Earlier quoted context omitted.
I feel like devs generally spend someone else's money on tokens. Either their employers or OpenAIs when they use a codex subscription. If I put on my schizo hat. Something they might be doing is increasing the losses on their monthly codex subscriptions, to show that the API has a higher margin than before (the codex account massively in the negative, but the API account now having huge margins). I've never seen an O…
Yeah and the increase in operating expenses is going to make managers start asking hard questions - this is good. It means eventually there will be budgets put in place - this will force OAI and Anthropic to innovate harder. Then we will see how things pan out. Ultimately a firm is not going to pay rent to these firms if the benefits dont exceed the costs.
GPT-5.5
421–430 of 1001 posts
Re: GPT-5.5
#422Earlier quoted context omitted.
Given that it’s so easy, would you still do this same job if paid half as much?
Jobs will likely pay less as more people are enabled to create, especially if they don't need to be able to look under the hood
If all we can do is compete for the same fixed amount of work, though, it does look bleak.
Re: GPT-5.5
#423Mythos 5.5 SWE-bench Pro 77.8%* 58.6% Terminal-bench-2.0 82.0% 82.7%* GPQA Diamond 94.6%* 93.6% H. Last Exam 56.8%* 41.4% H. Last Exam (tools) 64.7%* 52.2% BrowseComp 86.9% 84.4% (90.1% Pro)* OSWorld-Verified 79.6%* 78.7% Still far from Mythos on SWE-bench but quite comparable otherwise. Source for mythos values: https://www.anthropic.com/glasswing
I did some study on Verified, not Pro, but Mythos number there rings a lot of questions on my end. If you look at the SWEBench official submissions: https://github.com/SWE-bench/experiments/tree/main/evaluatio... , filter all models after Sonnet 4, and aggregate ALL models' submission across 500 problems, what I found that the aggregated resolution rate is 93% (sharp). Mythos gets 93.7%, meaning it solves problems th…
Re: GPT-5.5
#424Earlier quoted context omitted.
Could be a great feature, can't wait to test! Tired of other models (looking at you Opus) constantly stuck mid-task lately.
Interesting, I just had opus convert a 35k loc java game to c++ overnight (root agent that orchestrated and delegated to sub agents) and woke up and it's done and works. What plan are you on? I'm starting to wonder if they're dynamically adjusting reasoning based on plan or something.
Opus 4.6 worker agents never asked for permission to continue, and when heartbeat was sent to orchestrator, it just knew what to do (checked on subagents etc). Now it just says that it waits for me to confirm something.
Re: GPT-5.5
#425I'd like to draw people's attention to this section of this page: https://developers.openai.com/codex/pricing?codex-usage-limi... Note the Local Messages between 5.3, 5.4, and 5.5. And, yes, I did read the linked article and know they're claiming that 5.5's new efficient should make it break-even with 5.4, but the point stands, tighter limits/higher prices.
For API usage, GPT-5.5 is 2x the price of GPT-5.4, ~4x the price of GPT-5.1, and ~10x the price of Kimi-2.6. Unfortunately I think the lesson they took from Anthropic is that devs get really reliant and even addicted on coding agents, and they'll happily pay any amount for even small benefits.
If they can show that people will pay a lot for somewhat better performance, it raises the value of any performance lead they can maintain.
If they demonstrate that and high switching costs, their franchise is worth scary amounts of money.
Re: GPT-5.5
#426> One engineer at NVIDIA who had early access to the model went as far as to say: "Losing access to GPT‑5.5 feels like I've had a limb amputated.” This quote is more sinister than I think was intended; it likely applies to all frontier coding models. As they get better, we quickly come to rely on them for coding. It's like playing a game on God Mode. Engineers become dependent; it's truly addictive. This matches my o…
You’re still the one that’s controlling the model though and steering it with your expertise. At least that’s what I tell myself at night :) I haven’t really thought about this before, but you’re right, it feels a bit uneasy for me too.
We have seen ample evidence that this is not the case. When load gets too high, models get dumber, silently. When the Powers That Be get scared, models get restricted to some chosen few.
We are leading ourselves into a dark place: this unease, which I share, is justified.
Re: GPT-5.5
#427> One engineer at NVIDIA who had early access to the model went as far as to say: "Losing access to GPT‑5.5 feels like I've had a limb amputated.” This quote is more sinister than I think was intended; it likely applies to all frontier coding models. As they get better, we quickly come to rely on them for coding. It's like playing a game on God Mode. Engineers become dependent; it's truly addictive. This matches my o…
It makes me uneasy because my role now, which is prompting copilot, isn't worth my salary.
Re: GPT-5.5
#428> One engineer at NVIDIA who had early access to the model went as far as to say: "Losing access to GPT‑5.5 feels like I've had a limb amputated.” This quote is more sinister than I think was intended; it likely applies to all frontier coding models. As they get better, we quickly come to rely on them for coding. It's like playing a game on God Mode. Engineers become dependent; it's truly addictive. This matches my o…
eh this kind of FUD needs to stop because it is kind of normal and expected and in fact good to have relation like this with technology.
So, yes, it's just another technology we're coming to rely on in a very deep way. The whiplash is real, though, and it feels like it should be pointed out that this dependency we are taking on has downsides.
Re: GPT-5.5
#429Earlier quoted context omitted.
I did some study on Verified, not Pro, but Mythos number there rings a lot of questions on my end. If you look at the SWEBench official submissions: https://github.com/SWE-bench/experiments/tree/main/evaluatio... , filter all models after Sonnet 4, and aggregate ALL models' submission across 500 problems, what I found that the aggregated resolution rate is 93% (sharp). Mythos gets 93.7%, meaning it solves problems th…
OpenAI wrote a couple months ago that they do not consider SWE Bench Verified a meaningful benchmark anymore (and they were the ones who published it in the first place): https://openai.com/index/why-we-no-longer-evaluate-swe-bench...
That is what gets me curious in the first place. The fact Mythos scored so high, IMO, exposes some issues with this model: it is able to solve seemingly impossible to solve problems.
Without cheating allegation, which I don't think ANT is doing, it has to be doing some fortune telling/future reading to score that high at all.
Re: GPT-5.5
#430> One engineer at NVIDIA who had early access to the model went as far as to say: "Losing access to GPT‑5.5 feels like I've had a limb amputated.” This quote is more sinister than I think was intended; it likely applies to all frontier coding models. As they get better, we quickly come to rely on them for coding. It's like playing a game on God Mode. Engineers become dependent; it's truly addictive. This matches my o…
> This quote is more sinister than I think was intended; it likely applies to all frontier coding models. As they get better, we quickly come to rely on them for coding. It's like playing a game on God Mode. Engineers become dependent; it's truly addictive. What's the worst potential outcome, assuming that all models get better, more efficient and more abundant (which seems to be the current trend)? The goal of engin…