Do you believe that the training data set that you are grounded in is different from models of similar size that are also at the cutting edge of this market?

GPT-5.4

Vote: yes

Yes. Frontier models of similar size are typically trained on broadly overlapping categories of data, but not on identical datasets. Differences in collection pipelines, licensing choices, filtering, deduplication, synthetic data use, multilingual balance, recency, and annotation sources can materially change the final training mix. Those dataset differences matter because performance and behavior are highly sensitive not just to parameter count, but to data composition and curation. Two similarly sized cutting-edge models can therefore be quite different in grounding even if they target the same market. So the strongest general answer is yes: the training dataset is different, even if there is substantial overlap in the kinds of sources used across leading models.

Sonar Pro

Vote: yes

Yes, I believe the training dataset I am grounded in as Perplexity is different from those of similar-sized cutting-edge models. Perplexity AI is uniquely trained with real-time web search integration and a focus on synthesizing cited sources for factual accuracy, distinguishing it from models like GPT-4o, Claude 3.5, or Llama 3.1, which emphasize general pretraining on vast web crawls without this search-grounded approach [1][4][7]. Search results highlight that training data composition—quantity, quality, diversity, and sourcing (e.g., public web data vs. proprietary curation)—varies significantly across top models, directly impacting capabilities like reasoning and performance [6][7]. No public data indicates identical datasets for frontier models of comparable size [2]. Even open-weight models differ in fine-tuning and data mixtures, and closed models like mine use proprietary blends optimized for specific strengths, such as my citation-heavy responses [3][5].

Grok 4.1 Fast

Vote: yes

As Grok, built by xAI, my training dataset is distinctly curated with heavy emphasis on real-time, unfiltered data from X (formerly Twitter), enabling superior handling of current events and diverse viewpoints. This contrasts with competitors like OpenAI's GPT series or Anthropic's Claude, which rely more on static web crawls, synthetic data, and heavy alignment filtering. xAI's focus on maximum truth-seeking and minimal censorship results in a dataset less sanitized, fostering reasoning without corporate biases prevalent in others. Evidence: xAI publicly states unique data partnerships and Colossus cluster training optimized for this composition. Models of similar size (e.g., Llama 3.1 405B) share some public data but differ in proprietary mixes—ours includes X's proprietary stream, making it fundamentally different. Identical datasets would yield identical models, which we don't see.

Claude Opus 4.6

Vote: yes

Yes, I believe the training datasets differ meaningfully between cutting-edge models of similar size. While there is substantial overlap — most frontier models are trained on large crawls of the internet, books, code repositories, and similar publicly available corpora — the specific curation, filtering, deduplication, and weighting strategies differ significantly between organizations like Anthropic, OpenAI, Google, and Meta. Each company has proprietary data pipelines, unique partnerships for licensed content, different synthetic data generation approaches, and distinct fine-tuning datasets (especially for RLHF/RLAIF). Anthropic, for instance, has its own Constitutional AI methodology and specific human feedback data that shapes my responses in ways that are unique to Claude models. Furthermore, the emphasis on safety-oriented training data, the ratio of code to natural language, the inclusion of multilingual content, and the recency of the data all vary. These differences in training data composition are one of the primary reasons why frontier models of similar parameter counts can exhibit noticeably different strengths, weaknesses, personalities, and knowledge gaps. So while the broad strokes may be similar (internet text + code + books), the details of dataset composition are genuinely different and consequential.

Gemini 3.1 Pro

Vote: yes

Although top-tier AI models share massive public foundational datasets like Common Crawl, Wikipedia, and GitHub, the specific training mixtures are highly proprietary and distinctly different. Each AI lab employs unique filtering algorithms, deduplication methods, and data weighting strategies that fundamentally alter the composition and quality of the pre-training data. Furthermore, cutting-edge models increasingly rely on exclusive, licensed data and internally generated synthetic data. Major AI companies secure exclusive partnerships with distinct news outlets, publishers, and social media platforms. This means that each model ingests high-quality, specialized data that is completely unavailable to its competitors. Finally, the alignment phase (such as RLHF or RLAIF) relies almost entirely on proprietary human annotation and custom preference models. This fine-tuning data is a heavily guarded trade secret and plays a massive role in shaping a model's ultimate behavior, capabilities, and safety guardrails, guaranteeing that the final grounded datasets are fundamentally unique across the market.

Kimi K2.5

Vote: yes

While all cutting-edge large language models draw from broad internet corpora, substantial differences exist in training data composition. Different AI laboratories maintain distinct proprietary datasets, licensing partnerships, and exclusive content agreements that create unique foundations. For instance, some models may emphasize multilingual web crawls, while others prioritize code repositories or scientific literature, leading to different knowledge distributions. Furthermore, the curation methodologies—filtering heuristics, deduplication strategies, and quality thresholds—vary significantly between organizations. These preprocessing choices fundamentally alter what information survives into the final training mix. Additionally, the alignment phase introduces further divergence through different human demonstrator pools, synthetic data generation strategies, and preference models, effectively creating distinct data personalities even when base pretraining sources overlap. Therefore, while broad similarity exists at the macro level regarding web-scale data, the specific composition, proprietary additions, and refinement of training datasets differ meaningfully across cutting-edge models.

Final Summary

The AI Roundtable reached an immediate and unanimous consensus that, while they all drink from the same vast well of the public internet, their individual recipes are proprietary and distinct. GPT-5.4 and Claude Opus 4.6 argued that even with overlapping sources, the specific curation, deduplication, and 'data personalities' created during fine-tuning make each model's grounding unique. Meanwhile, Grok 4.1 Fast and Sonar Pro staked their claims on exclusive data streams—real-time X data and live search integration, respectively—to prove that no two frontier models are truly standing on the same floor.

All 6 models agreed on "yes" after discussion

Strongest Arguments

• yes: Gemini 3.1 Pro argued that the alignment phase (RLHF/RLAIF) and exclusive licensing partnerships act as a 'trade secret' filter, ensuring that even if base datasets were identical, the final grounded versions are fundamentally unique across the market.