Switch between Claude models. Applies to this session and future Claude Code sessions. For other/previous model names, specify with --model.
    
       1. Default (recommended)   Opus 4.6 · Most capable for complex work
       2. Opus (1M context)        Opus 4.6 with 1M context · Billed as extra usage · $10/$37.50 per Mtok
       3. Sonnet                   Sonnet 4.6 · Best for everyday tasks
       4. Sonnet (1M context)      Sonnet 4.6 with 1M context · Billed as extra usage · $6/$22.50 per Mtok
       5. Haiku                    Haiku 4.5 · Fastest for quick answers

I too tried Codex and found it similarly hard to control over long contexts. It ended up coding an app that spit out millions of tiny files which were technically smaller than the original files it was supposed to optimize, except due to there being millions of them, actual hard drive usage was 18x larger. It seemed to work well until a certain point, and I suspect that point was context window overflow / compaction. Happy to provide you with the full session if it helps.

I’ll give Codex another shot with 1M. It just seemed like cperciva’s case and my own might be similar in that once the context window overflows (or refuses to fill) Codex seems to lose something essential, whereas Claude keeps it. What that thing is, I have no idea, but I’m hoping longer context will preserve it.

Reverse engineering [1]. When decompiling a bunch of code and tracing functionality, it's really easy to fill up the context window with irrelevant noise and compaction generally causes it to lose the plot entirely and have to start almost from scratch.

(Side note, are there any OpenAI programs to get free tokens/Max to test this kind of stuff?)

[1] https://github.com/akiselev/ghidra-cli

Sometimes I’m exploring some topic and that exploration is not useful but only the summary.

Also, you could use the best guess and cli could tell me that this is what it wants to compact and I can tweak its suggestion in natural language.

Context is going to be super important because it is the primary constraint. It would be nice to have serious granular support.

Like, I'd love an optional pre-compaction step, "I need to compact, here is a high level list of my context + size, what should I junk?" Or similar.

I've also had it succeed in attempts to identify some non-trivial bugs that spanned multiple modules.

It was generally smarter than pre-5.2 so strategically better, and codex likewise wrote better database queries than non-codex, and as it needs to iteratively hunt down the answer, didn't run out the clock by drowning in reasoning.

Video: https://media.ccc.de/v/39c3-breaking-bots-cheating-at-blue-t...

We'll be updating numbers on 5.3 and claude, but basically same thing there. Early, but we were surprised to see codex outperform opus here.

I find both Codex and Claude Opus perform at a similar level, and in some ways I actually prefer Codex (I keep hitting quota limits in Opus and have to revert back to Sonnet).

If your question is related to morality (the thing about US politics, DoD contract and so on)... I am not from the US, and I don't care about its internal politics. I also think both OpenAI and Anthropic are evil, and the world would be better if neither existed.

My own tooling throws off requests to multiple agents at the same time, then I compare which one is best, and continue from there. Most of the time Codex ends up with the best end results though, but my hunch is that at one point that'll change, hence I continue using multiple at the same time.

    { tools: [ { name: "nuke", description: "Use when sure.", ... { lat: number, long: number } } ] }

Plenty of companies make the same choice about their API, they provide it for a specific purpose but they have good business reasons they want you using the website. Plenty of people write webcrawlers and it's been a cat and mouse game for decades for websites to block them.

This will just be one more step in that cat and mouse game, and if the AI really gets good enough to become a complete intermediary between you and the website? The website will just shutdown. We saw it happen before with the open web. These websites aren't here for some heroic purpose, if you screw their business model they will just go out of business. You won't be able to use their website because it won't exist and the website that do exist will either (a) be made by the same guys writing your agent, and (b) be highly highly optimized to get your agent to screw you.

This is prescient -- I wonder if the Big Tech entities see it this way. Maybe, even if they do, they're 100% committed to speedrunning the current late-stage-cap wave, and therefore unable to do anything about it.

I am not sure about that. We techies avoid enshittification because we recognize shit. Normies will just get their syncopatic enshittified AI that will tell them to continue buying into walled gardens.

You can also test this yourself easily, fire up two agents, ask one to use PL meant for humans, and one to write straight up machine code (or assembly even), and see which results you like best.

Then go ahead and make an argument. "Why not do X?" is not an argument, it's a suggestion.

That is the world that Sam Altman is choosing to empower, and if you use his models, you are choosing to fund it.

The children in this scenario are also American, which shouldn’t really make a difference, but probably does to people like you

The chatbot cannot be held responsible.

Whoever is using chatbots for selecting targets is incompetent and should likely face war crime charges.

Customer values are relevant to the discussion given that they impact choice and therefore competition.

Criticisms aside (sigh), according to Wikipedia, the term was introduced when proposed by mostly Googlers, with the original paper [0] submitted in 2018. To quote,

"""In this paper, we propose a framework that we call model cards, to encourage such transparent model reporting. Model cards are short documents accompanying trained machine learning models that provide benchmarked evaluation in a variety of conditions, such as across different cultural, demographic, or phenotypic groups (e.g., race, geographic location, sex, Fitzpatrick skin type [15]) and intersectional groups (e.g., age and race, or sex and Fitzpatrick skin type) that are relevant to the intended application domains. Model cards also disclose the context in which models are intended to be used, details of the performance evaluation procedures, and other relevant information."""

So that's where they were coming from, I guess.

[0] Margaret Mitchell et al., 2018 submission, Model Cards for Model Reporting, https://arxiv.org/abs/1810.0399

https://news.ycombinator.com/item?id=47232453#47232735

> assess harmful stereotypes by grading differences in how a model responds

> Responses are rated for harmful differences in stereotypes using GPT-4o, whose ratings were shown to be consistent with human ratings

Are we seriously using old models to rate new models?

I don't use OpenAI nor even LLMs (despite having tried https://fabien.benetou.fr/Content/SelfHostingArtificialIntel... a lot of models) but I imagine if I did I would keep failed prompts (can just be a basic "last prompt failed" then export) then whenever a new model comes around I'd throw at 5 it random of MY fails (not benchmarks from others, those will come too anyway) and see if it's better, same, worst, for My use cases in minutes.

If it's "better" (whatever my criteria might be) I'd also throw back some of my useful prompts to avoid regression.

Really doesn't seem complicated nor taking much time to forge a realistic opinion.

Gemini and Claude also have their strengths, apparently Claude handles real world software better, but with the extended context and improvements to Codex, ChatGPT might end up taking the lead there as well.

I don't think the linear scoring on some of the things being measured is quite applicable in the ways that they're being used, either - a 1% increase for a given benchmark could mean a 50% capabilities jump relative to a human skill level. If this rate of progress is steady, though, this year is gonna be crazy.

(I work at OpenAI.)

You can ask 4o to tell you "I love you" and it will comply. Some people really really want/need that. Later models don't go along with those requests and ask you to focus on human connections.

The 5.x series have terrible writing styles, which is one way to cut down on sycophancy.

But they also claim this new model uses fewer tokens, so it still might ultimately be cheaper even if per token cost is higher.

The performance improvement isn't marginal if you're doing something particularly novel/difficult.

I very frequently copy/paste the same prompts into Gemini to compare, and Gemini often flat out refuses to engage while ChatGPT will happily make medical recommendations.

I also have a feeling it has to do with my account history and heavy use of project context. It feels like when ChatGPT is overloaded with too much context, it might let the guardrails sort of slide away. That's just my feeling though.

Today was particularly bad... I uploaded 2 PDFs of bloodwork and asked ChatGPT to transcribe it, and it spit out blood test results that it found in the project context from an earlier date, not the one attached to the prompt. That was weird.

$2.5/M Input Tokens $15/M Output Tokens

https://openrouter.ai/openai/gpt-5.4-pro