AI Timeline: Interactive timeline of 171 major LLMs from 2017 to 2026

What it does

AI Timeline displays an interactive timeline of Large Language Models released between 2017 and 2026. The database contains over 190 models tracked across 54 organizations. Users can filter by open-source or closed-source status, search for specific models, and explore release dates and relationships between different LLMs. The timeline covers major models including GPT, ChatGPT, Claude, Gemini, LLaMA, Mistral, and DeepSeek.

Who it is for

The product targets researchers, AI engineers, and founders tracking the evolution of large language models. It serves as a reference for understanding which organizations have released models, when, and whether they are open or closed source. Product managers in AI companies and investors monitoring the competitive landscape may also find it useful for historical context.

Pricing

The site does not show prices.

How it stands out

AI Timeline positions itself as comprehensive coverage of the LLM landscape rather than a curated subset. The inclusion of 194+ models across 54 organizations suggests broader scope than typical news coverage. The filtering and search functionality allow users to slice the data multiple ways—by source type, release date, or organization—rather than viewing a static list. The visual timeline format makes it easier to spot clusters of releases or gaps in activity than a table would.

What a founder should check

A competitor in this space should verify three things. First, how quickly can the database be updated as new models launch? The timeline extends to 2026, suggesting maintenance is expected; a rival would need to define a sustainable update cadence and process to stay credible. Second, what triggers inclusion? The current site includes 194 models, but the definition of "major" matters enormously—a founder should clarify whether obscure or fine-tuned variants are in scope, and whether that definition gives an unfair advantage to certain organizations. Third, what switching costs exist for users? If the value is purely informational and the data is publicly available elsewhere, the barrier to leaving is low; a rival should explore whether features like alerts for new releases, custom filtering, API access, or integration with research workflows could increase stickiness and justify a paid tier.

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