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That's what you get when you use speculative decoding and focus / overfit the draft model on coding. Then when the answer is out of distribution for the draft model, you get increased token rejections by the main model and throughput suffers. This probably still makes sense for them if they expect a lot of their load will come from claude code and they need to make it economical.

I'm curious to know if Anthropic mentions anywhere that they use speculative decoding. For OpenAI they do seem to use it based on this tweet [1].

[1] https://x.com/stevendcoffey/status/1853582548225683814

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