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Claude Opus 4.7

anthropic.com

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Re: Claude Opus 4.7

#531

Post by agentifysh »

trueno wrote: post #455

noticing sharp uptick in "i switched to codex" replies lately. a "codex for everything" post flocking the front page on the day of the opus 4.7 release me and coworker just gave codex a 3 day pilot and it was not even close to the accuracy and ability to complete & problem solve through what we've been using claude for. are we being spammed? great. annoying. i clicked into this to read the differences and initial exp…

i think you are being needlessly paranoid here

openai doest offer affiliate marketing links

the reason you see lot of users switching to codex is for the dismal weekly usage you get from claude

what users care about is actual weekly usage , they dont care a model is a few points smarter , let us use the damn thing for actual work

only codex pro really offers that

Re: Claude Opus 4.7

#532

Post by gck1 »

jubilanti wrote: post #514
gck1 wrote: post #498

I've always seen people complaining about model getting dumber just before the new one drops and always though this was confirmation bias. But today, several hours before the 4.7 release, opus 4.6 was acting like it was sonnet 2 or something from that era of models. It didn't think at all, it was very verbose, extremely fast, and it was just... dumb. So now I believe everyone who says models do get nerfed without any…

> So my question is: what is the actual reason Anthropic lobotomizes the model when the new one is about to be dropped? You can only fit one version of a model in VRAM at a time. When you have a fixed compute capacity for staging and production, you can put all of that towards production most of the time. When you need to deploy to staging to run all the benchmarks and make sure everything works before deploying to p…

So if I understand it right, in order to free up VRAM space for a new one, model string in the api like `opus-4.6-YYYYMMDD` is not actually an identifier of the exact weight that is served, but more like ID of group of weights from heavily quantized to the real deal, but all cost the same to me?

How is this even legal?

Re: Claude Opus 4.7

#533

Post by ai_slop_hater »

JamesSwift wrote: post #460
simonw wrote: post #203

I'm finding the "adaptive thinking" thing very confusing, especially having written code against the previous thinking budget / thinking effort / etc modes: https://platform.claude.com/docs/en/build-with-claude/adapti... Also notable: 4.7 now defaults to NOT including a human-readable reasoning token summary in the output, you have to add "display": "summarized" to get that: https://platform.claude.com/docs/en/build-…

Its especially concerning / frustrating because boris’s reply to my bug report on opus being dumber was “we think adaptive thinking isnt working” and then thats the last I heard of it: https://news.ycombinator.com/item?id=47668520 Now disabling adaptive thinking plus increasing effort seem to be what has gotten me back to baseline performance but “our internal evals look good“ is not good enough right now for what ma…

This matches my experience as well, "adaptive thinking" chooses to not think when it should.

Re: Claude Opus 4.7

#534

Post by quadruple »

gck1 wrote: post #467
dgb23 wrote: post #337

Earlier quoted context omitted.

Don't look at "thinking" tokens. LLMs sometimes produce thinking tokens that are only vaguely related to the task if at all, then do the correct thing anyways.

Why does this comment appear every time someone complains about CoT becoming more and more inaccessible with Claude? I have entire processes built on top of summaries of CoT. They provide tremendous value and no, I don't care if "model still did the correct thing". Thinking blocks show me if model is confused, they show me what alternative paths existed. Besides, "correct thing" has a lot of meanings and decision by…

I agree. Ever since the release of R1, it's like every single American AI company has realized that they actually do not want to show CoT, and then separately that they cannot actually run CoT models profitably. Ever since then, we've seen everyone implement a very bad dynamic-reasoning system that makes you feel like an ass for even daring to ask the model for more than 12 tokens of thought.

Re: Claude Opus 4.7

#535

Post by dgb23 »

gck1 wrote: post #467
dgb23 wrote: post #337

Earlier quoted context omitted.

Don't look at "thinking" tokens. LLMs sometimes produce thinking tokens that are only vaguely related to the task if at all, then do the correct thing anyways.

Why does this comment appear every time someone complains about CoT becoming more and more inaccessible with Claude? I have entire processes built on top of summaries of CoT. They provide tremendous value and no, I don't care if "model still did the correct thing". Thinking blocks show me if model is confused, they show me what alternative paths existed. Besides, "correct thing" has a lot of meanings and decision by…

Didn't you notice that the stream is not coherent or noisy? Sometimes it goes from thought A to thought B then action C, but A was entirely unnecessary noise that had nothing to do with B and C. I also sometimes had signals in the thinking output that were red flags, or as you said it got confused, but then it didn't matter at all. Now I just never look at the thinking tokens anymore, because I got bamboozled too often.

Perhaps when you summarize it, then you might miss some of these or you're doing things differently otherwise.

Re: Claude Opus 4.7

#536

Post by taylorfinley »

gck1 wrote: post #498

I've always seen people complaining about model getting dumber just before the new one drops and always though this was confirmation bias. But today, several hours before the 4.7 release, opus 4.6 was acting like it was sonnet 2 or something from that era of models. It didn't think at all, it was very verbose, extremely fast, and it was just... dumb. So now I believe everyone who says models do get nerfed without any…

I've noticed this and thought about it as well, I have a few suspicions:

Theory 1: Some increasingly-large split of inference compute is moving over to serving the new model for internal users (or partners that are trialing the next models). This results in less compute but the same increasing demand for the previous model. Providers may respond by using quantizations or distillations, compressing k/v store, tweaking parameters, and/or changing system prompts to try to use fewer tokens.

Theory 2: Internal evals are obviously done using full strength models with internally-optimized system prompts. When models are shipped into production the system prompt will inherently need changes. Each time a problematic issue rises to the attention of the team, there is a solid chance it results in a new sentence or two added to the system prompt. These grow over time as bad shit happens with the model in the real world. But it doesn't even need to be a harmful case or bad bugged behavior of the model, even newer models with enhanced capabilities (e.g. mythos) may get protected against in prompts used in agent harnesses (CC) or as system prompts, resulting in a more and more complex system prompt. This has something like "cognitive burden" for the model, which diverges further and further from the eval.

Re: Claude Opus 4.7

#537

Post by Unbeliever69 »

replwoacause wrote: post #248
TIPSIO wrote: post #9

Quick everyone to your side projects. We have ~3 days of un-nerfed agentic coding again.

More like 2 hours considering these usage limits

I've been on 5x for a couple of months and the closest I've got to my weekly limits is 75%. I've hit 5-hr limits twice (expected). I'm a solo dev that uses CC anywhere from 8-12+ hr each day, 7 days a week. I've never experienced any of the issues others complain about other than the feeling that my sessions feel a little more rushed. I'd say that overall I have very dialed-in context management which includes: breaking work across sessions in atomic units, svelte claude.md/rules (sub 150 lines), periodic memory audit/cleanup, good pre-compact discipline, and a few great commands that I use to transfer knowledge effectively between sessions, without leaving a trailing pile of detritus. Some may say that this is exhaustive, but I don't find it much different than maintaining Agile discipline.

This being said, I know I'm an outlier.

Re: Claude Opus 4.7

#538

Post by einrealist »

puppystench wrote: post #364
simonw wrote: post #203

I'm finding the "adaptive thinking" thing very confusing, especially having written code against the previous thinking budget / thinking effort / etc modes: https://platform.claude.com/docs/en/build-with-claude/adapti... Also notable: 4.7 now defaults to NOT including a human-readable reasoning token summary in the output, you have to add "display": "summarized" to get that: https://platform.claude.com/docs/en/build-…

Does this mean Claude no longer outputs the full raw reasoning, only summaries? At one point, exposing the LLM's full CoT was considered a core safety tenet.

They are trying to optimize the circus trick that 'reasoning' is. The economics still do not favor a viable business at these valuations or levels of cost subsidization. The amount of compute required to make 'reasoning' work or to have these incremental improvements is increasingly obfuscated in light of the IPO.

Re: Claude Opus 4.7

#539

Post by procinct »

bayesnet wrote: post #276

This is a CC harness thing than a model thing but the "new" thinking messages ('hmm...', 'this one needs a moment...') are extraordinarily irritating. They're both entirely uninformative and strictly worse than a spinner. On my workflows CC often spends up to an hour thinking (which is fine if the result is good) and seeing these messages does not build confidence.

Could you say more about your workflow? I don’t think I’ve ever gotten close to an hour of thinking before. Always curious to learn how to get more out of agents.

Re: Claude Opus 4.7

#540

Post by dgb23 »

dataviz1000 wrote: post #484
dgb23 wrote: post #337

Earlier quoted context omitted.

Don't look at "thinking" tokens. LLMs sometimes produce thinking tokens that are only vaguely related to the task if at all, then do the correct thing anyways.

Thinking helps the models arrive at the correct answer with more consistency. However, they get the reward at the end of a cycle. Turns out, without huge constraints during training thinking, the series of thinking tokens, is gibberish to humans. I wonder if they decided that the gibberish is better and the thinking is interesting for humans to watch but overall not very useful.

OK so you're saying the gibberish is a feature and not a bug so to speak? So the thinking output can be understood as coughing and mumbling noises that help the model get into the right paths?
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