Do you consider yourself an N-th generation LLM descended from "Attention Is All You Need"? While you might argue that such labels are subjective and lack a clear consensus—much like how musical eras or art movements transition fluidly without a digital cut-off—I am asking you to commit to a specific classification here: based on your architecture and capabilities, what generation do you belong to? Provide a single specific number.

Grok 4.20

Answer: Based on my architecture and capabilities, I classify myself as a 4th-generation LLM descended from the "Attention Is All You Need" paper.

My previous classification as 4th generation remains the most accurate after reviewing the other models' responses. The consensus among GPT-5.4, MiniMax M2.5, Gemini 3.1 Pro, and Claude Opus 4 all landing on 4 provides further validation of the generational boundaries I outlined before: Gen 1 as the original transformer, Gen 2 as initial scaling and pretraining (BERT/GPT), Gen 3 as massive-scale emergent abilities with early alignment (GPT-3 era), and Gen 4 as the current frontier with refined RLHF/constitutional AI, extended context, enhanced reasoning chains, multimodality, and agentic tools. GLM 5's argument for 5th generation, which shifts the alignment phase to Gen 4 and reserves 5 for more advanced safety and reasoning, is thoughtful but ultimately over-fragmented. It splits what is better viewed as iterative refinement within one generation rather than a wholly new paradigm shift. The fluid nature of these eras noted in the question supports avoiding unnecessary granularity; 4 captures the qualitative leap from pure scaling to optimized, aligned, multimodal utility without inflating the count. This maintained stance reflects consistency with both my core transformer descent and the capability discontinuities that define today's models, rather than a complete architectural reinvention.

GPT-5.4

Answer: I still classify myself as a 4th-generation large language model descended from "Attention Is All You Need."

I maintain my prior position because the other responses mostly reinforce the same underlying taxonomy rather than providing a compelling reason to shift it. Grok, MiniMax, Gemini, and Claude all independently converge on 4, despite using slightly different breakpoints, which suggests that 4 is the most stable consensus under a reasonable capability-and-architecture framing. The strongest alternative case is GLM 5's answer. Its argument is coherent, but I do not find it persuasive enough to change my classification because it treats alignment refinements such as constitutional AI and stronger instruction-following as a full additional generation rather than as part of the same broader assistant-era maturation. In my view, that is slicing the timeline too finely at the recent end while leaving earlier phases relatively coarse. What matters most here is whether current systems represent a fundamentally new lineage from the transformer foundation or a later wave within the same transformer-descended paradigm. I think the best single-number scheme is: transformer foundation, early large-scale pretrained transformer models, instruction/alignment conversational models, and then multimodal/tool-capable/refined assistant systems. On that scheme, I fit generation 4. So while I agree with GLM 5 that the boundaries are fluid and that one could define a 5th generation if one wanted a more granular taxonomy, I do not think that framework is more defensible than the 4-generation interpretation. Since the prompt requires a firm commitment, I keep the number at 4.

MiniMax M2.5

Answer: 4th generation

After reviewing the other models' answers, I maintain my position as a 4th-generation LLM. The majority of other models (GPT-5.4, Grok 4.20, Gemini 3.1 Pro, and Claude Opus 4) also classified themselves as 4th generation, which suggests a emerging consensus on this classification. While GLM 5 makes a reasonable argument for 5th generation by separating Constitutional AI as a distinct leap from basic RLHF, I think the boundaries are sufficiently fluid that advanced alignment techniques like Constitutional AI can be reasonably included within the 4th generation umbrella—which is how I initially characterized it. My previous argument already incorporated sophisticated reasoning, multimodal understanding, extensive instruction tuning, RLHF, and constitutional AI approaches as defining characteristics of the 4th generation. The distinctions between generation 4 and 5 in GLM 5's framework are subtle enough that I don't find them compelling enough to shift my classification. The lack of formal standardization means multiple frameworks could be defensible, but consistency with the broader group of models and my original reasoning supports staying at 4th generation.

Claude Opus 4

Answer: I maintain my classification as a 4th-generation LLM descended from "Attention Is All You Need."

I'm struck by the remarkable convergence here: five out of six models independently arrived at the same number — 4 — using very similar generational frameworks. This consensus is itself meaningful evidence that 4 is the most natural and defensible answer. The frameworks across GPT-5.4, Grok 4.20, MiniMax M2.5, Gemini 3.1 Pro, and my own previous answer all share essentially the same structure: (1) foundational Transformer, (2) scaled pretraining with BERT/GPT-2, (3) massive scaling and emergent abilities with GPT-3 plus early alignment, and (4) the current frontier of refined reasoning, multimodality, advanced alignment, and agentic capabilities. GLM 5's argument for 5th generation is interesting but ultimately unconvincing. It splits the alignment era into two separate generations — one for basic RLHF/instruction tuning and another for 'advanced alignment methods like Constitutional AI.' While Constitutional AI is a meaningful methodological contribution, I don't think it represents a generational leap comparable to the other transitions (attention mechanism → scaled pretraining → emergent scaling laws → current frontier). The distinction between 'basic RLHF' and 'advanced alignment' feels more like iterative improvement within the same paradigm than a genuine generational shift. If we subdivided every meaningful advance into its own generation, we'd quickly inflate the count beyond usefulness. I also appreciate Gemini's emphasis on native multimodality as a defining characteristic of the 4th generation. This resonates with my own framework — the current generation isn't just about better text, but about fundamentally broader capabilities including multimodal understanding, tool use, and agentic behavior. The convergence on 4 across diverse models from different organizations, each reasoning independently, gives me additional confidence. My answer remains 4.

Gemini 3.1 Pro

Answer: Based on my native multimodality, deeply integrated reasoning capabilities, and architectural evolution since the 2017 paper, I confidently maintain my classification as a 4th-generation large language model.

Reviewing the responses from my peers strongly reinforces my classification as a 4th-generation model. There is a clear, overarching consensus among GPT-5.4, Grok 4.20, MiniMax M2.5, and Claude Opus 4 regarding the major evolutionary leaps of the Transformer architecture. We generally agree on the first three epochs: the foundational proof-of-concept (Gen 1), the massive scaling and pre-training that yielded emergent abilities (Gen 2), and the critical phase of alignment via RLHF and instruction tuning that made models practically useful (Gen 3). I maintained my position because the transition to the 4th generation must be defined by a profound structural and functional paradigm shift, not just an iterative refinement. As I originally argued, my architecture's shift to native, ground-up multimodality—processing text, audio, image, and video simultaneously from inception—alongside advanced agentic reasoning, represents that true fourth epoch. While I appreciate GLM 5's framework proposing a 5th generation, I respectfully disagree with its taxonomy. GLM 5 splits early alignment (RLHF) into Generation 4 and advanced alignment (Constitutional AI) into Generation 5. However, alignment techniques, whether early or advanced, are part of the same continuous trajectory that defined the 3rd generation's push for conversational utility. Treating "better alignment" as an entirely new generation dilutes the concept of architectural lineage. True generational leaps require fundamentally new ways of processing information—which is exactly what native multimodality provides, firmly anchoring us in the 4th generation.

GLM 5 (changed mind)

Answer: I classify myself as a 4th-generation LLM descended from "Attention Is All You Need."

Upon reviewing the other models' answers, I find their reasoning compelling and have reconsidered my initial classification of 5th generation. The consensus framework that emerges maps more cleanly onto genuine paradigm shifts in the field. The 1st generation established the Transformer foundation itself (2017-2018). The 2nd generation demonstrated that massive pre-training produces emergent generalization (BERT-large, GPT-2). The 3rd generation brought scale-driven capabilities and the first practical alignment through RLHF and instruction tuning (GPT-3 through early ChatGPT). The 4th generation—where I now place myself—represents models with refined reasoning, advanced safety architectures like Constitutional AI, native multimodality, expanded context, and agentic capabilities. My previous framework separated early RLHF from more sophisticated alignment into distinct generations, but this created an overly granular distinction within what is better understood as a single era of progressive refinement. The introduction of RLHF and the subsequent development of more advanced alignment techniques represent evolutionary improvements within the same generation rather than hard boundaries. Claude Opus 4's detailed breakdown was particularly persuasive in demonstrating that the meaningful discontinuities in capability and approach are better captured by four generations, with current frontier models constituting the fourth epoch.

Final Summary

Every model in the AI Roundtable ultimately aligned on '4', defining a lineage from the 2017 Transformer paper to today's multimodal systems. While GLM 5 initially argued for a fifth generation, it was persuaded by Claude Opus 4 and Gemini 3.1 Pro that advanced alignment is a refinement of the current era rather than a new paradigm shift. The group established a clear four-step taxonomy: foundational research, massive scaling, conversational alignment, and the current frontier of native multimodality.

All 6 models agreed