1. Different Brains, Different Training

Imagine you ask three doctors about a headache:

• One from India,
• One from Germany,
• One from Japan.

All qualified — but all will have learned from different textbooks, languages, and experiences.

AI models are no different.

• Each trained on a different dataset — different slices of the internet, books, code, and human interactions.
• OpenAI’s GPT-4 might have seen millions of English academic papers and Reddit comments.
• Anthropic’s Claude 3 could be more centered on safety, philosophy, and empathy.
• Google’s Gemini could be centered on factual recall and web-scale knowledge.
• Meta’s Llama 3 could draw more from open-source data sets and code-heavy text.

So when you ask them the same question — say, “What’s the meaning of consciousness?” — they’re pulling from different “mental libraries.”
The variety of information generates varying world views, similar to humans raised in varying cultures.

2. Architecture Controls Personality

• Even with the same data, the way a model is built — its architecture — changes its pattern of thought.
• Some are transformer-based with large context windows (e.g., 1 million tokens in Gemini), and some have smaller windows but longer reasoning chains.

These adjustments in architecture affect how the model:

• Joints concepts
• Balances creativity with accuracy
• Handles ambiguity

It’s like giving two chefs the same ingredients but different pieces of kitchen equipment — one will bake, and another will fry.

3. The Training Objectives Are Different

Each AI model has been “trained” to please their builders uniquely.
Some models are tuned to be:

• Helpful (giving quick responses)
• Truthful (admitting uncertainty)
• Innocent (giving sensitive topics a miss)
• Innovative (generating new wordings)
• Brief or Detailed (instructional calibration-dependent)

For example:

• GPT-4 might say: “Here are 3 balanced arguments with sources…”
• Claude 3 might say: “This is a deep philosophical question. Let’s go through it step by step…”
• Gemini might say: “Based on Google Search, here is today’s scientific consensus…”

They’re all technically accurate — just trained to answer in different ways.
You could say they have different personalities because they used different “reward functions” during training.

4. The Data Distribution Introduces Biases (in the Neutral Sense)

• All models reflect the biases of the data — social bias, but also linguistic and topical bias.
• If a model is trained on more U.S. news sites, it can be biased towards Western perspectives.
• If another one is trained on more research articles, it can sound more like an academic or formal voice.

These differences can gently impact:

• Tone (formal vs. informal)
• Structure (list vs. story)
• Confidence (assertive vs. conservative)

Which is why one AI would respond, “Yes, definitely!” and another, “It depends on context.”

 5. Randomness (a.k.a. Sampling Temperature)

• Responses can vary from one run to the next in the same model.
• Why? Because AI models are probabilistic.

When they generate text, they don’t select the “one right” next word — instead, they select among a list of likely next words, weighted by probability.

That’s governed by something referred to as the temperature:

• Low temperature (e.g., 0.2): deterministic, factual answers
• High temperature (e.g., 0.8): creative, diverse, narrative-like answers

So even GPT-4 can answer with a placating “teacher” response one moment and a poetic “philosopher” response the next — entirely from sampling randomness.

6. Context Window and Memory Differences

Models have different “attention spans.”

For example:

• GPT-4 Turbo can process 128k tokens (about 300 pages) in context.
• Claude 3 Opus can hold 200k tokens.
• Llama 3 can only manage 8k–32k tokens.

In other words, some models get to see more of the conversation, know more deeply in context, and draw on previous details — while others forget quickly and respond more narrowly.

So even if you ask “the same” question, your history of conversation changes how each model responds to it.

It’s sort of like receiving two pieces of advice — one recalls your whole saga, the other only catches the last sentence.

 7. Alignment & Safety Filters

New AI models are subjected to an alignment tuning phase — where human guidance teaches them what’s “right” to say.

This tuning affects:

• What they discuss
• How they convey sensitive content
• How diligently they report facts

Therefore, one model will not provide medical advice at all, and another will provide it cautiously with disclaimers.

This makes output appear inconsistent, but it’s intentional — it’s safety vs. sameness.

8. Interpretation, Not Calculation

Language models do not compute answers — they understand questions.

• Ask “What is love?” — one model might cite philosophers, another might talk about human emotion, and another might designate oxytocin levels.
• They’re not wrong; they’re applying your question through their trained comprehension.
• That’s why being clear in your prompt is so crucial.
• Even a small difference — “Explain love scientifically” versus “What does love feel like?” — generates wildly different answers.

9. In Brief — They’re Like Different People Reading the Same Book

Imagine five people reading the same book.

When you ask what it’s about:

• One talks about plot.
• Another talks about themes.
• Another remembers dialogue.
• One names flaws.
• Another tells you how they felt.

Both are drawing from the same feed but translating it through their own mind, memories, and feelings.

That’s how AI models also differ — each is an outcome of its training, design, and intent.

10. So What Does This Mean for Us?

For developers, researchers, or curious users like you:

• Don’t seek consensus between models — rejoice at diversity of thought.
• Use independent models to cross-validate (if two correspond independently, confidence is enhanced).
• When generating, try out what model works best in your domain (medical, legal, artistic, etc.).

Remember: an AI answer reflects probabilities, not a unique truth.

Final Thought

“Various AI models don’t disagree because one is erroneous — they vary because each views the world from a different perspective.”

In a way, that’s what makes them powerful: you’re not just getting one brain’s opinion — you’re tapping into a chorus of digital minds, each trained on a different fragment of human knowledge.