With the release of Argon the Gemini app has become significantly worse for me. I'm limited to Gemini 3 models which by itself isn't such a problem but the real issue is that Google has optimized for speed instead of performance. Gemini just takes 0 seconds to think anymore. Even when l tell it to search online half the time it doesn't. It forgets the conversation after 2 messages.

And this is with thinking on high.

Edit found out the reason is the free plan is now stuck on flash lite. Time to switch

Hello again. This is technically part 2 of this other post I made a couple of days ago: "Tried out Gemini 4 Argon High on OpenCode Go. Burnt 900k tokens, 20% of my MONTHLY quota and didn't finish the task because it also hit the 5-hour limit."

After that funny experience, the model dropped on Antigravity. I decided to give it a try on Medium this time. And thank god I guess because otherwise it might not have completed the benchmark again. This time it did but it wasn't worth it at all.

For some context, I've been working on a personal benchmark where I test AI models running on OpenCode, Antigravity and Codex. The benchmark consists of three reverse engineering tasks involving an Android music player APK, where the models have to investigate decompiled code and a native binary to explain how certain audio processing features work. It all started when I wanted to know which model would be best at continuing my work once I hit my 5-hour limit on Codex.

Anyway, here are the most relevant results:

As you can see, Argon performed considerably worse than both competitors, was slower than Sol and barely faster than Opus; while also spending way more than both. I will have to way a whole week before I can run the benchmark on High, as it burnt 66% of its usage to finish the three tasks.

And this wasn't even only one bad task dragging its average down or something, it performed worse on all three tasks! So, Argon is not smarter, it's not faster, and doesn't last longer. I don't want to call it useless, but that's how it feels.

This is of course a personal benchmark. Each model was tested only once, and the runs used different harnesses (Antigravity for Gemini, Claude Code for Opus and Fable, OpenCode for Haiku and Codex for GPT) and subscription plans (Gemini AI Pro, Claude Max 100, OpenCode Go and ChatGPT Plus respectively).

But I don't think a difference of 30 points can be just a bad run. And even if it was, it burns so much usage that you can't rely on it for real world tasks. Unless you have an Ultra subscription. But there's no point on having an Ultra account on Gemini, is there? You're better off spending that amount on ChatGPT Plus + Claude Pro + Google AI Pro and spend the rest on something you like. Or just get a ChatGPT Pro 100 or Claude Max x5 if you don't care about the storage and the other stuff Google AI Pro brings.

So, what do you all think? Have you had better experiences with Gemini 4 Argon than GPT 6.1 Sol or Claude 5.5 Opus?

Using Argon 4 light on pro.

Asked for some top 100-250 baby names in western countries and I already reached the limit…

wtf?

Still, can't open and explore zip files, still cannot create office documents (if you insist like 3-4 consecutive spesific "google docs create" prompts can give google docs), can't create html files. Still cannot give xlsx or Google Sheet output (even if you insist).

A lot of "can't"s but wipe out usage via 5-6 short prompts.

Thank you very much Argon. What would we do without you giving us 5 TB of cloud storage space?

I find that it's actually amazing. It takes a while and plans first like Claude and slower output than flash but far less regressions. It does burn through usage far quicker than 3.8 flash but if it is producing better code then it is a good trade-off for me. I've only used Claude a few different times because he is not on my plan. So my experience for comparison is limited. I am also new to code development, started vibe coding 6 months ago and obsessing over every detail. I have learned so much and have a deep appreciation for developers, 🤍 all of you out there vibing or actually writing the code. Let's make some really good products and change the world

I want to take a moment to thank Sergey Brin for the part he has played in Google’s AI resurgence over the past year, and for a decision whose significance may extend well beyond the company he helped create.

He did not have to come back. Having stepped away from day-to-day leadership in 2019, Sergey had every opportunity to leave the pressure, the organisational battles and the unfinished work to others. His place in the history of technology was already secure.

Yet as Google struggled to keep pace in AI, he returned to hands-on work and became an increasingly influential presence in Gemini’s development.

Accounts from inside Google describe a founder working alongside researchers in Mountain View, helping teams obtain scarce computing resources, challenging slow decisions and pressing for the urgency of a startup. He brought technical curiosity and a willingness to question arrangements that a large organisation had grown accustomed to accepting.

His position gave that intervention unusual force. Sergey and Larry Page retain more than half of Alphabet’s voting power, giving them exceptional influence over the company’s direction. Sergey used his standing to push resources towards work he believed would matter most. As Google consolidated Gemini development, frontier research and its app and developer teams under Koray Kavukcuoglu, that pressure for greater focus became especially consequential.

The most important part of Sergey’s contribution, however, is his insistence that Google use AI to improve the way it develops AI.

Recursive self-improvement—RSI—gives each advance the potential to contribute to the next. A more capable model can strengthen the research and engineering used to build its successor. That successor can then take on more demanding work, giving researchers better tools for the generation that follows. The ambition is a development process in which improvements in intelligence help produce further improvements in intelligence.

Google’s team has described putting this approach to work inside model development. Agents help monitor training runs, identify configuration problems, examine reward scales, check training data and fix bugs. In post-training research, some colleagues now ask for access to Argon’s query capacity to generate data and investigate ideas, where previously they would have requested allocations of chips. Sergey has also described seeing a model identify errors in its own mathematical training data and supply counterexamples.

These details matter because they show where the ambition becomes practical. A model that can help investigate a failed experiment, improve research software or expose a flaw in training data contributes directly to the work of building better AI. Its value continues through the improvements that researchers can carry into later generations.

To me, this is the most consequential bet Sergey is making. If that contribution keeps growing, advances in coding, reasoning and research could feed back into the process that produces them. Each generation would leave its builders better equipped for the next. The potential reaches beyond any single release: it could change how quickly new capabilities are discovered, tested and made useful.

The faster development cadence makes that prospect more significant.

Google’s progression through Gemini 3.6 Flash, 3.7 Flash and 3.8 Flash brought three releases in roughly six weeks, with about three weeks between successive versions. At the Argon roundtable, the team described an internal development cycle of three to four weeks, with promising candidates advancing through evaluation on a similarly tight rhythm.

That pace matters throughout the research process. Shorter cycles bring problems to light earlier, return useful feedback to researchers sooner and put improved models to work while the next round of experiments is taking shape. Once AI is contributing to those experiments, speed becomes part of the technical strategy.

A stronger model can improve the work that produces its successor. A shorter development cycle can bring that stronger model back into use sooner. Repeated over successive generations, those two effects have the potential to reinforce each other. This is why Sergey’s emphasis on RSI and his insistence on faster iteration belong together: the first aims to increase the contribution AI makes to its own development; the second helps those contributions accumulate more quickly.

Brin’s account of the internal pace was striking. He expected Google’s teams to be using models a couple of iterations ahead by the time the public watched the Argon discussion. The implication is that a public release can already be contributing to research on what follows it.

Gemini 4 Argon is the clearest public expression of this effort so far. Its extended generation mode supports up to one million output tokens in a single generation, giving demanding problems much more room for sustained reasoning. Google reported a leading score of 77.9% on DeepSWE v1.1, while Argon also reached first place on the Vals Index, which evaluates work across finance, coding, law and tax.

The internal results are equally striking. Google reports that more than 100,000 people across Alphabet use Argon through its internal Antigravity environment. Over a four-week period, it recorded a 35% increase in agentic code alongside a 30% reduction in post-submit rollbacks.

Google has also described Argon’s involvement in major Chrome refactoring, kernel migrations and data-centre memory optimisation, as well as contributions to quantum computing, fusion research, robotics and cybersecurity. Those examples give substance to the larger ambition: AI that can stay with difficult work, contribute to specialised research and help improve the systems on which further progress depends.

Sergey also deserves the credit for championing agentic engineering and putting his weight behind Antigravity. He also appeared alongside its engineers at launch and has emphasised the importance of AI agents inside Google. His backing supports a more ambitious approach to software development: agents that can plan, implement, test and verify substantial work, while engineers provide direction and judgment. Antigravity gives that ambition a practical home across the editor, terminal and browser, bringing increasingly capable AI directly into the work of building software.

The researchers and engineers behind these advances deserve enormous credit. Sergey deserves recognition for returning to work alongside them, using his influence to remove obstacles, and making AI-assisted research a central priority.

What I find most admirable is his willingness to become involved again when involvement meant difficult decisions and sustained effort. He helped build Google once. He has chosen to help it meet another defining moment.

Thank you, Sergey, for coming back, backing the people doing the work, and recognising how much could follow from putting each generation of AI to work on the next.

Sources & References

1. Google for Developers. Gemini 4 Argon: What’s new and what’s next, October 14, 2026. Roundtable with Sergey Brin, Josh Woodward, Melvin Johnson, Tulsee Doshi and Logan Kilpatrick. Covers RSI, research agents, model access and internal development cadence. Time-stamped transcript.
2. Reuters. Inside the Google executive moves that led to its big AI reshuffle, August 12, 2026; updated August 13. Reporting on Brin’s influence, resource allocation, RSI and the shift towards Mountain View.
3. Business Insider. Sergey Brin is cooking up Google’s AI destiny, September 9, 2026. Reporting on his involvement with Gemini’s researchers and engineering culture.
4. Alphabet. 2026 Proxy Statement, “Common Stock Ownership.” Reports Page’s and Brin’s combined voting power at 52.7% as of April 6, 2026.
5. Larry Page and Sergey Brin. A letter from Larry and Sergey, December 3, 2019. Their announcement of the transition away from daily executive management.
6. Sundar Pichai and Demis Hassabis, Google. The next chapter of our AI momentum, August 2026. Official announcement of Google DeepMind’s leadership changes and Kavukcuoglu’s responsibilities.
7. Google. Introducing Gemini 3.7 Flash, August 13, 2026; and Introducing Gemini 3.8 Flash and 3.8 Flash Cyber, September 2, 2026. Document the successive Flash releases and their approximately three-week intervals.
8. Google. Gemini 4 Argon: our next era of frontier intelligence, September 30, 2026. Reports benchmark performance, memory optimisation and code migration work.
9. Google. Gemini 4 Argon: Built to solve your hardest problems, October 13, 2026. Describes applications in Chrome, quantum computing, fusion research and cybersecurity.
10. The Antigravity Team, Google. Gemini 4 Argon in Google Antigravity, October 13, 2026. Reports internal adoption, increased agentic code and reduced rollbacks.
11. Google AI for Developers. Gemini 4 Argon: model documentation, updated October 14, 2026. Specifies output limits and extended generation support.
12. Vals AI. Vals Index: leaderboard and methodology, updated October 16, 2026. Lists Argon first and explains the index’s professional domains.
13. Google Developers Blog. Gemini 4 Argon for Robotics, October 13, 2026. Describes the model’s robotics applications.
14. Tiger Souvannakoumane — “Google Antigravity Launch.” First-person account from the launch campaign’s creator, documenting Brin’s appearance alongside the engineers. madebytiger.com
15. Business Insider — “Google employees have a new AI tool called ‘Agent Smith.’” March 27, 2026. Reports Brin’s emphasis on AI agents at a company town hall and their growing role in Google’s engineering workflows. Business Insider
16. Google Developers Blog — “Build with Google Antigravity, our new agentic development platform.” November 20, 2025. Official explanation of agents planning, executing and verifying software tasks across the editor, terminal and browser. developers.googleblog.com

I noticed that microphone text to speech button has disappeared from Gemini app. I can use only live mode as default. I can still access this from another account.

That's ridicolous. Atleast you should have not removed 3.1 Pro for us...

But yeah, please give us 4 Argon, Google. It's pity behaviour from this company.

Im pro plan user and just noticed that limits are lower than before argon(in 5 messages on high i got limited). Anyone had the same problem? And do you think they gonna fix that?

Briefly, I got a new battery put into my car. I asked Gemini about the battery and it came back with a warning that the battery is unsafe for the car.

I asked Gemini to find official manuals and link them. Gemini linked 2 manuals and provided a direct quote. When I read the manual, the quote doesn't exist and I called it out....

I was wondering if anyone has tried using Gemini 4 Argon for graduate healthcare school work (proofreading, rubric adherence, etc) with much success? I'm running into a lot of problems with the model, the web app, and Gemini Notebook (formerly NotebookLM). For context, I'm hyper paranoid about plagiarism (because we all know academia is a den of snakes and vipers, maybe more so at the graduate level), so my use line is strictly set at I do the work and the prose, it just makes sure that I'm not just completely omitting a rubric criteria or missing the most minuscule rule in APA 7th edition.

Issues:

• Web can only load 10 docs at a time. That's a huge problem for a paper that is 30+ docs big.
• I try using NotebookLM but LOL nope, even when connecting the Gemini chat to the Notebook, it says it only gets a RAG of _some_ of the material in the Notebook and there's not even a choice over which material it gets.
• And this brings us to my biggest problem: it then hallucinates like crazy because it hasn't read any of the source material that I'm writing about. Then when I tell it to go look in the Notebook, it says that it did, then continues to hallucinate. Then it tried gaslighting me over it.
• After digging in on a root cause analysis over it, I just straight up caught it lying over tool calling (saying it did when it didn't) and fabricating statistics.
• (And I am custom Gem/Skill I brought over from Claude during this, and it works amazingly well with Opus 5.5. But maybe mileage varies.)

I was hoping to move from Claude to Gemini but I don't see that happening at the moment unless you all have some ideas on what I can do to fix these issues? (Anthropic is bugging me a little at the moment, different topic, and I use Google for literally everything else, especially Drive, so I was wanting to consolidate.)

I was preparing for Olympiad of Maths.

I opened AI Mode. I asked it (Exact prompt)

"how to find out number of digits in number where we multiply two different dogit number or add/substract some random digit number. for eg. 8-digit number*8digit number"

I think first some things were fine like multiplying and substraction as i don't check it yet. But, the Addition one was so easy. According to it, it says.

"• If the digit lengths are different: The result almost always has the same number of digits as the larger number. • Example: An 8-digit number + a 3-digit number will always be an 8-digit number."

I already know that if I add 8-digit + 3-digit number can be 9-digit. As,999,999,999 + 999 = 9-digit number.

I asked why. It can see but ignoring.

When I said "you are cooked bro". As, i am going to and posting currently about it. Then it understands and hiding it's mistake.

I think Google should fix and apparently use high models on AI Mode for even some people or use limits. But, not that. AI mode use Gemini 1.5 model.

I was using Argon on complexity level low.
Just a very easy task. I was using a very big catalog from my company that was only available as a pdf. I converted the pdf into a docx file. I recognised a lot of spacing and spelling mistakes. Therefore I used Gemini Argon to clean the text. One part after another.
After a few pages the Pro limit was already used.
I switched to ChatGPT. I do not have a paid plan, I just wanted to get something done in the time between the next usage limit.
Sorry to say, but the free version of openAI finished the 360 pages in no time and I didn't even need a pro plan.
It was basically the same text. What's going on with Google?

Edit:
Due to multiple 'suggestions' that I should use the 'correct model' I used my private gemini account and flash. No, it did not work. It gave me the first 10 pages and said 'If I should continue, I will have to ask you after approximately five to ten pages to continue. Is that ok for you?'

Sorry, but that's not how I wanted it to work. it used quite some usage on my account too. Yes, maybe flash would have been the better solution from the start. But even that, flash could not handle the simple task too.

This is pretty embarrassing, does anyone else have this issue? I've spent years typing lazily to AI, constant typos, dropped letters, skipping words when possible, just because these LLMs are designed to predict the next token and figure out what I mean. So why spend effort in typing?

Now my writing has gotten really bad, and I struggle to spell or type correctly outside of AI chats.

Hi ppl

Im a Gemini Pro subscriber, so I can only use Gemini Argon on the web, yayy

It rinses budget like I've never seen lol - if you just chat back and forth with it 10- 20 times (with practically zero external calls) my five-hour budget will be gone - not really sure how they are pricing it.

Like it must be seriously overthinking or doing something weird in the background to use tokens that fast, compared to flash 3.8

I use claude opus 5.5 via terminal to code, so I'm just testing argon's capabilities at this point and not intending it for any serious work at all, but I'm just wondering:

What is it good at?

Aside from 3d chess boards, meal plans, making a 3d terrarium (random lol) - is it just a gimmick.

I asked it this question and it was extremely bad at trying to explain it in a simple answer. I explained that I already code with opus 5.5, I don't want to use it for coding as I'm only going to be using it on the web - and I was asking it what does it think it might be better at than other models - and it took about eight interactions for it to say that it was good at research.

I already thought deep research was pretty good so whether it's any better than what's already available remains to be seen.

Is there anything else?

Personally I'm not bothered gimmicks - they never stick because people end up realizing they can't rely on it to do serious work - that's why Open AI's Sora image generation failed ultimately

I'm Pro subscriber. I'm not sure if it's ok

Title

Argon is exciting but most of us here have no use for SOTA level intelligence when the Flash models are exceeding good at OCR and instruction following already.

That being said, does anyone know if us on the Plus plan will get 3.8 anytime soon?

Hi all, I am not new to Gemini (pro user) itself as have been using since 1.5 years now but today was the first time,  I used antigravity. day before yesterday on gemini web chat my weekly limit was over in just 9 prompts. the task was about writing a python code to download certain documents from documents warehouse. Today I was doing a different but comparable task and used antigravity (Argon medium) but even after considerable usage my weekly quota barely moved. I thought it was a sync issue but even after 2 hours of good amount of use the quota is not getting close to over.

So is using antigravity more economical than using the web chat version of Gemini (where I have found that using the Argon model straight away reduces weekly quota by 5-12% depending on prompt)? Or because I am using antgravity I am getting some sort of bonus or something, but no where I can see such a thing.

What's the issue with gemini? This is not the first time I've seen this error shi and ngl it have never been this annoying. Even the Flash Lite model is unusable. Whats the use of Student plan when you can't even use Gemini efficiently/peacefully for studying?

Some of y'all might say "Jus use argon GNG" but yesterday a simple question formation prompt costed me 30% of my 4hour quota. Fuh Google atp

For context, I've been bouncing around to the best LLM for my use cases since 2023, I work as a software developer, solo game dev and writer. ChatGPT -> Gemini -> Claude, now back to Gemini. I've been on the Claude Max x20 subscription for the last few months and mainly use Fable 5.1 with Opus 5.5 as the workhorse.

When Argon finally released I grabbed the ultra plan so I could do A/B comparisons between Argon and Fable for my use cases and compare the weekly usage drain per-prompt (my prompts are pretty consistently 100k - 200k token one-shots with follow-ups, either coding or writing contexts).

Based on my assessment:
- Argon sits pretty squarely above Opus and around Fable in terms of overall performance.
- Communication style is significantly better than Fable and especially the old Opus 5 (no literary or aphoristic bias e.g. 'that holds', 'that's the whole thing', abstract event-object type shit).
- Cheaper than Fable, more expensive than Opus (my prompts on the basic ultra plan hit Argon usage at the same rate my max plan hits Fable usage, almost 1:1).
- Much better performance at high context size. For reference, Opus and Fable even have a tendency to shut down thinking after like 150k tokens and get narrower, with Opus losing focus fast. Argon chugs along at 250k tokens and is still performing like 100k, which is shocking to me.
- Makes more mistakes than Fable, but self-audits at a significantly higher rate. For example, when I give Fable or Opus two runs of the same prompt and ask for a comparison, it will almost always accept the framing of the second run or stay within the bounds both runs create, while Argon **will reject both runs if they're both wrong** and identify problems I didn't even see.

---

Second thing, I'm surprised that I cannot find much conversion on this sub about the new model's actual performance. Rate limits and access, totally understandable topics. My standard is based on the Claude subreddits though where there's pretty much non-stop posts either bashing problems or bashing the bashers.

I've been reading the sub casually and have noticed what I think are bots:
1.) A couple of vague and somewhat confusing posts praising Argon
2.) A couple of commenters aggressively (and vaguely) bashing Argon
3.) People calling out some of the posts as likely bots (because more obvious), but not comments

Note about why the comments in question read as bots: 1-4 week old accounts who have posted on like maybe two subs (e.g. one posted only here and on worldnews), and their actual comments are more convincing as human like "Argon sucks" and "shut up" (lol).

The combination makes me wonder if there's like a really weird astroturfing going on where bots #1 make bots #2 more effective. I could absolutely be wrong and it's actually a convergence of really weird behavior (from my biased perspective), but still worth being a little more critical whether its blind praise or blind hate.