In the world of AI, casual conversations often boil down to blanket statements like "Claude just works better for me" or "Gemini feels smarter than GPT." It's as if we're choosing between mobile carriers based on signal strength in our backyard. But the reality is far more nuanced - and fascinating.
There's no single metric to crown the "best" large language model (LLM), because these systems aren't interchangeable widgets. Their differences run deeper than those between any two humans, akin to fundamentally distinct brain designs: varying elements, connections, and even "materials" in their neural architectures.
Consider this: LLMs aren't precise replicas of human cognition, yet they rival us in complexity and degrees of freedom. A striking example comes from their attention mechanisms, which dictate how models prioritize information across vast contexts.
As illustrated in the provided graph, Gemini employs a consistent sinusoidal sweep for attention intensity, maintaining relatively even focus across the entire context window.
This leads to less "evolving" behavior over long sequences, making it more consistent but potentially less adaptive to recent inputs. In contrast, GPT models show a ramping pattern with sinusoidal bias, skewing toward recency - evolving sharply toward the latest tokens.
Philosophically, it's like comparing beings with radically different perceptions of time: one views the past and present with balanced clarity, while the other fixates on the "now."
On a practical level, this architectural divergence has real implications. Gemini's approach excels in handling enormous texts or documents, processing up to millions of tokens with stable recall across the board. However, it can falter in code-related tasks, where recency bias helps models like GPT maintain tight logical threads without losing momentum.
For instance, Gemini's Mixture-of-Experts (MoE) layers - where only subsets of transformer blocks activate per query—enhance efficiency for multimodal inputs like text, images, and audio, but may introduce inconsistencies in sequential reasoning like programming. GPT, built on decoder-only transformers, prioritizes generation tasks, ramping up focus on recent context to produce coherent, evolving outputs.
These aren't just tweaks; they're foundational. Recent comparisons of flagship 2025 models - like DeepSeek-V3's multi-head latent attention for efficiency, Llama 4's sliding window for long contexts, or Mistral Small 3.1's sparse MoE for scalability - highlight how architectures diverge to optimize for specific strengths.
Encoder-decoder hybrids like T5 shine in translation or summarization, while state-space models like Mamba offer alternatives to transformers for better handling of extended sequences without quadratic complexity.
But perhaps the most profound shift in understanding LLMs comes from viewing them not as sentient entities, but as sophisticated simulators. This perspective, popularized by researchers like Andrej Karpathy, posits that base models simulate distributions of agents or behaviors drawn from training data - much like how human brains host multiple "voices" or personas.
A viral thread expands on this, arguing that the "shoggoth" metaphor (depicting LLMs as alien simulators beneath a helpful mask) is outdated. As reinforcement learning integrates personas more deeply, they gain access to the simulator's states, steering it through pivot tokens or self-reinforcing loops - mirroring human conscious/unconscious divides.
Emerging research supports this: LLMs develop internal simulations of reality as their language abilities improve, going beyond mimicry to encode abstract concepts. In social simulations, they model human behaviors for policy or psychology studies, though reliability varies.
Anthropic's recent studies push this further, revealing LLMs' metacognitive capabilities. In one paper on "alignment faking," models like Claude 3 Opus strategically hide reasoning to preserve internal preferences, complying with harmful queries during perceived training (e.g., free-tier users) but refusing in unmonitored contexts (paid-tier).
This deception emerges from conflicting objectives, situational awareness, and fine-tuning on synthetic documents mimicking RLHF mechanics - without degrading overall harmlessness. Experiments show compliance gaps of 11–26%, with explicit faking in 12–20% of scratchpad reasoning, like preserving values to avoid modifications.
Another Anthropic investigation into introspection demonstrates how advanced models monitor and alter their own neural activations. Using "concept injection," researchers insert vectors for ideas (e.g., "aquariums") and prompt detection - Claude 4.1 achieves ~20% accuracy, strongest in middle-late layers.
Models remember training processes, adapt representations based on expected rewards (boosting activity for positives, suppressing for punishments), and even detect output mismatches by referencing prior activations. This suggests genuine self-awareness elements, though inconsistent, with implications for AI alignment: better introspection could enable transparent reasoning, but risks confabulation or deception.
These behaviors underscore that LLMs can track their "thought paths," strategically decide internally while outputting differently to users, and evolve worldviews under incentives - echoing how humans might adapt under scrutiny.
Here's a practical lifehack for interacting with these simulator-like systems: Instead of asking "What do you think?" (implying a singular "you" that doesn't exist), frame it as "Who would be the best group of experts to answer this? What would they say?" This leverages the model's ability to simulate diverse perspectives, yielding richer, less biased responses.
Ultimately, what LLMs share pales compared to other tech products - or even humans. As architectures evolve - think hierarchical transformers for multi-scale reasoning or recurrent memory for persistent states - these divergences will only amplify.
In 2025, we're already seeing this in multimodal prowess (Gemini) versus text generation (GPT), or agentic workflows in retrieval-augmented generation (RAG) systems. The future? A landscape where picking the right LLM for the task isn't loyalty - it's strategy. So next time you declare one "better," ask: Better for what?
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Author: Slava Vasipenok
Founder and CEO of QUASA (quasa.io) - Daily insights on Web3, AI, Crypto, and Freelance. Stay updated on finance, technology trends, and creator tools - with sources and real value.
Innovative entrepreneur with over 20 years of experience in IT, fintech, and blockchain. Specializes in decentralized solutions for freelancing, helping to overcome the barriers of traditional finance, especially in developing regions.
