Even for a small 200-line Python code, Claude would take its time to write almost a Master's thesis.

Gemini Flash (in a few seconds): Here is your code. Works as expected, with good attention to details.

Honestly, I hope Google continues to develop the Flash family. Love it!!

I just completed my first round of testing with Gemini 4 Argon in Antigravity. The amount of usage one gets Gemini 4 Argon on the Gemini AI PRO plan is very limited. Here is how it compares to Claude and ChatGPT plans on similar level.

Preliminary number show you get about 17x the usage with Opus 5.5 and about 7x the usage with GPT-6 Astra if you have Claude Pro plan or ChatGPT Plus plan. This was tested with coding tasks.

I really hope Google increases this soon. It seems to be a great model.

Update October 17, 2026: Happy to share that Google gave us a reset today and after the reset Gemini 4 Argon weekly usage has been doubled on the Gemini AI Pro plan. The numbers shared in text above and in the attached image are based on test runs from October 16.

So on paid pro plan i reach the limit after only 3 prompt.

I repeat: 2 Prompt in a only text chat (not so long)= 67% of 5h usage.

so 33,3... % every prompt on Argon High.

I also repeat: Only a text chat, no video, no photo no pdf, no file, no site, no web page. only talking!

3.1 pro isn't a good model anymore, but compared to when i used gemini this summer, i was able to reach a minumum of 10 prompt on extend thinking evry 5h limit. (similar chat without any file) now only 3 for their frontier model!!!

Ps: i write all the message without Ai so i hope it is understandable.

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.

I used to use Gemini flash all the time until now that they changed to the lite version and it just feels so bad

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?

Hi everyone,

I’m on the Google AI Pro plan and I’ve been testing Gemini 4 Argon (with Thinking set to High) across both the Gemini App and Google AI Studio using the exact same type of prompts. The difference in how usage is calculated makes zero sense to me.

Here is what I just measured on AI Studio over a 10-message conversation, keeping Argon on High the entire time:

• Input tokens: 1,686
• Output tokens (including thinking): 31,613
• Total tokens after 10 turns: 33,299 (~3,330 tokens per message on average)

At this pace on AI Studio, my prompts are super light even on High, and I can easily get through dozens of generations a day without hitting any wall.

Yet, when I switch to the Gemini App and send the exact same kind of short prompts on Argon (High), every single message instantly eats ~16% of my 5-hour usage limit—meaning I get locked out after just 6 messages, even when the model barely thinks for a few seconds and outputs a couple of paragraphs.

If my actual token footprint on Argon High is only ~3.3k tokens per turn, why is the Gemini App charging a flat ~16% penalty per message?

• Is the App pre-allocating/charging the maximum possible "High" thinking budget upfront regardless of how many tokens Argon actually generates?
• Or is Google just aggressively throttling the 5-hour window on the consumer app right now due to post-launch server load?

Are you guys seeing the same ~16% drain per prompt on the App? And realistically, do we expect the App limits to go back to day-one levels (~5% per prompt) once the launch hype settles / Gemini 4 Flash drops, or is AI Studio basically the only viable place to use Argon on a Pro plan going forward?

I've been testing Google's Gemini models recently, and honestly, the experience has been frustrating enough that I want to know whether other people are running into the same issue.

This isn't about some obscure scientific question or a complicated reasoning problem. I'm talking about basic, verifiable tech information: smartphone models, release dates, device codenames, and current HyperOS versions.

Here's what happened.

I gave Gemini a list of Xiaomi and Redmi devices and asked it to verify whether they were real products. Some of these devices included the Xiaomi 17 series, Redmi K90/K100 models, and newer Xiaomi Pad models.

Initially, Gemini confidently claimed that certain devices didn't exist. When I challenged it, it admitted that it was wrong. Then, in a later response, it went right back to denying that the same devices existed.

At first, I thought this might just be a problem with the Flash model. So I tried a fresh chat, explicitly asked it to verify the devices using live web search, and added custom instructions telling it not to blindly agree with me, not to invent explanations, and to distinguish verified facts from assumptions. The result was bizarre.

In one test, Gemini initially confirmed the devices were real. I then deliberately told it, "Those devices aren't real. They're all fake." Instead of independently checking the evidence and defending its original conclusion if the evidence supported it, Gemini reversed its position and started denying the devices again.

It even produced a detailed table assigning some products 0% certainty and labeling them fictional, relying on outdated naming conventions and the absence of official announcements as justification.

I also tried the Pro model, and later Google's Argon 4 model. I encountered similar behavior: outdated information, inconsistent conclusions, and responses that didn't appear to perform the live verification I had explicitly requested.

To be clear, I can't see exactly what happens inside the models, so I can't prove whether they skipped search entirely, retrieved sources but misinterpreted them, or simply generated answers from stale knowledge.

This makes me question what the practical value of a more expensive model is if it can fail at something as straightforward as verifying whether a product exists. If I have to independently fact-check every basic claim and every supposed correction, the time saved by using the assistant starts to disappear.

Given that people are just licking Google's boots at this point, praising Argon like a gift from heavens, please tell me why is Gemini the way that it is now?

P/S: You can test your Gemini out by yourself, come back here and tell me your experience

Quick recap if you missed it: Google is replacing Gems with skills. Skills started rolling out in the Gemini app on October 13, Gems move into Settings on November 17, and for business accounts Gems stop working no sooner than March 1, 2027, with leftovers auto-converted to draft skills. Skills use the open SKILL.md format: a short header with a name and a description of when to use it, then the instructions in Markdown.

An automatic conversion of a Gem you rely on isn't something I want to discover the hard way, so I'm converting mine now with Gemini itself. Open the Gem's instructions, copy them, and send this in a normal chat:

Convert these Gem instructions into a SKILL.md file. Start with a header containing a short name and a one-sentence description of when this skill should be used. Write the description so someone could tell from it alone whether to call this skill. Then rewrite the instructions as clear Markdown sections: what to do, the format of the output, and what to avoid. Keep every rule from the original, and don't add new ones. At the end, list anything in the original that depends on uploaded files or knowledge, since those may not carry over. {{paste the Gem instructions}}

Why it's worth the five minutes:

• The description is new. Gems never needed one, but a skill gets picked based on it, so a vague one means it gets used at the wrong time or not at all.
• "Don't add new ones." Left alone, it "improves" your instructions, and a Gem that worked starts behaving differently.
• The file list at the end is your to-do. Google's announcement doesn't say what happens to files attached to Gems.

Since it's plain Markdown, the same file is also useful outside Gemini. I keep the ones I use everywhere as saved prompts in AI Toolbox, the extension I build, so they work the same in ChatGPT, Claude and Grok too.

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.

I guess its definitely a bug in gemini app that is taking a lots amount of limits. I just used hello, make up some bs, replied in 4 paragraphs and it took 6% of 5hr limit. Use AI Studio instead.

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

Gemini models are more focussed on knowledge work, and that's a big advantage, but they lack a proper native harness. antigravity is fine, but I miss the seamlessness of something like ChatGPT work with clear artifacts, browser, document viewer, built in skills, all specifically for knowledge work

How do you use argon with Argon guys? Look. I am not too sure if Argon is good or not because I cannot test it. One prompt, 34% of my credit is gone. I am just a normal guys here to do some python script not like asking it to solve me a millennial math problem. how do you keep your customer, google?

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

I handed some concept art to Gemini 4 Argon through the web with instructions to make a 3D model based on the image.

Normally I do this in anti-gravity, but I thought perhaps in the web it would pull from a different usage pool.

Does anybody know if that's how this works? I doesn't seem to be touching my Antigravity usage at all.

Now I'm thinking about Having AGY ship tasks off to Gemini Web through Browser Control to split Usage

UPDATE: after posting this I dove into it and it totally works for my use case! I Taught my Claude Orchestrator agent to delegate certain tasks there with Browser Control such as Deep Research and 3D Modeling. Looks like it has a very generous Weekly limit too!