Yes, 100% agree. Without more details on how they got their data, it's very hard to understand why they don't value DS Flash 4.1 as much as pretty much every other analysis I've seen (and my own experience).

I see this take frequently; that coding does not require general intelligence. Perhaps I am in the minority, but rarely do I find myself in coding situations that are completely divorced from other subject areas. The code is modeling or interacting with the world, either through business processes, or (in my case) simulating the physical world, and I need the agent to have some working knowledge of not just the code, but also physics, materials, or social conventions. Are you all really able to just "write code" without dealing with all the externalities?

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How often you needed the coding AI know all the trivia about some metal band? Like who are the members, songs , years etc. You can't even be sure if the facts are correct . This are good for a generalist model where you say remember some scene from music video and the AI will find the video for you.

Sure some world knowledge like things are affected by gravity, how people function, how a society function I think is needed for a coding AI but not trivia.

How often do the cousins of Louis the 5th of France impact on coding decisions? I can't see the argument for a completely generalist model being best for _coding_ in all situations.

They probably don't. But some words acquire their full sense meaning from historical and-non-programming context.

Words that are or were fairly common in tech circles — "bailiwick", "gatekeeper", "daemon", "boundary", "infer", "method".

It's true to say that most of these words have some specific meanings in tech, but you don't necessarily want the LLM to have only those narrow meanings when you are talking to it. A broader grasp of the word means the LLM won't go down a specific pathway before understanding you. That broader grasp comes from non-programming understanding.

Its a good point that there is benefit to using general intelligence models to figure out what code to write and to write specs. Implementation plans as well as long as the general intelligence is also great at coding.

The Opus frontier model will spin up a cheaper Sonnet subagent to explore the code base. Similarly we can have a Pareto optimal subagent write most code or verify it.

They do, to the extent that general intelligence training is what gives you the ability to talk in broad natural language to a coding agent.

Words only have their meanings through general context.