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I am really interested in the future of Opus; is it going to be an absolute monster, and continue to be wildly expensive? Or is the leap from 4 -> 4.5 for it go
by steveklabnik 1y ago
I am really interested in the future of Opus; is it going to be an absolute monster, and continue to be wildly expensive? Or is the leap from 4 -> 4.5 for it going to be more modest.
- criemen 1y agoTechnically, they released Opus 4.1 a few weeks ago, so that alone hints at a smaller leap from 4.1 -> 4.5, compared to the leap from Sonnet 4 -> 4.5. That is, of course, if those version numbers represent anything but marketing, which I don't know.
- steveklabnik 1y agoI had forgotten that, given that Sonnet pretty much blows Opus out of the water these days. Yeah, given how multi-dimensional this stuff is, I assume it's supposed to indicate broad things, closer to marketing than anything objective. Still quite useful.
- mcintyre1994 1y agoBizarrely they already call Opus 4.1 “legacy brainstorming model”.
- dheera 1y agoI wonder what the next smaller model after Haiku will be called. "Claude Phrase"?
- steveklabnik 1y agoIt's interesting to think about various aspects of marketing the models, with ChatGPT going the "internal router" direction due to address the complexity of choosing. I'd never considered something smaller than Haiku to be needed, but I also rarely used Haiku in the first place...
- ACCount37 1y agoIf you're going smaller than Haiku, you might be at the point of using various cheap open models already. The small model would need some good killer features to justify the margins.
- Brendinooo 1y agoClaude Couplet
- u8080 1y agoClaude Banger
- dotancohen 1y agoIf they do come up with a tiny model tuned for generating conversion and code, I think that Claude Acronym would be a perfect name.
- entanglr 1y agoClaude Punchline
- fnordsensei 1y agoClaude Garden Path Sentence
- WalterSear 1y agoClaude from Nantucket
- senko 1y agoClaude Glyph. Smallest, fastest model yet, ideally suited for Bash oneliners and online comments.
- clbrmbr 1y agoClaude Koan
- devnullbrain 1y agoKWATZ!
- stavros 1y agoClaude Groan.
- grandpa 1y agoClaude Clause.
- chrisweekly 1y agoClaude Char
- Razengan 1y agoClaude .
- gwd 1y agoOpus disappeared for quite a while and then came back. Presumably they're always working on all three general sizes of models, and there's some combination of market need and model capabilities which determine if and when they release any given instance to the public.
- sharkjacobs 1y agoMy impression is that Sonnet and Haiku 4.5 are the same "base models" as Sonnet and Haiku 4, the improvements are from fine tuning on data generated by Opus. I'm a user who follows the space but doesn't actually develop or work on these models, so I don't actually know anything, but this seems like standard practice (using the biggest model to finetune smaller models) Certainly, GPT-4 Turbo was a smaller model than GPT-4, there's not really any other good explanation for why it's so much faster and cheaper. The explicit reason that OpenAI obfuscates reasoning tokens is to prevent competitors from training their own models on them.
- sharkjacobs 1y agoWhich is all to say that I think the reason they went from Opus 3 to Opus 4 is because there was no bigger model to fine tune Opus 3.5 with. And I would expect Opus 4 to be much the same.
- jascha_eng 1y agoBut sonnet 4.5 outperforms opus 4 on most benchmarks and tasks that can't be all that's to it
- sharkjacobs 1y agothat's not all there is to it, but I think that "the rest of it" is just additional fine tuning. Benchmarks are good fixed targets for fine tuning, and I think that Sonnet gets significantly more fine tuning than Opus. Sonnet has more users, which is a strategic reason to focus on it, and it's less expensive to fine tune, if API costs of the two models are an indicator.
- qudat 1y agoThese frontier model companies are bootstrapping their work by using models to improve models. It’s a mechanism to generate fake training data. The rationale is the teacher model is already vetted and aligned so it can reliably “mock” data. A little human data gets amplified.