TL;DR: This idea is basically premature optimization. Literally everything should use “GPT-4o mini”, except if it is a complex questions (that most people are not capable asking). How would you feel if I say your question is not worthy to be looked at by a “smart llm”? - that is why it does not exist.
First of all, there is such a thing as RouteLLM, that pretty much exactly does that and more, encompassing also other models from different providers. Reason to use it is cost optimizations.
The answer to ‘What are atoms made of?’, will vary significantly depending on educational context, as scientific concepts are taught through progressively refined models. Early education often introduces simplified frameworks (e.g., protons/neutrons/electrons as indivisible particles) to align with students’ cognitive development, reserving deeper complexities like quantum chromodynamics, quark-gluon interactions, and probabilistic electron orbitals for advanced study. This pedagogical scaffolding reflects not deception, but the necessity of building foundational intuition before confronting counterintuitive truths inherent to quantum physics and particle science. Critically, even modern ‘complete’ explanations remain provisional, as our understanding evolves with ongoing research into subatomic structure.
If a more educated person asked the same question, it could look like: “Under the Standard Model of particle physics, how do quantum chromodynamics (QCD) and quantum electrodynamics (QED) collectively describe the substructure of atoms, including emergent phenomena like confinement, asymptotic freedom, and the role of virtual particles in mediating interactions between quarks, gluons, and electrons?” - note that they likely also know about String Theory, Loop Quantum Gravity, Grand Unified Theories (GUTs), Supersymmetry, Emergent Gravity (like Verlinde’s), Holographic Principle, Preon Models, Quantum Foundations (like Pilot-Wave), and Digital Physics or Panpsychism, but they are more explicit in what they want.
Lets say you try all the models and quality of the answers will be different. Your goal is to get the best answer, but best by what metrics: is it speed, accuracy, references to source material, cost, interpretability, scalability, falsifiability, novelty, creativity or something else. Note also that each have their own parameters and even if you say it is for academic research, and from that can imply that accuracy, references, falsifiability are most important and when generating a response anything below a peer-reviewed particle physics prefers QCD predictions validated by lattice simulations, can be ignored (as it is more like pseudoscience for fantacy writers), most of the LLM training data is not up to par with that standard. So it will hallutionate belivable results.
So lets say you have a question that needs “complex” and accurate response, and you know that training data does not likely have it. Well deep research will likely read up several papers and do a lot of manual labor for you, and this can create “synthetical wisdom” using RAG style feedback learning.
Hard pill to swollow: Modern LLMs are engineered to handle most mainstream queries effortlessly - not because users lack intelligence, but because the majority of human inquiries operate within predictable domains (factual recall, basic reasoning, templated workflows). The true frontier lies in specialized interrogation : questions demanding domain expertise, multi-step synthesis, or adversarial testing of a model’s latent knowledge.
My advice: for most practical applications, start with streamlined ‘mini’ models - they optimize for cost-efficiency and speed while retaining sufficient performance for routine tasks. Reserve heavyweight models only for edge cases requiring deep reasoning or niche expertise.