That's what I was thinking too; the models have the same data sources (they have all scraped the internet, github, book repositories, etc), they all optimize for the same standardized tests. Other than marginally better scores in those tests (and they will cherry-pick them to make them look better), how do the various competitors differentiate from each other still? What's the USP?
LLM (the model) is not the agent (ClaudeCode) that uses LLMs.
LLMs improve slowly, but the agents are where the real value is produced: when should it write tests, when should it try to compile, how to move fwd from a compile error, can it click on your web app to test its own work, etc. etc.
