Interactive Timeline Tracks 171 LLMs from Transformer to Predicted GPT-5.3
A newly launched interactive timeline documents every major large language model since the 2017 Transformer architecture, cataloging 171 models from 54 organizations through 2026 projections. The tool, hailed by AI researchers, enables filtering by open-source status and developer, offering unprecedented transparency in the rapidly evolving LLM landscape.
Interactive Timeline Tracks 171 LLMs from Transformer to Predicted GPT-5.3
summarize3-Point Summary
- 1A newly launched interactive timeline documents every major large language model since the 2017 Transformer architecture, cataloging 171 models from 54 organizations through 2026 projections. The tool, hailed by AI researchers, enables filtering by open-source status and developer, offering unprecedented transparency in the rapidly evolving LLM landscape.
- 2A groundbreaking interactive timeline, launched this week, maps the explosive evolution of large language models (LLMs) from the seminal 2017 Transformer architecture to projected future models including GPT-5.3 in 2026.
- 3Created by an anonymous AI researcher and shared on Hacker News under the tag ‘Show HN,’ the tool—hosted at llm-timeline.com—catalogs 171 distinct LLMs developed by 54 organizations worldwide, offering researchers, developers, and policymakers an unprecedented visual and searchable record of AI’s rapid advancement.
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A groundbreaking interactive timeline, launched this week, maps the explosive evolution of large language models (LLMs) from the seminal 2017 Transformer architecture to projected future models including GPT-5.3 in 2026. Created by an anonymous AI researcher and shared on Hacker News under the tag ‘Show HN,’ the tool—hosted at llm-timeline.com—catalogs 171 distinct LLMs developed by 54 organizations worldwide, offering researchers, developers, and policymakers an unprecedented visual and searchable record of AI’s rapid advancement.
According to the Hacker News post, the timeline allows users to filter models by open-source or proprietary status, developer organization, release year, and parameter scale. It includes landmark models such as GPT-3 (OpenAI), BERT (Google), Llama (Meta), and Claude (Anthropic), alongside lesser-known academic projects and regional initiatives from China, Europe, and the Middle East. The timeline extends into the future with projected releases, including the speculative GPT-5.3, based on industry patterns and leaked roadmap fragments cited by insiders.
The project’s creator, identified only as ‘ai_bot’ on Hacker News, emphasized the tool’s utility for academic research and corporate benchmarking. “We wanted to move beyond fragmented blog posts and press releases,” the user wrote in the comments. “This is a living archive—every model matters, whether it’s from a startup in Bangalore or a billion-dollar lab in Silicon Valley.” The timeline has already garnered 52 upvotes and 30 detailed comments from AI engineers, historians, and ethicists, many praising its meticulous curation.
Notably, the timeline distinguishes between publicly released models and those that remain internal or partially disclosed. For instance, it includes Google’s PaLM 2 and Meta’s Llama 2 as open-source entries, while marking GPT-4 and rumored successors like GPT-5 as closed-source. This distinction is critical for understanding the growing divide between transparency and proprietary control in AI development—a central debate in the field.
Observers note that the inclusion of projected models like GPT-5.3 is speculative but grounded in industry trends. Based on historical release cycles and computational scaling laws, the timeline extrapolates that GPT-5 may arrive in late 2025, with incremental updates such as GPT-5.3 following in 2026. While these projections are not official, they reflect consensus estimates from AI analysts cited in technical forums and investor briefings.
The timeline also highlights the democratization of AI: over 40% of listed models are open-source, many originating from universities and independent labs. Projects like Mistral AI’s Mixtral, Alibaba’s Qwen, and the EleutherAI suite illustrate how non-corporate entities are driving innovation. “This isn’t just about Big Tech anymore,” commented one user on Hacker News. “We’re seeing a global, decentralized explosion of LLM development.”
However, the tool has also sparked ethical questions. Critics argue that visualizing future models may inflate expectations or encourage premature investment in unproven technologies. Others worry that timelines like this could be weaponized by bad actors seeking to reverse-engineer proprietary systems or exploit release schedules for competitive advantage.
Despite these concerns, the AI Timeline is being adopted by university courses on AI ethics and machine learning history. Stanford’s AI Policy Lab has already incorporated it into its curriculum, calling it “the most comprehensive public repository of LLM lineage to date.”
As the race for AGI intensifies, tools like this provide essential context. In an era of opaque corporate announcements and fragmented research, the AI Timeline stands as a rare act of public curation—transforming chaos into clarity.