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Claude Opus 4.7

anthropic.com

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Re: Claude Opus 4.7

#181

Post by Glemllksdf »

mattas wrote: post #121
aurareturn wrote: post #31

Earlier quoted context omitted.

They don't have enough compute for all their customers. OpenAI bet on more compute early on which prompted people to say they're going to go bankrupt and collapse. But now it seems like it's a major strategic advantage. They're 2x'ing usage limits on Codex plans to steal CC customers and it seems to be working. It seems like 90% of Claude's recent problems are strictly lack of compute related.

Hard for me to reconcile the idea that they don't have enough compute with the idea that they are also losing money to subsidies.

They are loosing money because the model training costs billions.

Re: Claude Opus 4.7

#182

Post by fetus8 »

u_sama wrote: post #5

Excited to use 1 prompt and have my whole 5-hour window at 100%. They can keep releasing new ones but if they don't solve their whole token shrinkage and gaslighting it is not gonna be interesting to se.

on Tuesday, with 4.6, I waited for my 5 hour window to reset, asked it to resume, and it burned up all my tokens for the next 5 hour window and ran for less than 10 seconds. I’ve never cancelled a subscription so fast.

Re: Claude Opus 4.7

#184

Post by abatilo »

vesrah wrote: post #82
mesmertech wrote: post #48

Not showing up in claude code by default on the latest version. Apparently this is how to set it: /model claude-opus-4-7 Coming from anthropic's support page, so hopefully they did't hallucinate the docs, cause the model name on claude code says: /model claude-opus-4-7 ⎿ Set model to Opus 4 what model are you? I'm Claude Opus 4 (model ID: claude-opus-4-7).

On the most current version (v2.1.110) of claude: > /model claude-opus-4.7 ⎿ Model 'claude-opus-4.7' not found

Dash, not dot

Re: Claude Opus 4.7

#185

Post by siegers »

cube2222 wrote: post #59
buildbot wrote: post #12

Too late, personally after how bad 4.6 was the past week I was pushed to codex, which seems to mostly work at the same level from day to day. Just last night I was trying to get 4.6 to lookup how to do some simple tensor parallel work, and the agent used 0 web fetches and just hallucinated 17K very wrong tokens. Then the main agent decided to pretend to implement tp, and just copied the entire model to each node...

I've been using it with `/effort max` all the time, and it's been working better than ever. I think here's part of the problem, it's hard to measure this, and you also don't know in which AB test cohorts you may currently be and how they are affecting results.

Agree. I keep effort max on Claude and xhigh on GPT for all tasks and keep tasks as scoped units of work instead of boil the ocean type prompts. It is hard to measure but ultimately the tasks are getting completed and I'm validating so I consider it "working as expected".

Re: Claude Opus 4.7

#186

Post by ruairidhwm »

computomatic wrote: post #72
cupofjoakim wrote: post #20

> Opus 4.7 uses an updated tokenizer that improves how the model processes text. The tradeoff is that the same input can map to more tokens—roughly 1.0–1.35× depending on the content type. caveman[0] is becoming more relevant by the day. I already enjoy reading its output more than vanilla so suits me well. [0] https://github.com/JuliusBrussee/caveman/tree/main

I was doing some experiments with removing top 100-1000 most common English words from my prompts. My hypothesis was that common words are effectively noise to agents. Based on the first few trials I attempted, there was no discernible difference in output. Would love to compare results with caveman. Caveat: I didn’t do enough testing to find the edge cases (eg, negation).

I literally just posted a blog on this. Some seemingly insignificant words are actually highly structural to the model. https://www.ruairidh.dev/blog/compressing-prompts-with-an-au...

Re: Claude Opus 4.7

#187

Post by jwr »

> Opus 4.7 uses an updated tokenizer that improves how the model processes text. The tradeoff is that the same input can map to more tokens—roughly 1.0–1.35× depending on the content type. Second, Opus 4.7 thinks more at higher effort levels, particularly on later turns in agentic settings. This improves its reliability on hard problems, but it does mean it produces more output tokens.

I guess that means bad news for our subscription usage.

Re: Claude Opus 4.7

#189

Post by Glemllksdf »

helloplanets wrote: post #153

I wonder why computer use has taken a back seat. Seemed like it was a hot topic in 2024, but then sort of went obscure after CLI agents fully took over. It would be interesting to see a company to try and train a computer use specific model, with an actually meaningful amount of compute directed at that. Seems like there's just been experiments built upon models trained for completely different stuff, instead of any…

The industry probably moves a lot faster adding apis and co than learning how to use a generic computer with generic tools.

I also think its a huge barrier allowing some LLM model access to your desktop.

Managed Agents seems like a lot more beneficial

Re: Claude Opus 4.7

#190

Post by bryanlarsen »

cube2222 wrote: post #59
buildbot wrote: post #12

Too late, personally after how bad 4.6 was the past week I was pushed to codex, which seems to mostly work at the same level from day to day. Just last night I was trying to get 4.6 to lookup how to do some simple tensor parallel work, and the agent used 0 web fetches and just hallucinated 17K very wrong tokens. Then the main agent decided to pretend to implement tp, and just copied the entire model to each node...

I've been using it with `/effort max` all the time, and it's been working better than ever. I think here's part of the problem, it's hard to measure this, and you also don't know in which AB test cohorts you may currently be and how they are affecting results.

It works better, until you run out of tokens. Running out of tokens is something that used to never happen to me, but this month now regularly happens.

Maybe I could avoid running out of tokens by turning off 1M tokens and max effort, but that's a cure worse than the disease IMO.

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