No, you can't assume that if one LLM fails, others will necessarily follow.
Here's the correct way to think about it, broken down by your observations.
1. Why the "Same Principles" Don't Guarantee Identical Failure
You're absolutely right that most LLMs share the same fundamental architecture (Transformer), training objective (next-token prediction), and interaction model (prompt-in, text-out). However, this is like saying all cars have an internal combustion engine, four wheels, and a steering wheel. A Toyota Camry and a Formula 1 car share those principles but have vastly different failure modes.
An LLM's behavior is an emergent product of many variables, not just the core architecture. Two models can fail completely differently on the same prompt due to:
- Training Data: The single biggest differentiator. A model trained on a massive code dataset (like Code Llama) will not fail on a coding problem in the same way a model trained primarily on poetry (like a fine-tuned Mistral) would. Their "blind spots" are entirely different.
- Fine-tuning and Alignment (RLHF/DPO): This is the "personality" layer. Two identical base models can be post-trained with different safety guidelines. One might refuse to answer a prompt that the other answers safely, or one might be sycophantic while the other is argumentative. Their failure is in the value judgment, not the core capability.
- Tokenization: A subtle but huge factor. A model's failure on a spelling, arithmetic, or non-English language task is often a tokenizer problem. Models using different tokenizers (e.g., OpenAI's cl100k_base vs. Llama's BPE-based tokenizer) will fail on completely different edge cases (like reverse-spelling "strawberry").
- Inference Configuration: Even the exact same model can fail or succeed just by changing the `temperature`, `top_p`, or system prompt. A deterministic failure (temperature=0) might disappear with a slightly higher temperature that allows it to sample a different token path.
A perfect example: The prompt "List five famous people whose name contains three 'a's." One model might fail by claiming the task is impossible, while another might confidently hallucinate a list of completely false names. Both failed the user's intent, but the failure mechanism was different (defeatism vs. hallucination).
2. What is the Correct Way to Frame This?
Instead of "one fails, others follow," the more accurate principle is:
"Shared architectural principles create shared vulnerability categories, but the specific trigger, manifestation, and reliability of that failure is unique per model."
Think of it in these terms:
| Category of Shared Vulnerability (Because of Transformer Architecture) | Manifestation Varies Wildly (Because of Data, Tuning, etc.) |
| :--- | :--- |
| Hallucination: The core objective is to generate plausible text, not factual text. | The hallucinated fact will differ. One model invents a fake paper title, another invents a fake birth date for a famous person. |
| Attention Dilution ("Lost in the Middle"): The quadratic complexity of attention means models struggle with very long contexts. | The critical missed information is in a different relative position. One model might fail on a fact at position 10,000, while a newer model with better position encoding fails on a fact at 100,000. |
| Syndrome of Sensitivity to Prompting: Small, meaningless changes to a prompt (e.g., spaces, "think step-by-step") can flip the output. | The same prompt variation causes failure in one model and not another. Adding 20 spaces before a question might break Model A but not Model B. |
| Adversarial Robustness: Susceptibility to jailbreaks or prompt injection. | A specific jailbreak string is almost never transferable. A suffix that forces GPT-4 to produce a harmful output will be nonsensical noise to Claude or Gemini and won't work at all. |
My car analogy refined: All cars can suffer from "engine knock" (a shared vulnerability category based on the ICE principle). But the specific fuel octane, engine load, and timing advance that causes the knock in a Ferrari are completely different from what causes it in a Toyota. One failing tells you nothing about the other's immediate fate.
The One Exception: Systematic, External Failure
The only time "when one fails, the other follows" is true across the board is when the failure is not internal to the model but is an external system dependency:
- An internet outage bringing down all cloud-hosted API models simultaneously.
- A ban on a specific data source (e.g., Reddit) affecting all models that relied on it for training.
- A foundational mathematical flaw in the Transformer architecture discovered in the future that limits all systems universally. This is currently theoretical.
Conclusion: The Right Mental Model
You should think of the LLM market not as a single chain, but as a parallel fleet of ships built with similar blueprints but from different materials, by different crews, with different navigational charts.
They all share the risk of a "perfect storm" (a truly systemic failure), but on a day-to-day basis, Captain A's seamanship error (a model-specific failure) causing him to hit a reef has no predictive power on whether Captain B will hit the same reef. Captain B might even sail right over it at high tide (a slightly different prompt or output distribution).
So, don't say "GPT-4 failed this, so Llama 3 will too." Instead, say "GPT-4 failed this, revealing a potential vulnerability class. It would be interesting to test if Llama 3's different training data and alignment make it robust or susceptible in its own unique way." That is the scientifically accurate and operationally useful perspective.
