This is a clear step change, I'm genuinely amazed.
Its answers are to-the-point, logically structured, clearly written. Like a really well made mini essay on your question.
It also really likes to build little simulations / models that you can tweak the parameters on. Amazing to explore how different assumptions affect economic scenarios, for example.
It's also great at picking (sourced) illustrations.
I'll be using this for everything that isn't coding from now on.
I've been trying it out via the API and the model has been a delight so far. Clear in the head, not RL-fried, distinct writing style, and big model smell. That said, the em-dash is back w/ this model.
That said, I would be so frustrated with Sundar Pichai if I was on the Google team. The botched rollout of this model seems to be entirely a matter of capacity, and Pichai has been more than happy to give it to anybody but his own team.
I know it's a matter of personal taste but it reminds me of a grad student that tries to sound smarter than he is by being dismissive and opaque.
> You gave me a long answer but it didn’t answer my question: can we safely remove $X
So obviously, government documents, top publications from liberal arts scholars and STEM > Wikipedia > Reddit (and within, r/AskHistorians > r/all, etc.). The first group is the one most likely to (over)use correct / fancy typography.
I have a related pet theory: the telltale sign of AI writing is being too good for the context it's found in, in terms of vocabulary and style as well as structure.
What is jarring about this is precisely the quality - we associate certain styles with important (or self-important) venues, so it's uncanny to suddenly see random shitposts, copywriting spam, or regular chat replies look like work of NYT bestselling authors or legal scholars - it feels wrong in places we expect basic language, grug-brain reasoning, and lots of typos.
Also, what makes the above much more noticeable, is that AI systems don't have cross-session stylistic memory. From the POV of an LLM, that perfectly fitting, masterfully crafted starter is the first and only time it used it. From the user's POV, it's the tenth time they're seeing it in the span of an hour.
Em dashes ended up as one of the first victims of llm age
If you just want good llm text, it’s great.
I think there was a lot of misplaced hate of the em-dash due to all the low-qualify AI slop people were trying to pass off as their own for a while.
At the same time, OpenAI had a disastrous dev day that led a lot of users to jump ship to Claude.
Couple weeks later, Gemini-4-argon is still unavailable on anything less than the ultra tier, which means there's no natural funnel to get ppl to pay for the more expensive ultra plan other than "trust us the model is good".
If Google had argon broadly available earlier this month they would have picked up a ton of new users.
Models move so fast now and Google is very cautious about releasing them.
What sucks, hard, is googles ability to deploy direct to devs and customers at scale when the deployment is not a longstanding Google project.
Case in point: I’m an ultra subscriber and see in my app right now an ad to try Gemini 4; meanwhile the only models I have available are 3.1 pro,3.6 flash, and 3.8 thinking.
Clicking the try gemini 4 link takes me to a demo prompt that loads 3.6 flash. Srsly
I won’t even start telling you what getting api key based access together was like a year ago.
Then basic mistakes (inferring wrong information from the given text, hallucinations...). Gemini is okay for some absolute basics and the rest of the work goes to actual frontier models.
Narrating this as if it’s the game of thrones is a fun exercise, but it’s just becoming commonplace now that models jump ahead of each other on a weekly to Monthly basis.
This was part of the issue, as stated in their comment. We couldn't jump around to try it for the reason that is wasn't made available to, as you say, "all the power users".
Do you really think people in google are happy about how long it took to release this model?
We’re all running on Astra medium the whole week and loving it.
I’m curious what I missed.
$500/month for 40+ hours a week of Astra usage, with ultrafast mode available when you’re near the weekly reset has made our jobs so much easier. Our product has some sciencey bits as well, ands Astra kills it as a rubber duck in this area.
As a consumer whose 20x account got downgraded, I am a bit sad. I can’t justify $500 a month on personal AI spend. As an engineering manager, I could not be happier with OpenAI’s services. Also, Codex App Server is amazing.
Can you elaborate on this?
Reddit thread with 247 comments here: https://www.reddit.com/r/codex/comments/1wtgv3z/dev_day_l/
Aside from that I'm not sure what was "disastrous" - maybe the lack of a new model release to compete with the just-released Claude Opus 5.5?
I mean, I heard rumors too that it would be ultra only, but that doesn't seem to be the case, unless you are specifically talking about antigravity?
Realistically, market adoption isn't going to be the result of terminally online people (like me, I confess) following things way too closely on social.
edit: just a few hours after this comment I got access to Gemini 4 on the Gemini app. Yayy! :)
https://blog.google/innovation-and-ai/models-and-research/ge...
Oh yeah. Completely forgot about that. Big headlines and big claims, but no one could actually use it and report on it.
Does seem like they don't quite grasp how the hype train operates. Or maybe they don't care as much.
Given FelonyBench, do we also have a FantasyBench?
- Try in Gemini
- Try in Google AI Studio
- Try in Google Antigravity
Will non users of Google products even know what these three things even are?
I have AI Pro, but AI Studio tells me I can "unlock" Argon by upgrading to Ultra.
Is there anybody in Google having a look at how bad and complicated their payment system is? In all the other AI providers you just sign in, pay, get a key, done. In other clouds like AWS, you add a card, pay little to verify the card the first time, monthly bill, done. In Google it looks like you have to fight them every time for the very thing they should make as easy as possible: opening your wallet.
i live in French Polynesia, a French territory, i'm a Google AI subscriber and i can't use Google AI Studio or antigravity cli because i'm in a geo restricted area ...
It seems Google has no issue laying out cables across the Pacific Ocean from/to Tahiti, they don't have an issue selling their services and plans there but i can't use AI Studio or antigravity cli because of geo restrictions ...
They made using it so hard/unclear that myself and every other dev I knew gave up.
It's clear that they still haven't done any organizational self-reflection.
What you did isn’t wrong, but is unfortunately very similar to what real scammers do. The heuristics behind these anti-abuse efforts are always going to have false positives.
I would also advise against buying Gemini if you have any other Google services, because you get banned across board if they don't like something. To use Google it's best to first migrate off of Google.
Otherwise great product, I really like and would recommend Gemini. The usage is also very generous, at least Flash models.
I recently had to verify my YouTube account with a phone number recently because it requires this in order to set a thumbnail.
Questions right off the bat:
1. Why do I need to verify to post a thumbnail that will take 1MB on their servers but not to post a video that will take 100MB on their servers and be public??
2. Why do I need to verify my phone number on YouTube when it's linked to my Google account which has a verified phone number and 2FA etc.?
In any case, it takes you to a CSS-less page that asks you to choose between getting an SMS or being called to get a code. I choose SMS and it doesn't work. It shows an error message with no description just "something went wrong". I try a few times and it keeps not working.
I then switch to the call, and guess what! I have tried "too many times" and will have to try again later. Call worked the next day, but geez, what a mess.
Ended up with Soniox instead, took minutes to get started.
They had gemini cli, what happened to that? Was fine
Couldn't you just say what the price will be after the introductory period and when it'll take effect? Do they have some SKILL.md for these blog posts that requires the most important information to always be buried ten levels deep and hidden behind a login page?
$2/million input and $10/million output today. $4/$20 after December 1st.
It's perhaps a face saving thing to pretend you'll charge the same. Let's see if it actually happens.
Is Elon Musk doing their pricing?
It is price fixing, nothing else
+1. FWIW, I didn't even get far enough to wonder about the price.
The lead (and only) sentence states "Our next era of frontier intelligence, built to solve your hardest problems" a statement equally true of any new Google or competitor model, hence completely devoid of useful meaning. I then kept scrolling down... and down... and down, giving up after a half dozen screens of big images, huge videos and some customer stories. The first sentence of every text block was equally vague or meaningless so... tv;dr (Too vague, didn't read).
Even the Gemini sub-reddit is laughing: https://www.reddit.com/r/GeminiAI/comments/1x2dr47/the_lates...
It was the case a year ago, but it's less clear cut nowadays, I've seen non-tech people using claude on their phone in the train, and many tech people seem to have switched from Claude Code + Opus to Codex + Sol over the past few month.
- Natively multi-modality
- Enormous context windows
- Reliably cheap prices
It's like they don't release a model until it passes the above checklist.
Most providers have some mixture of these three occasionally, but with Google, every model is consistent. This kind of consistency is really nice to have.
https://arena.ai/leaderboard/agent/chat/pareto
For almost same intelligence (+15.56%, +15.18%), drastic difference in cost/task ($3.51 vs $0.41)
For chat, yes. But switch to code on the same page, and the picture changes. OpenAI takes the lead.
Argon build: https://html.non.io/lcars-argon/
GPT 6.1 sol: https://html.non.io/lcars-gpt-6.1-sol/
Opus 5.5 (currently SOTA for this): https://html.non.io/lcars-opus-5.5/
Original designs: https://diffui.ai/app/canvas/5093e689-1e74-4f26-b632-2a4500f...
(Chrome, also incognito)
As soon as gpu acceleration is disabled, the webpage goes from 60fps across the three systems I tried to ~7-20fps when software rendering is forced.
I suspect if you go to chrome://gpu you'll find something different from what I have here: https://image.non.io/a47a1b03-1b87-4fd5-92a2-b39e62b1cfd9.we...
So that all being said, a normal website should work totally fine on low power mode, and this should definitely be a negative on the Opus implementation.
I would disagree with this based on two things:
1. the prompt, which explicitly states to render the planets in the background in 3js (thus the gpu implementation).
2. the purpose of this, which is a test of its ability to convert an image design into a html/js/css implementation that matches it as closely as possible. I've talked about this before when it's come up[†]. The purpose is to be a good test, not a good design or website.
For reference, here's the full prompt used:
> Create a web page implementation from the following instructions: https://diffui.ai/build/Futuristic_Interior_UI_build.md?auth... dont reference any other folders that have similar builds. Focus on the flowing SVGs. Animate elements that make sense to be animated. For the starships that have planets in the background, ask for high quality images with the windows as a transparent background, and recreate the planets behind them in 3js. It should be one single page application that I can just open the index.html file to view.
[†] https://news.ycombinator.com/item?id=49997135
[‡] here's a copy of this since the diffui build instructions wont be accessible without an account: https://non.io/lcars-build-instructions
FWIW, I have literally the exact same configuration (M3 air, google chrome). Here's a recording on mine where I'm seeing 60fps: https://non.io/video/video-3587018401.mp4
Edit: The argon built site pins 1.3 cpu cores. The Sol 6.1 site uses 0.18 cores.
Here's a vid of both opus and argon side by side, which might be more viewable on mobile: https://x.com/pwnies/status/2110058987462988152
Here's low, medium, high. Even low is better than pelicans from any other lab: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
low cost 10.185 cents, medium was 11.098 cents, high was 17.166 cents.
The costs are similar because it used more output tokens than reasoning tokens for all three pelicans, even for high.
> If it's on HN and is a meme at this point, it will end up in the training set.
> It's kind of fun to imagine that there is an intern in every AI company furiously trying to get nice looking svg pelicans on bicycles.
Google listened and assigned a few interns, I guess, for the task
Figure of speech.
>Why not just generate svgs with any editing software?
How do you do that from the whiteboard/notebook?
Everyone says that until ms, apple, google, etc introduces this polished feature as part of their tablet imaging editing/document collaboration office suite. Then everyone can;t get enough of it.
> It's a small task whose result is an input to something else - ideal for a tool for LLM (or script)
Have you actually tried this? If it's a small task then why can't LLMs actually do it? Kind of like the pelican test. Every LLM can do a SVG pelican but most of the results are unusable in practice for production/productivity purposes.
Every LLm can extract some half-broken SVG hallucination of your jots, but I'm asking for an app that's polished and works accurately 99% of the time, which means a dedicated app/model tuned specifically for this task, and nobody makes this yet.
> How many pelican riding bicycle SVGs were there before this test existed? What if the training data is being polluted with all these wonky results...
But Simon has demonstrated before that asking for any animal doing something silly will still get similar results from the same model.
Expect Google models to be benchmaxxed as much as possible, but don't expect commensurate performance on real-world tasks.
This is not true. It is needless negative hyperbole that IMHO doesn't belong on HN.
If what you say is true, then the next set of benchmarks will be written to illuminate the true reflection of real-world tasks. Then those get benchmaxxed so managers can show "numbers go up."
Repeat enough times, and you get materially and genuinely improved products.
The burden is on the press, outside researchers, and general tech industry to both write and care about the metrics that matter.
They look remarkably good, sure -- but not perfect.
I note that even these bikes are not actually rideable. For a bike to be stable, the front wheel axle needs to extend forward a little ahead of the fork - the fork must ”bend forwards”. This is what actually keeps the bike upright, not gyroscopic effects or some such. Given LLMs are generally good at basic maths, im surprised they get this wrong.
Maybe a bit less so in this case though. As per the announcements, the model was actually trained for this prompt, so was fed some training data. I can well imagine the data there was just not so good.
People drawing bikes, scroll down to see they having made renders of all the drawings. Hilarious
You can also ride a negative rake fork. They are less stable, yes, but a far cry from not actually rideable, cf https://www.instagram.com/yourforkisonbackwards/
I'll also note that, using a piece of paper as a straight edge against my screen, there is a very slight forward curve on the low and a bit more on the high responses. Medium does appear to be perfectly straight.
https://www.walmart.com/ip/Huffy-26-inch-Rock-Creek-Men-s-Mo...
(apologies if I'm misunderstanding).
You're nit-picking on a detail no one actually cares about.
Subtle highlights on the metal frame, sweat beads, jaunty little hat…it has style. At least compared to the rather literal ChatGPT outputs. https://static.simonwillison.net/static/2026/gpt-pelicans-gr...
Is the prompt context telling the models what their effort level is, and the ambient presence of that concept of ‘high effort’ in the context window is leaking through and affecting the portrayal?
https://huggingface.co/Qwen/Qwen3.8-27B/blob/main/chat_templ...
xhigh: "Reasoning effort is set to xhigh. Please think carefully through the task, validate key assumptions, consider plausible alternatives, and prioritize correctness, consistency, and clarity in the final answer."
low: "Reasoning effort is set to low. Keep your thinking brief and focused, moving directly to the conclusion without unnecessary elaboration."
https://en.wikipedia.org/wiki/Pelican#/media/File:Pelikan_Wa...
Argon: https://jonclegg.github.io/pacman-bakeoff/dev/#gemini-4-argo...
Opus 5.5 https://jonclegg.github.io/pacman-bakeoff/dev/#claude-opus-5...
Also, that first tweet contains a commentary that, for me, explain a lot about the public misconception about how current AI works:
> wait, does this mean you guys are actively training svg generation? i was mostly amazed by it thinking the models doing better at it by itself due to becoming better at reasoning.
No, it's not better reasoning that drives current AI, it's better examples we have been giving them. Whole army of people generating content for the frontier labs.
Also, reasoning improving the out of distribution part, like computer use, tool use etc.
Another case where allowing oneself to anthropomorphise LLMs a little yields better intuitions.
People don't get more skilled by raw reasoning, either. They improve their skills with better textbooks (pre-training data), more practice exercises (RL data), and generally goofing around (self-play).
It's a simple manner of prompt...generate concept art with nanobanana, measure, llm creates svg with text, measure the distance between rasterized svg embedding to nanobanana art....do post-training RL on a 3 million such images and matching embeddings.
I swear this will be a canonical example of Goodhardt's Law in action.
You have to switch to a new benchmark that isn't so easy to run a verifiable rewards pipeline on...Perhaps a longer horizon task where it's easy to mess up midway.
Yes, Simon has made pelicans a benchmark and now models have grown very good at rendering them. Is there a reason to believe that this isn’t manifesting as useful skill in general in SVG generation?
It would perhaps be slightly interesting if the prompt was run 100 times for each model. I doubt the pelicans would look identical.
I've noticed that chatgpt is working harder. But still I prefer Claude, it just feels nicer to me.
As one point, the high pelican's arm is see-through/messed up.
They do look worlds better than previously, to be fair.
Here's 'Generate an SVG of an armadillo in fishnet tights jaywalking on Mars' for Gemini 4 Argon: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
Some other models for comparison: https://tools.simonwillison.net/markdown-svg-renderer?url=ht...
Modern models can make nearly photorealistic games in threejs. So why do they struggle with these pelicans?
However, result quality really drops off once you get out of the top 5 or so.
(NSFW note: all buttocks are covered by fairly conservative bikini bottoms and show no genitals, bulges, etc.)
The test is always going to be hard though, because it's an absurd image to ask for.
UPDATE: I like this theory about using an agentic harness that can render and iterate better: https://news.ycombinator.com/item?id=50073879#50074395
https://www.gianlucagimini.it/portfolio-item/velocipedia/
HN discussion: https://news.ycombinator.com/item?id=17699017
https://www.wired.com/2016/04/can-draw-bikes-memory-definite...
The pretty 3d stuff you see are agents controlling a Blender MCP in a loop where they look at the scene, think, adjust the scene, repeat.
(As I said in a separate comment above, results are all bikini-clad and not revealing. However, whether it is NSFW is up to you/your work policies.)
Are you sure they're not fetching free asset packs for that?
wasn't the whole point of pelicans is that labs *wouldn't* train for that?
so now the benchmark is ruined
<https://tools.simonwillison.net/svg-render#%3Csvg%20font-fam...> <https://tools.simonwillison.net/svg-render#%3Csvg%20font-fam...>
- I was having trouble embedding the SVGs as links, and ChatGPT recommended Simon Willison's SVG Renderer as a workaround.
Just my 2 cents on the pelicans benchmark: I think it might help if you were a bit more explicit about not taking this one benchmark too seriously. I know you say that at times, but at other times (such as the comment I'm replying to) you elide it which can read as a seriousness about the benchmark.
Medium and high are fixies apparently, (or they have a worm gear, although the hub doesn't look big enough for that), since neither has a derailleur. That would also explain the lack of brakes at least.
Low is still obviously failing the "test". It's pedals are not actually attached to the bike either.
Medium has a handlebar that goes through the pelican's beak. So I'd say also failing.
For the high, the only nitpick would be that the bell isn't quite on the handlebars and the stripes on his scarf don't line up correctly.
Like a working scroll on a webpage.
God damn this is impossible to use of safari mobile.
Argon is trying to solve the "hard" problems like those.
Is it actually available?
Eyes glazed over again.
Did you even read your own site before you posted it?
One of the first pages I opened:
> Smoking-gun proof of plant-label ingestion: in Row 2, the central cut root is labeled okol (f88r.15), and directly underneath it on Line 2 (f88r.19) the recipe writes...chokol daiin qoekol qoekol qockhol okol cheol...!
It's just gibberish.
What kind of progress was made with Argon with the Voynich?
It looks like you've managed to decode it? Is that legit?
The code is bad though.
They all ramble, they're all allergic to getting to the point. But, Gemini historically has pretty good prose. Even the self-hosted Gemma models have pretty nice, inoffensive prose. I wouldn't want to read a novel written by any of them, but I tend to ask Gemini questions in chat more often than Claude or GPT, because it has good prose and is good at using the web to research.
Or else it say, here is the result. (but no it didn't do no research, and tells me, sorry I forgot to search).
It's got balls to lie straight at my face.
Now, I happen to believe that's an excellent strategy. I bet they actually resolve more LLM requests than any other company at this point. The lay person still uses Google exclusively.
if they throw in youtube premium in that package then I might reconisder
they gave away so much pro that they need to have smaller usage levels than competitors or they'll lose a ton of money. $20 was already a loss for users actually using their quota. the majority of pro users are paying far less than that.
so they'll lose their paying users, and keep their free or subsudized, which will make the economics even worse.
I use the hell out of both. Kind of sad that they took Spark out of beta-access, being able to orchestrate via your phone on something that has direct access to your Drive meant I was authoring skills and other functionality on a schedule with spreadsheets serving as databases, deterministic tests, etc.
Hope they roll it out to a full feature soon
More specifically, who are they targeting with Argon and why is it behind the Ultra plan? Who is paying $99/month if not developers?
Corporate companies. Bog-standard office workers don't need top-of-the-line Mythos intelligence, just something to add to Google Workspace. I personally doubt developers use Gemini for coding beyond marketing materials.
There is another subset in the Google Workspace for enterprise clients.
Because if it’s not, and it’s not available to all paying users (Right now, it’s only available to Ultra users), then they are behind the curve if they’re treating it like Fable.
It’s tone deaf.
Unique users, maybe. But a simple query that's answered in a paragraph is negligible compared to devs running agents continuously for tens of hours.
When I see non technical people use that Google reply I'd be surprised if it's more than 1k output tokens, and with a tiny model. So a 3000x in output tokens, on a model at least 10x as expensive, I wouldn't be surprised if hardware usage is 30.000x for a dev that uses coding subs vs someone who uses the Google AI overview a couple of times a day.
And that's not counting the absurd difference in cache reads.
As of my knowledge cutoff, three of the top state-of-the-art large language models are Google's Gemini 2.5 Pro, OpenAI's o3 (and GPT-4.5), and Anthropic's Claude 3.7 Sonnet.
edit: It failed on the second prompt and they disabled it now.
https://antigravity.google/blog/gemini-4-argon-in-google-ant...
> Gemini 4 Argon is available today for Google AI Pro and Ultra subscribers
1. It's available to Pro in Gemini app, but NOT antigravity (for coding)
2. It's a phased rollout and Ultra got it first, Pro is coming later
The latter blog post is specifically "Argon in Antigravity" versus the other blog post is about Argon in general, so seems like #1 is more likely.
EDIT:
Apparently Ultra subscribers gets Argon everywhere including in Google Antigravity
Pro subscribers only get them in Gemini app and Search
Antigravity CLI 1.3.4
[My account email] (Google AI Ultra)
Gemini 4 Argon (High)
~/[working folder]
> /model
⎿ Model set to Gemini 4 Argon (High)https://one.google.com/about/google-ai-plans/?icid=microsite...
It seems to be rolling out gradually.. not available at once to everyone.
It's crazy to me how care there is in most of these vibe-coded vibe-coding harnesses. I don't like oai's models much but I have to say their UI has by far the best attention to detail of any major player.
It doesn't show in the desktop UI.
I like this phrasing and am curious what new word or phrase we might use to describe big companies over pushing Ai internally at the expense of their product quality. This pattern is so ubiquitous across google cloud products I have taken to calling them the 21st century IBM, having been a IBM baby and witnessing first hand their fall from exceptionalism.
And took similar companies that were quality engineering focused -- like DEC or Palm -- down with them through acquisition.
Related funny https://www.youtube.com/watch?v=DlTNN0gvkLM
build this scene space station docking scene https://x.com/_satyam_ai/status/2110097633922662752?s=20
This is crazy if it can work accurately.
It thinks a ton, ~24k output tokens a run vs ~3k for Sonnet. It missed a solution by thinking its way into a 10 min timeoout.
Something to look out for - if you're using pi, it doesn't have Argon in its catalog yet and reports cost at Kimi's rates, about a third of what OpenRouter actually bills.
Pretty misleading.
Claude Opus 5.5 58.7%
Gemini 4 Argon 47.1%
Claude Fable 5.1 46.7%
Language info: https://killswitch-lang.org/
https://dach.peerbench.ai/compare?models=google%2Fgemini-4-a...
Me: Gemini, navigate to nearest McDonalds.
Gemini: [Lists 10 McDonalds locations wnd asks which I want.]
Me: What were the priginal instructions just now?
Gemini: [Recites imperatives about tone and content from its memory system.]
Me: No. I told you to navigate to the NEAREST McDonalds. There's only one NEAREST McDonalds. Do that one.
Gemini: My mistake, navigating to the nearest McDonalds. [Opens Maps, navigating to a McDonalds across town.]
I also have had shockingly bad results with gemini on android. Ended up giving up on it after a few months. Silly silly google with their trademark UX polish and attention to usability.
Flash lite is very noticeably more idiotic than 3.8 Flash. It's why the quick AI overview is often much worse than the AI mode one.
The only thing I can figure is that Google's consumer-facing free-to-use models are set to "reason as fast as possible with only the most basic plausibility checks." I have to correct them like this every single time.
Similarly, you can run local LLMs on your Google Pixel smartphone, but if you want them to be hardware accelerated using the chip that's included for that very purpose on your phone... it has to be Google's models via Google's apps. So you can't say "alright, a fast, local model which sounds great, but I want one that is trained to always run a search before answering, in a harness with good search tools for answering the kinds of practical questions I often ask", you're actually not free to put that together in a practicable way.
So there is indeed some competition here. But it's not really fair competition.
1. I ask it to list out all my old Keep notes that are just throwaway task lists, reminders, etc. to propose deleting them.
2. It comes up with like 4 of them (I know for a fact there are dozens, if not hundreds). I ask it for an exhaustive list.
3. I see its thinking trace briefly show "Refusing to produce an exhaustive list..." and then it says "Yes, that is all of them."
Not sure whether it's lack of intelligence, bad tools, or both, but that was extremely frustrating to see.
I think these companies have good models but expose them poorly like others have mentioned
If it had exact location access people would be crying that Gemini steals their location data.
I tell it to "Play Let it go from frozen". It plays it on youtube music. 5 seconds later I tell it again 'Play let it go from frozen" it then just dies saying "I can't play videos in car".
i benchmarked 20+ models in real worlds and gemini families are the best (highest customer satisfaction)
From what I saw online.
Source? Their documentation says it's available on the $20 plan and I can confirm firsthand.
it needs MCP asap
Which is even weirder given that the api costs are less than for the other providers
Maybe they don't really care about antigravity users. Also, monitoring usage is horrible.
Even after using the free model in Google search talking to Anthropic or OpenAI models feels like talking to an ox.
Gemini 3 Flash was around 0.403, 2.5 Pro was around 0.34.
is it just 'Hire the right people'?
It's extremely inconsistent internally as well.
Look up the slime mold analogy for Google.
Google did a very good job on things that required longer research times and compute scale, note that Transformers, BERT & Scaling Laws came out of Google (though the latter were also worked on at OpenAI).
Conversely, Google is horrible at product, and without OpenAI, it probably would have taken many years longer to take off. I know this because Google had demos of this internally, but absolutely no stomach to release it, and they didn't believe in the API use case at all.
Which is to say, I think if Google was smaller, we probably would have gotten more SaaS, not more breakthrough research.
It's also worth noting, Gemini 3.5 Pro never came out.
Having worked there (but only in a small part of it that eventually got absorbed into Gemini), it's some combination of:
* The eye of Sauron (management) can make specific spots (e.g. Gemini) push harder (and attract the motivated people at the company and be selective)
* They hire a lot better than average (Generally from the top 10%, hotter places in Google are more selective)
* They have a promo process that gives career motivated people a way to prove themselves (not always aligned with things being on the right track ofc, and not always non-political, but they definitely do something)
* Redundancy in teams causes them to compete (not necessarily productively)
Of course, there are other downsides not listed here (insane bureaucracy for launches, GDPR/BCID compliance, etc, etc), but a lot of what makes Google possible is the insane amounts of cash Search throws off.
Perhaps one of their more successful chat clients, but that's all I can think of
The really annoying ones are the ones they buy, and then shutter because they can't make enough money on them fast enough.
What if the answer is "they don't"?
Okay, so I'll get Pro to try it.
I subscribe.
No gemini 4.
The most generous reading I have is that it's a mistake. But I also downloaded the PC app, and now I really wonder why I did that, it seems to offer nothing beyond the web experience and also doesn't even know its a local app. (When asked about its capabilities it always responded with reasons pertaining to it being a web app.)
EDIT: Okay, the AI plans page claims under pro - "Expanded access to Gemini 4 Argon in Google Antigravity." So maybe it is just a matter of it rolling out. Though Google has a long history of serious confusing and misinformation on those tier descriptions.
It feels so complicated and hard to know what we can use vs what hands them our codebases
Unavailable · Gemini 4 Argon is currently available to Ultra plan subscribers.
Edit: noble gases not noble taxes, though the latter would be a funnier naming scheme.
"Across Google", wonder how much that is because of an effort by Google to curb usage of Anthropic models. MSFT and Meta similarly are pushing for in-house models. It doesn't mean the models are better when you push your engineers to dogfood and restrict access to other LLMs.
(Not that I know for sure this is the case, it's more of a hypothesis).
I wonder if any of the AI safety people will begin encouraging AI researchers to swap jobs to work at a failing lab in order to keep them all extremely competitive with one another.
At this point a lot of stuff happens at the product level.
OpenAI and anthropic have distribution and data advantages now that are hard to replicate.
Every time I try to generate one it's like a slot machine, where a different part will be messed up after fixing the first.
I told it to strip screenshots from our old basic explainer video to generate modern 3D video.
Pretty happy with the result. I told it to keep the audio and match the video and it did.
I previously tried with Seedance 2.5, Flux 3, Kling 3, Gemini Omni without much luck.
On safari the page doesn't scroll very well on the first section
It still thinks Charlie Kirk is alive, though.
But on December 1st Argon doubles in price - so it will be the same price as Opus 5.5.
Likewise, you look at Artificial Analysis' general index and it cost $8,708 to run with Opus and $2,407 to run with Argon so even if Argon's token price doubles, it won't be the same price as Opus.
Sonnet is cheaper than Opus per-token, but in their coding tests Sonnet burns through enough tokens that it's 7.5% more expensive.
So much depends on how token efficient something is and whether it gets you a good result or not, right?
Plus, if one is using a plan instead of paying token rates, what does my $200 get me from OpenAI, Anthropic, or Google?
It just feels very hard to compare pricing given that the number of tokens used can vary so much. In Artificial Analysis' coding tests, Sonnet used 7.1x more tokens than Sol. Sonnet and Sol might have the same per-token pricing, but there's a huge difference in the number of tokens used.
This was hardly a consideration a year ago, when many model tasks could be tested using a single prompt and it was hard to spend more than a dollar.
Which one is it?
e.g.
X: The Parameters
Y: The "System"
Z: The "Ongoing Grind"
Another quite unpleasant tick was that if you specify a peculiar term in your prompt as a conversational shortcut, it would keep repeating that throughout the conversation. e.g. "...What is a bitter lesson pilled way to do this?", then you'd see it will quote "bitter lesson pilled" at like 7 places in its response. This one seems to have eased off a little.
That was a long wait.
What’s the point lol. I’m not subbing to ultra to have it
"Hans, we're doing a new point release! What isn't crossed out on the periodic table yet?"
Start preparing Gemma 5 release.
I have found that Gemini often claims it did something, but didn't actually complete the task.
I just had 3.8 Flash do a major refactor -- that never should have needed to be done in the first place -- and switched to Argon 4, and had it review it.
Almost everything had been stubbed out by 3.8, and none of it was properly integrated into the codebase. :facepalm:
fyi: argon is an interesting choice of name, coming from the Greek argos meaning "lazy or living w/out labor".
Gemini Live vs. Gemini Lite vs. Gemini Pro vs. Gemini Flash vs. Nano Banana Lite (that one apparently has no Gemini prefix?)... Please, just pick a metaphor and stick with it without wildly mixing reasoning modes, model sizes, and input/output modalities in the model name!
Now that needed to be done for some time!
Regardless of if an AI helped or not, regardless of whether that was Google's or some other AI or not (though Argon does sound pretty cool!), regardless of whether an AI cut the time from months to weeks or not, refactoring Chrome -- decomposing it into individual clearly demarcated non-tightly coupled software parts with core webpage handling at the center -- that needed to be done for some time!
Now all of that being said, Gemini 4 Argon does sound pretty cool, and we congratulate both teams at Google -- the team that created Gemini 4 Argon, and the team that refactored Chrome with it!
But all of that being said, while the advancements made for Gemini 4 Argon are important and impressive (and they are!)... refactoring Chrome properly (IMHO) is slightly more important and impressive!
(You know, one man's humble opinion! That is, I am not the guy who is responsible for awarding performance-based bonuses at Google at the end of the year, so take everything I say with the proverbial "grain of salt"! :-) I am probably biased, one way or the other! :-) )
God knows why the server different things to the same people on different interfaces
I never touch 3.8, it's awful. Curious to try this, but I've yet to use any Gemini that is good for agentic work.
Also, unrelated but this made me look at Artificial Analysis to see where it lands on Cost per Task, and it ranks Opus 5.5 at almost double Astra 6. That does not align with my experience.
Google...why. You keep getting so close but constantly fumble at the last moment. I can use Opus 5.5 and Sol/Astra on the $20 Anthropic and OpenAI plans.
You are already playing from behind. You need to GET YOUR MODELS INTO MORE PEOPLES HANDS by any means possible. Continuing to make it difficult to use your products DOES NOT HELP.
This model might be great but the majority aren't going to subscribe for $100/month just to try it.
The fact that multiple companies can breach any benchmark means there is no ambiguity in terms of how to build AI, its just a matter of time.
Certainly Meta and Google are benchmaxxing to please public markets, but it still proves the above
I wonder how much is actual improvements in the model and how much comes from better harnesses. I'd be willing to bet that the combination of better harnesses + the models being trained around agentic workflows (i.e the harnesses) has made it significantly better in many things, but it's hard to find other improvements imo.
A more intelligent model would not default to spaghetti throwing, it would pause to consider the problem fundamentals, the accuracy of assumptions, and ask questions that remove ambiguity. This however does not lend itself to increasing token sales while prices remain inflated from market and brand dominance.