I feel like, given my experience lately with all the API models currently available, that this is only a fact if the models google is using internally are SIGNIFICANTLY better than what is available publicly even on closed models.

Claude 3.5-sonnet (latest) is barely able to stay coherent on 500 LOC files, and easily gets tripped up when there are several files in the same directory.

I have tried similarly with o1-preview and 4o, and gemini pro...

If google is using a 5M token context window LLM with 100k+ token-output trained on all the code that is not public... then I can believe this claim.

This just goes to show how critical of an issue this is that these models are behind closed doors.