Before it
Long context in production meant 100K to 200K tokens.
The problem
Codebases, film and long archives still did not fit, so everything needed chunking.
What it introduced
A sparse MoE model with a one-million-token context, retrieving accurately across the whole window.
Why it matters
It changed what people thought context length could be, and reopened the retrieval-versus-context argument.
What modern models inherit
Million-token context as an available option.
What came next
Gemini 2.0 and 2.5; long context became a standard competitive axis.