The marketing copy and the current livestream appear tautological: "it's better because it's better."
Not much explanation yet why GPT-5 warrants a major version bump. As usual, the model (and potentially OpenAI as a whole) will depend on output vibe checks.
As someone who tries to push the limits of hard coding tasks (mainly refactoring old codebases) to LLMs with not much improvement since the last round of models, I'm finding that we are hitting the reduction of rate of improvement on the S-curve of quality. Obviously getting the same quality cheaper would be huge, but the quality of the output day to day isn't noticeable to me.
I find it struggles to even refactor codebases that aren't that large. If you have a somewhat complicated change that spans the full stack, and has some sort of wrinkle that makes it slightly more complicated than adding a data field, then even the most modern LLMs seem to trip on themselves. Even when I tell it to create a plan for implementation and write it to a markdown file and then step through those steps in a separate prompt.
Not that it makes it useless, just that we seem to not "be there" yet for the standard tasks software engineers do every day.
I haven’t used GPT5 yet, but even on a 1000 line code base I found Opus 4, o3, etc. to be very hit or miss. The trouble is I can’t seem to predict when these models will hit. So the misses cost time, reducing their overall utility.
I'm exclusively using sonnet via claude-code on their max plan (opting to specify sonnet so that opus isn't used). I just wasn't pleased with the opus output, but maybe I just need to use it differently. I haven't bothered with 4.1 yet. Another thing I noticed is opus would eat up my caps super quick, whereas using sonnet exclusively I never hit a cap.
I'd really just love incremental improvements over sonnet. Increasing the context window on sonnet would be a game changer for me. After auto-compact the quality may fall off a cliff and I need to spend some time bringing it back up to speed.
When I need a bit more punch for more reasoning / architecture type evaluations, I have it talk to gemini pro via zen mcp and OpenRouter. I've been considering setting up a subagent for architecture / system design decisions that would use the latest opus to see if it's better than gemini pro (so far I have no complaints though).
Agree, I think they'll need to move to performance now. If a model was comparable to Claude 4, but took like 500ms or less per edit. A quicker feedback loop would be a big improvement.
2:40 "I do like how the pelican's feet are on the pedals." "That's a rare detail that most of the other models I've tried this on have missed."
4:12 "The bicycle was flawless."
5:30 Re generating documentation: "It nailed it. It gave me the exact information I needed. It gave me full architectural overview. It was clearly very good at consuming a quarter million tokens of rust." "My trust issues are beginning to fall away"
I feel like we need to move on from using the same test on models since as time goes on the information about these specific test is out there in the training data and while i am not saying that it's happened in this case there is nothing stopping model developers from adding extra data for theses tests directly in the training data to make their models seem better than they are
Honestly, I have mixed feelings about him appearing there. His blog posts are a nice way to be updated about what's going on, and he deserves the recognition, but he's now part of their marketing content. I hope that doesn't make him afraid of speaking his mind when talking about OpenAI's models. I still trust his opinions, though.
I think the biggest tell for me was having the leader of Cursor up vouching for the model, who has been a big proponent of Claude in Cursor for the last year. Doesn't seem like a light statement.
When they were about to release gpt4 I remember the hype was so high there were a lot of AGI debates. But then was quickly out-shadowed by more advanced models.
People knew that gpt5 wouldn’t be an AGI or even close to that. It’s just an updated version. GptN would become more or leas like an annual release.
There's a bunch of benchmarks on the intro page including AIME 2025 without tools, SWE-bench Verified, Aider Polyglot, MMMU, and HealthBench Hard (not familiar with this one): https://openai.com/index/introducing-gpt-5/
I didn't think GPT-4 warranted a major version bump. I do not believe that Open AI's benchmarks are legitimate and I don't think they have been for quite some time, if ever.
It seems like you might need less output tokens for the same quality of response though. One of their plots shows o3 needing ~14k tokens to get 69% on SWE-bench Verified, but GPT-5 needing only ~4k.
O3 has had some major price cuts since Gemini 2.5 Pro came out. At the time, o3 cost $10/Mtok in and $40/Mtok out. The big deal with Gemini 2.5 Pro was it had comparable quality to o3 at a fraction of the cost.
I'm not sure when they slashed the o3 pricing, but the GPT-5 pricing looks like they set it to be identical to Gemini 2.5 Pro.
Explains why I find AGI fundamentalists similar to tater heads. /s
(Not to undermine progress in the foundational model space, but there is a lack of appreciation for the democratization of domain specific models amongst HNers).
I'm being hyperbolic but yeah four roses is probably the best deal next to Buffalo trace. All their stuff is fairly priced. If you want something like Weller though, you should get another wheated bourbon like Maker's Mark French oaked.
If you can find Buffalo Trace for msrp which is $20-30, it's a good deal. I think the bourbon "market" kind of popped recently so finding things has been getting a little easier.
> I think the bourbon "market" kind of popped recently
It def did. The overproduction that was invested in during the peak of the COVID collector boom is coming into markets now. I think we'll see some well priced age stated products in the next 3-4 years based on by acquaintances in the space.
Ofc, the elephant in the room is consolidation - everyone wants to copy the LVMH model (and they say Europeans are ethical elves who never use underhanded mopolistic and market making behavior to corner markets /s).
I can already see LLMs Sommeliers: Yes, the mouthfeel and punch of GPT-5 it's comparable to the one of Grok 4, but it's tenderness lacks the crunch from Gemini 2.5 Pro.
Isn't it exactly what the typical LLM discourse is about? People are just throwing anecdotes and stay with their opinion. A is better than B because C, and that's basically it. And whoever tries to actually bench them gets called out because all benches are gamed. Go figure.
Well, reduced sibilance is an ordinary and desirable thing. A better "audiophile absurdity" example would be $77,000 cables, freezing CDs to improve sound quality, using hospital-grade outlets, cryogenically frozen outlets (lol), the list goes on and on
The "audiophile" attitude is such that that "work" is enjoyment. It's a game, a hobby. I'm not defending the extremes of it, but it's not like these people are miserable, they enjoy doing it even if it rapidly becomes completely insane nonsense entirely detached from reality.
Always have been. This LLM-centered AI boom has been my craziest and most frustrating social experiment, propped up by the rhetoric (with no evidence to back it up) that this time we finally have the keys to AGI (whatever the hell that means), and infused with enough AstroTurfing to drive the discourse into ideological stances devoid of any substance (you must either be a true believer or a naysayer). On the plus side, it appears that this hype train is taking a bump with GPT-5.
The room is the limiting factor in most speaker setups. The worse the room, the sooner you hit diminishing returns for upgrading any other part of the system.
In a fantastic room a $50 speaker will be nowhere near 95% of the performance of a mastering monitor, no matter how much EQ you put on it. In the average living room with less than ideal speaker and listening position placement there will still be a difference, but it will be much less apparent due to the limitations of the listening environment.
You might lose headroom or have to live with higher latency but if your complaint is about actual empirical data like frequency response or phase, that can be corrected digitally.
You can only EQ speakers and headphones as far as the transducer can still respond accurately to the signal you're sending it. No amount of EQ will give the Sennheiser HD-600's good sub-bass performance because the driver begins to distort the signal long before you've amplified it enough to match the Harman target at a normal listening level.
DSP is a very powerful tool that can make terrible speakers and headphones sound great, but it's not magic.
Pretty much my first point… At the same time that same DSP can make a pretty mediocre speaker that can reproduce those frequencies do so in phase at the listening position so once again the point is moot, effectively add a cheap sub.
There is no time where you cannot get results from mediocre transducers given the right processing.
I’m not arguing you should, but in 2025 if a speaker sounds bad it is entirely because processing was skimped on.
This varies wildly with what frequency range you're talking about. Bass region, yes - room geometry makes a big difference. The rest of the range, DSP is your friend. Loudspeakers and Rooms by Floyd Toole is an awesome resource here.
Not much explanation yet why GPT-5 warrants a major version bump. As usual, the model (and potentially OpenAI as a whole) will depend on output vibe checks.