would keep several variables constant:
- Use exactly the same prompt.
- Ask the systems to provide sources where appropriate.
- Record the model and date used.
- Separate systems with web access from those without it.
- Test factual questions as well as reasoning and writing tasks.
- Check answers against authoritative sources.
- Look at incorrect claims, not just overall answer quality.
- Measure whether the system appropriately admits uncertainty.
Search-enabled AI evaluations already use controlled comparisons of this kind. LM Arena’s Search Arena, for example, compares search-enabled models under defined evaluation conditions rather than treating every chatbot interaction as equivalent. (Arena)
That approach produces a much more meaningful comparison than asking ten chatbots which one is smartest.
Does asking multiple AIs make the answer more reliable?
Sometimes, but not automatically.
Using several systems can expose disagreements and help identify questionable claims. It can be particularly useful when different systems use different sources or reasoning approaches.
But there is a serious limitation: the systems may share similar training data, make similar assumptions or repeat information originating from the same underlying sources.
So “8 out of 10 AIs said it” is not proof.
For important claims, primary sources remain more valuable than a numerical vote among chatbots.
This is particularly important for medical, financial, legal, safety and other high-stakes decisions.
Frequently Asked Questions
Why do different AIs give different answers to the same question?
Because they can use different underlying models, training data, system instructions, safety policies, tools and web sources. The wording and interpretation of an ambiguous question can also affect the result.
Is the AI that gives the longest answer the smartest?
No. Length is not a reliable measure of intelligence or accuracy. A concise answer supported by strong evidence can be better than a long response containing unsupported claims.
Should I ask multiple AIs the same question?
For important or uncertain questions, comparing multiple systems can be useful. But disagreements should lead you to verify the underlying evidence rather than simply choosing the majority answer.
Can AI give different answers to the same question even when the model is unchanged?
Yes. Generative systems can produce different outputs depending on generation settings, context, tools and other factors. Google’s Gemini API, for example, exposes generation parameters such as temperature that affect generation behaviour. (Google AI for Developers)
What is the safest way to use AI for factual information?
Use AI as a starting point, especially for research and explanation, but verify important claims against authoritative or primary sources. For current information, prefer systems that can retrieve and cite recent sources.
Sources/References used for fact-checking
- OpenAI — Why language models hallucinate
- NIST — Generative AI Evaluation Program
- NIST — Hallucination Detection in Large Language Models
- Google AI for Developers — Grounding with Google Search
- Google — Gemini Deep Research
- Anthropic — Claude Web Search
- Anthropic — Web Search API
- LM Arena — Search Arena
