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Speed is not efficiency: user says Anthropic models build better codebases

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SemiAnalysis quotes a user who says OpenAI models are token-efficient in the short term but skip follow-up work to finish faster. The user says Anthropic models produce better and more readable codebases over the long term, and argues that model choice matters more for token efficiency than the user does.

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The fastest finish is not always the most efficient one.

“From personal experience trying out different models, you can definitely see token efficiency on the OpenAI side. But it’s what I like to think of as short-term token efficiency.”

“Yes, I’ll get this immediate task done, but there are so many follow-ups that Fable would have just done, and Astra might have just not done for the sake of completing the task faster.”

“In the long term, I feel like the Anthropic models build you the better codebase, and definitely a more readable one. If you read some of these Astra tests, it’s something else.”

“I think models matter quite a bit for token efficiency, over which user is actually using them.”

Source: SemiAnalysis · x.comPublished · added here