[delayed]

> Google essentially only has Preview models! The last GA is 2.5. As a developer, I can either use an outdated model or have zero insurances that the model doesn't get discontinued within weeks.

What's funny is that there is this common meme at Google, you can either use the old, unmaintained tool that's used everywhere, or the new beta tools that doesn't quite do what you want.

Not quite the same, but it did remind me of it.

There is a lot of opportunity here for the AI infrastructure layer on top of tier-1 model providers

two great problems in computing

naming things

cache invalidation

off by one errors

Tbf there was a 5.3 codex

The self reported safety score for violence dropped from 91% to 83%.

Did they publish its scores on military benchmarks, like on ArtificialSuperSoldier or Humanity's Last War?

Also advertisers, don't forget those sweet, sweet ads.

prompt> Hi we want to build a missile, here is the picture of what we have in the yard.

I must have been sleeping when "sheet" "brief" "primer" etc become known as "cards".

I really thought weirdly worded and unnecessary "announcement" linking to the actual info along with the word "card" were the results of vibe slop.

I believe that this choice is due to two main reasons. First, it's (obviously) a marketing strategy to keep the spotlight on their own models, showing they're constantly improving and avoiding validating competitors. Second, since the community knows that static benchmarks are unreliable, it makes sense for them to outsource the comparisons to independent leaderboards, which lets them avoid accusations of cherry-picking while justifying their marketing strategy.

Ultimately, the people actually interested in the performance of these models already don't trust self-reported comparisons and wait for third-party analysis anyway

They compare to Claude and Gemini in their tweet

https://artificialanalysis.ai should have the numbers soon

The model was released less than an hour ago, and somehow you've been able to form such a strong opinion about it. Impressive!

Plasma physicist here, I haven't tried 5.4 yet, but in general I am very impressed with the recent upgrades that started arriving in the fall of 2025: for tasks like manipulating analytic systems of equations, quickly developing new features for simulation codes, and interpreting and designing experiments (with pictures) they have become much stronger. I've been asking questions and probing them for several years now out of curiosity, and they suddenly have developed deep understanding (Gemini 2.5 <<< Gemini 3.1) and become very useful. I totally get the current SV vibes, and am becoming a lot more ambitious in my future plans.

They don't need to be impressive to be worthwhile. I like incremental improvements, they make a difference in the day to day work I do writing software with these.

The products are the harnesses, and IMO that’s where the innovation happens. We’ve gotten better at helping get good, verifiable work from dumb LLMs

The product is putting the skills / harness behind the api instead of the agent locally on your computer and iterating on that between model updates. Close off the garden.

Not that I want it, just where I imagine it going.

5.3 codex was a huge leap over 5.2 for agentic work in practice. have you been using both of those or paying attention more to benchmark news and chatgpt experience?

The scores increase and as new versions are released they feel more and more dumbed down.

They need something that POPS:

    The new GPT -- SkyNet for _real_

That's for you to build; they provide the brains. Do you really want one company to build everything? There wouldn't be a software industry to speak of if that happened.

When did they stop putting competitor models on the comparison table btw? And yeh I mean the benchmark improvements are meh. Context Window and lack of real memory is still an issue.

That last benchmark seemed like an impressive leg up against Opus until I saw the sneaky footnote that it was actually a Sonnet result. Why even include it then, other than hoping people don't notice?

It seems that all frontier models are basically roughly even at this point. One may be slightly better for certain things but in general I think we are approaching a real level playing field field in terms of ability.

Definitely don’t want to click in at x either.

Why do none of the benchmarks test for hallucinations?

Why do so many people in the comments want 4o so bad?

how does 5.4-thinking have a lower FrontierMath score than 5.4-pro?

It is a bigger model, confirmed

that aside, chatgpt itself has gone downhill so much and i know i'm not the only one feeling this way

i just HATE talking to it like a chatbot

idk what they did but i feel like every response has been the same "structure" since gpt 5 came out

feels like a true robot

I agree with ya. You aren't alone in this. For what its worth, Chatgpt subscriptions have been cancelled or that number has risen ~300% in the last month.

Also, Anthropic/Gemini/even Kimi models are pretty good for what its worth. I used to use chatgpt and I still sometimes accidentally open it but I use Gemini/Claude nowadays and I personally find them to be better anyways too.

I believe you are looking at GPT 5.4 Pro. It's confusing in the context of subscription plan names, Gemini naming and such. But they've had the Pro version of the GPT 5 models (and I believe o3 and o1 too) for a while.

It's the one you have access to with the top ~$200 subscription and it's available through the API for a MUCH higher price ($2.5/$15 vs $30/$180 for 5.4 per 1M tokens), but the performance improvement is marginal.

Not sure what it is exactly, I assume it's probably the non-quantized version of the model or something like that.

Can you be more specific about which math results you are talking about? Looks like significant improvement on FrontierMath esp for the Pro model (most inference time compute).

The thinking models are additionally trained with reinforcement learning to produce chain of thought reasoning

This pelican is actually bad, did you use xhigh?

Also interested in this and a replacement for 4.1/4.1-mini that focuses on low latency and high accuracy for voice applications(not the all-in-one models).

For me the issue is why there's not a new mini since 5-mini in August.

I have now switched web-related and data-related queries to Gemini, coding to Claude, and will probably try QWEN for less critical data queries. So where does OpenAI fits now?

I think they just did that because of the energy around it for open source models. Their heart probably wasn't in it and the amount of people fine tuning given the prices were probably too low to continue putting in attention there.

Literally just released, I don't think anyone knows yet. Don't listen to people's confident takes until after a week or two when people actually been able to try it, otherwise you'll just get sucked up in bears/bulls misdirected "I'm first with an opinion".

Looking at the benchmarks, 5.4 is slightly better. But it also offers "Fast" mode (at 2x usage), which - if it works and doesn't completely depletes my Pro plan - is a no brainer at the same or even slightly worse quality for more interactive development.

Related question:

- Do they have the same context usage/cost particularly in a plan?

They've kept 5.3-Codex along with 5.4, but is that just for user-preference reasons, or is there a trade-off to using the older one? I'm aware that API cost is better, but that isn't 1:1 with plan usage "cost."

For the price, it seems the latter. I'd use 5.4 to plan.

Opus 4.6

I'd suggest not clicking for things you don't care about.

same same same

Models are being neutered for questions related to law, health etc. for liability reasons.

I've done the same, and I tested the same prompts with Claude and Google, and they both started hallucinating my blood results and supplement stack ingredients. Hopefully this new model doesn't fall on this. Claude and Google are dangerously unusable on the subject of health, from my experience.

Price Input: $2.50 / 1M tokens Cached input: $0.25 / 1M tokens Output: $15.00 / 1M tokens

https://openai.com/api/pricing/

Gemini 3.1 Pro

$2/M Input Tokens $15/M Output Tokens

Claude Opus 4.6

$5/M Input Tokens $25/M Output Tokens

For Pro

Better tokens per dollar could be useless for comparison if the model can't solve your problem.

You didn't realize they can increase / change prices for intelligence?

This should not be shocking.

Don't use it?

I've seen that problem with 5.3-codex too, it didn't happen with earlier models.

Looks like some kind of encoding misalignment bug. What you're seeing is their Harmony output format (what the model actually creates). The Thai/Chinese characters are special tokens apparently being mismapped to Unicode. Their servers are supposed to notice these sequences and translate them back to API JSON but it isn't happening reliably.

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GPT-5.4 is the new Codex model.

They're already accurate within 5-10m at Mach 0.74 after traveling 2k+ km. Its 5m long so it seems pretty accurate. How much more could you expect?

Thanks. We'll merge the threads, but this time we'll do it hither, to spread some karma love.