What is AI Timeline?
AI Timeline is an interactive visual database that maps the development of large language models from 2017 onwards. The tool displays over 190 LLMs chronologically, covering major releases from OpenAI, Anthropic, Google, Meta, Mistral, and other organisations. You can explore when each model launched, see how different model families evolved, and understand the competitive landscape of LLM development. It's particularly useful for researchers, AI practitioners, and anyone trying to understand which models exist, when they were released, and how the field has progressed. The free version gives you access to the full timeline; premium features likely offer additional filtering, comparison tools, or detailed metadata about each model.
Key Features
Interactive timeline view
Scroll through LLM releases chronologically from 2017 to 2026 with visual positioning
Model filtering
Search and filter by organisation, release date, or model family to find specific models
Model details
Click on any LLM to see release date, key characteristics, and developer information
Comparison capability
View multiple models side-by-side to understand differences in release timing and positioning
Coverage breadth
Includes both closed-source models (GPT, Claude, Gemini) and open-source alternatives (LLaMA, Mistral, DeepSeek)
Pros & Cons
Advantages
- Saves time researching model releases and history; all major LLMs in one place
- Visual timeline format makes it easy to see trends in model development and release frequency
- Free access to comprehensive data with no account required
- Useful for understanding the competitive landscape and which models were available at specific points in time
Limitations
- Projections for 2025-2026 models are speculative and may not reflect actual releases
- Limited to factual data about release dates and basic model info; doesn't provide detailed performance comparisons or benchmarks
- May require occasional updates as new models are constantly released
Use Cases
Researching the evolution of LLMs for academic papers or industry reports
Choosing which historical model to study based on timeline and availability
Understanding which models were available at a particular date for backward compatibility checks
Training or onboarding team members on the landscape of AI model development
Tracking competitive releases from different AI organisations over time