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> it's them trying to push the models to burn less compute

I'm curious, how does using more tokens save compute?

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productivity (tokens per second per hardware unit) increases at the cost of output quality, but the price remains the same.

both Anthropic and OpenAI quantize their models a few weeks after release. they'd never admit it out loud, but it's more or less common knowledge now. no one has enough compute.

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Pretty bold claim - you have a source for that?
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There is no evidence TMK that the accuracy the models change due to release cycles or capacity issues. Only latency. Both Anthropic and OpenAI have stated they don't do any inference compute shenanigans due to load or post model release optimization.

Tons of conspiracy theories and accusations.

I've never seen any compelling studies(or raw data even) to back any of it up.

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Do you have a source for that claim?
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my source is that people have been noticing this since GPT4 days.

https://arxiv.org/pdf/2307.09009

but of course, this isn't a written statement by a corporate spokespersyn. I don't think that breweries make such statements when they water their beer either.

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I think that the idea is each action uses more tokens, which means that users hit their limit sooner, and are consequently unable to burn more compute.
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I'm 99.9% sure Opus 4.7 is a smaller model than 4.6.

Too many signs between the sudden jump in TPS (biggest smoking gun for me), new tokenenizer, commentary about Project Mythos from Ant employees, etc.

It looks like their new Sonnet was good enough to be labeled Opus and their new Opus was good enough to be labeled Mythos.

They'll probably continue post-training and release a more polished version as Opus 5

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It could be the adaptive reasoning
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If you've not seen Common People Black Mirror episode I strongly recommend it.

The only misprediction it makes is that AI is creating the brain dead user base...

You have to hook your customers before you reel them in!

https://www.netflix.com/gb/title/70264888?s=a&trkid=13747225...

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