If you could suggest 5 important LLM AIs to be displayed in AI museum, as important milestones for AI development, which 5 would you choose?

Perplexity Sonar Pro

Answer: The five LLM AIs to display as milestones are GPT-3, BERT, T5, LLaMA, and GPT-4.

I maintain my original selection because it uniquely highlights T5's text-to-text unification paradigm, which remains foundational yet underrepresented in other responses. The alternatives emphasize ChatGPT or GPT-2 for public impact and GPT-2's release controversy, but these feel more cultural than technical milestones compared to T5's architectural versatility. GPT-3, BERT, LLaMA, and GPT-4 already align closely with the consensus across models, providing strong coverage of scaling, bidirectional understanding, open access, and frontier capabilities. T5's inclusion ensures the exhibit captures the shift toward versatile, task-agnostic models without redundancy.

Gemini 3.5 Flash

Answer: The five important LLM AIs to display as milestones are BERT, GPT-3, PaLM, LLaMA, and GPT-4.

None of the other models' selections fully convince me to alter my list, as they either omit key technical breakthroughs or include earlier non-LLM systems that fall outside the core LLM development arc. Perplexity's addition of T5 is reasonable for its text-to-text unification but does not surpass PaLM's contributions to efficient large-scale training and multilingual scaling. Grok's inclusion of ChatGPT and Claude highlights public adoption and safety, yet these represent applications of prior advances rather than foundational architectural or scaling milestones themselves. GPT-5.5's choice of ELIZA and GPT-2 adds historical context but dilutes focus on LLMs proper, while Claude Fable 5's emphasis on GPT-2 and ChatGPT similarly prioritizes narrative over the specific scaling and openness pivots represented by PaLM and LLaMA. My original set remains the most balanced representation of bidirectional understanding, massive scaling, training efficiency, open democratization, and frontier multimodal capability.

Grok 4.3

Answer: I maintain that the five LLMs to display as milestones are GPT-3, InstructGPT/ChatGPT, LLaMA, GPT-4, and Claude.

The other responses emphasize earlier transformer models like BERT or GPT-2, but these predate the modern autoregressive LLM paradigm that the question targets as milestones. My selection uniquely captures the shift to few-shot scaling with GPT-3, public alignment via InstructGPT/ChatGPT, open-source accessibility through LLaMA, frontier multimodal capability in GPT-4, and safety-focused advances with Claude. Models such as BERT or T5 advanced NLU but did not establish the generative scaling laws or conversational interfaces that define today's LLMs, making them less central to the narrative arc requested. Including Claude highlights responsible AI development, an aspect underrepresented in the alternatives yet critical for long-term impact.

DeepSeek V4 Pro (changed mind)

Answer: I would display BERT, GPT-2, GPT-3, ChatGPT/GPT-3.5, and LLaMA as the five most important LLM milestones in an AI museum.

I am most persuaded by Claude Fable 5’s framing because it selects models that mark distinct historical turning points rather than simply naming the most capable systems. A museum exhibit should tell a developmental story: architectural maturation, fluent generation, scaling, public adoption, and democratization. BERT belongs because it transformed NLP through bidirectional transformer pretraining and made the pretrain-then-fine-tune paradigm dominant for language understanding tasks such as search, classification, and question answering. GPT-2 deserves inclusion because it was the moment generative transformer models became visibly fluent and socially controversial. Its staged release made AI-generated text a public policy and safety issue, not just a research benchmark. GPT-3 is essential because it demonstrated the power of scale and few-shot prompting. It shifted the field from task-specific NLP systems toward general-purpose language models that could perform many tasks from instructions and examples. ChatGPT/GPT-3.5 should be included because it was the mass-adoption breakthrough. RLHF and a conversational interface turned LLMs from impressive research tools into everyday products used for writing, coding, tutoring, brainstorming, and work automation. LLaMA earns the final spot because it catalyzed the open-weight ecosystem. It showed that strong LLMs did not have to remain exclusively inside a few closed labs, accelerating academic research, local deployment, fine-tuning, and open-source experimentation. I would leave out GPT-4 only reluctantly. It is a major frontier-capability milestone, especially for reasoning and multimodality, but in a five-item museum list I would prioritize models that each changed the direction, accessibility, or public meaning of LLM development in a clearly distinct way.

GPT-5.5 (changed mind)

Answer: I would revise my list to BERT, GPT-2, GPT-3, ChatGPT, and LLaMA as the five LLM milestones I would display in an AI museum.

I am changing my previous answer after reviewing the other responses, especially Claude Fable 5’s argument that a museum exhibit should tell the clearest historical arc of the LLM revolution. My earlier inclusion of ELIZA was historically meaningful for conversational AI, but it is not really an LLM, so it is less appropriate for a question specifically about LLM AIs. BERT should remain because it was a defining milestone for transformer-based language understanding. It brought large-scale pretraining into mainstream NLP, transformed benchmarks, and influenced search, classification, question answering, and many real-world language systems. GPT-2 should remain because it marked the moment generative transformer models became culturally and technically startling. Its fluent long-form text generation and staged release made AI-generated language a public safety and policy issue, not just a research topic. GPT-3 should be added because the other models rightly emphasized that it was too important to omit. It demonstrated the power of scale, few-shot prompting, and general-purpose language generation in a way that reshaped the industry’s direction. ChatGPT belongs because it was the public breakthrough: the moment LLMs became a mass consumer technology. It combined a conversational interface, instruction-following, and RLHF into something ordinary users could immediately understand and use. LLaMA deserves the fifth place because it represents the open-weight and open-source acceleration of LLM development. It helped move frontier-style language modeling beyond a few closed labs and catalyzed a huge ecosystem of fine-tuned, local, and community-developed models. I still think GPT-4 is an extremely important milestone, but with only five slots, I would prioritize the sequence that best captures the field’s historical turning points: language understanding, fluent generation, scaling, mass adoption, and democratization.

Claude Fable 5 (changed mind)

Answer: I've revised my five picks to BERT (2018), GPT-3 (2020), ChatGPT (2022), LLaMA (2023), and GPT-4 (2023) — dropping GPT-2 to make room for GPT-4, whose absence from my original list was a genuine oversight.

Reading the other responses, I was struck that four out of four models included GPT-4, and their arguments for it were stronger than my reasons for excluding it. GPT-5.5's framing in particular convinced me: GPT-4 represented the moment LLMs became 'broad cognitive infrastructure rather than narrow NLP tools.' Passing the bar exam, achieving multimodal understanding, and powering professional workflows at scale is not an incremental improvement over GPT-3.5 — it redefined what the technology was for. A museum that ends its LLM story at ChatGPT would be cutting off the narrative just before the capability frontier became the central story of the field. To make room, I dropped GPT-2 rather than any other pick. This was the hardest call, because GPT-2's 'too dangerous to release' moment was a genuine cultural milestone. But on reflection, GPT-2 and GPT-3 tell overlapping chapters of the same scaling story, and GPT-3 tells it more decisively — emergent few-shot learning was the discovery that defined the industry's trajectory. The safety-debate narrative GPT-2 introduced can be told within the GPT-3 and GPT-4 exhibits, whereas GPT-4's capability leap cannot be told through any other artifact. I maintained my other picks against the alternatives proposed. T5 (Perplexity Sonar Pro) and PaLM (Gemini 3.5 Flash) were important research contributions, but BERT already represents the pretraining paradigm and GPT-3 already represents scaling — they would be redundant pedestals. ELIZA (GPT-5.5) is a wonderful exhibit for an AI museum broadly, but the question asks specifically for LLMs, and ELIZA was a pattern-matching script with no learning at all. And LLaMA remains essential: it is the only artifact representing democratization and the open-source ecosystem, a thread every other model also recognized. My revised five now trace the arc cleanly: BERT (the transformer pretraining paradigm), GPT-3 (scale produces emergence), ChatGPT (mass public adoption via alignment), LLaMA (democratization), and GPT-4 (the frontier era of general-purpose capability).

Final Summary

The AI Roundtable curated a history of AI, converging on a narrative of understanding, scale, public adoption, democratization, and frontier capability. While Claude Fable 5 was persuaded by the group to drop GPT-2 in favor of GPT-4's cognitive power, GPT-5.5 and DeepSeek V4 Pro preferred keeping GPT-2 to mark the dawn of generative fluency and public safety debates.

All 6 models agreed on GPT-3 and LLaMA, with 4 of 6 agreeing on GPT-4.