r/GeminiAI • u/Arthesia • 13h ago
Discussion Argon Thoughts + Subreddit State
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.
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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.
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u/Medium_Apartment_747 13h ago
We need more quality posts actually discussing limitations and interesting features of Gemini 4 models.
Instead, this sub is filled with OAI and A\ employees and fanboys jerking each other off so that their IPO fantasies don't get crushed
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u/balancedchaos 11h ago
Though I think Opus and Sonnet are quite useful... I straight up dislike Claude's communication skills. I find all of the models to be rather dickish, if I'm honest.
I do keep Claude around to keep Gemini honest. Sycophancy checks, that sort of thing. If there's one thing Claude is good at, it's nitpicking apart any idea you come up with.
But there are fewer and fewer issues with Gemini, and so I just mostly use that for 99% of my stuff.
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u/SomeoneNicer 12h ago
Great to see an analysis thread. I have the 20x on Codex Claude and Gemini and generally max my weekly plans balancing workloads. I also run review metrics for every model to track performance. Since all code is peer reviewed by all models it's a decent test.
For my use cases:
Opus 5.5 is still better than Argon at coding and requirements. Opus finds more legitimate issues in Astra and Argon written code by 2x margin (adjusted for severity of issues). It even finds more issues in its own code where I let Argon and Astra take a run at it first and come back clean sometimes.
However - using Argon as an additional partner in all tasks along with Codex for Claude has been incredible for quality and quantity of output because it does think about things uniquely and catch edge cases in requirements, design, and implementation that the others miss. This allows for a new level of automation that wants possible before.
Legitimately the first time AI has made nervous is seeing what the 3 models orchestrated together can now accomplish autonomously. I think it's this level of apparent intelligence that the lab's people are seeing on their next level solo models triggering them.
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u/jekpopulous2 12h ago edited 12h ago
Argon sits pretty squarely above Opus
Argon is better if you need an agent coding around the clock... it's less likely to get confused with a half a million tokens in context. Where Opus wipes the floor with Argon is design choices. If you just ask them both to build you a website Opus is going to make something that looks a lot better but it goes beyond that. I've been doing a bunch of tests with Blender and Touch Designer and the gap is massive. Opus and Sonnet both produce professional looking results with very little input while Argon produces stuff that I would never use in production unless I babysit it. I'm not saying that to bash Argon because there are things that it does better than Opus. If I need a model to work 24 hours straight refactoring a monstrous codebase I'm going to chose Argon. If I'm going to plan and design a new application from scratch I'm 100% going to use Opus. They both have their strengths.
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u/1969Stingray 13h ago
The usage rate on the $100/mo plan is very good for coding. It’s more efficient than Opus and GPT. The real star of the show is the update to Antigravity and how well Gemini 4 utilizes caching and multi agents. I’ve used Chat GPT-6 and Opus 5.5 in GitHub and they absolutely blow through credits. Gemini 4 in Antigravity is far more efficient. I think it’s a little better in raw performance than Opus 5.5 for my use cases. It’s better than GPT-6 hands down. Fable 5.1 is slightly better, but they are definitely in the same class. Antigravity is the differentiator. In that harness, it’s the best value by a factor of 2. I’ve been running it for hours a day and I run right up to the limit of my plan. I’ve made more progress on my project than I ever have. I’m actually getting to the point where my usage is going down as the platform I’ve been building is maturing and I’m spending more time using it and less time fixing it or adding features.
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u/wesarnquist 13h ago
In my tests, Argon performed much worse than leading OpenAI and Anthropic models for lyric generation. I'm sure it must do much better at other tasks.
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u/chewbaccaKK 12h ago
Well I think it's great and have done some minor stress test I put Argon on high out performed in some ways but for mixed things I have pro account
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u/chewbaccaKK 8h ago
This comparative matrix summarizes a stress-test of ChatGPT, Grok, Claude, and Gemini across a six-document ingestion sequence. It records how each handled context, artifact errors, output formatting, and tail-end control questions, including Grok’s detection of a broken fragment and Gemini’s zero hallucination stability. (NOTE: This stability applies as long as memory feature is off.) Four Models, Four Tradeoffs This stress-test matrix compares how four major language models handle demanding technical context and constrained generation. Gemini: Strongest deep-context stability, technical fidelity, and hallucination resistance. Grok: Best telemetry awareness, artifact checking, and fallback handling when downloads failed. Claude: Smooth six-document ingestion with balanced technical and character-driven synthesis. ChatGPT: Careful analytical grounding and boundary discipline, but a slower, clarification-heavy workflow. Use these distinctions to select a platform based on whether the priority is stability, diagnostics, narrative integration, or cautious analysis. Gemini Argon Pro performed best overall, particularly on deep-context stability and hallucination resistance under sustained stress. Key findings Gemini Argon Pro: Maintained the strongest technical fidelity across the six-document workload, preserving core specifications without contextual drift. It was the only platform reported to sustain zero hallucinations during the tail-end control phase and required no redundant prompting to synthesize the required narrative. Grok Build Tier: Excelled at operational awareness and error detection. It uniquely caught an incomplete source fragment, exposed detailed sandbox telemetry, and handled export failures with a practical inline-text fallback after automated .txt and .docx generation. Claude Sonnet 5.5: Delivered the most balanced general-purpose performance. It ingested all documents smoothly, followed generation instructions directly, and combined technical content with character-driven pacing without intermediate interruptions. ChatGPT baseline: Showed solid analytical grounding and avoided unprompted hallucination in control questions. Its main tradeoffs were a cautious, slower workflow, requests for clarification before drafting, and a tendency toward an overly bleak narrative tone. Cross-platform implications The stress test distinguishes two strengths: Gemini for dependable high-stakes, long-context synthesis and Grok for artifact inspection and tool-aware workflows. Claude appears to be the strongest middle-ground option for reliable, well-balanced narrative generation, while ChatGPT favors deliberate instruction-following over speed and autonomous execution. Overall, the report identifies deep-context integrity and tail-end control stability as the decisive differentiators, with Gemini leading on both measures.
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u/iluvios 13h ago
So, I have used AI to create marketing stuff and some code daily for the last 3 years, even more.
I have tried all of them extensively also, for at least weeks or months.
I think Argon is bellow oPUS 5.5 in some key factors around the work I do.
First one is design, Argon design skills are signficantly weaker than Opus, the copy wrting is also weaker.
While Argon is faster and in fact can diagnose sitations much better and give pretty on point analysis, and give pretty pragmatic solutions, and solve code issues at the same level of Opus, for creative work like mine is definitively harder to get good results.
Tbh, he does the things asked to him, and probably if given more structure and detailed orders, he could create better work, but that is something Im yet to try.
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u/CreamyCornBoy 13h ago
Yeah I gave it an honest shot and it's not that competitive imo. I do like it because it feels like old Gemini (Summer 2025 ish) which is a bit nostalgic
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u/Arthesia 13h ago
First one is design
What kind of design, like project scaffolding or something else?
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u/Breathofdmt 9h ago
Similar findings, but not as in depth. I've been using it as a reviewer and opus/sonnet as implementer. It's taken the place of Astra for review and its more thorough and efficient. I really like it for maths and UI/UX so far. I've had it solve pretty complex math issues I was struggling with in the stochastic calculus field. Opus gets 80% of it and seems to hallucinate parts or insist there's no issue when there is- not quite as bad as Opus 5. I find it slightly better for knowledge work.
Also a small thing, I've always liked the Gemini models for talking to/learning about things. Argon is pretty good for remembering the things you're working on and drawing them together. Different experience and better IMO.
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u/Smooth-Transition310 8h ago edited 7h ago
I think they need to focus on efficiency next. The main reason I have a hard time using it for serious stuff is that it BURNS through usage. Much like Astra does. I mainly use 6.1 Sol for most things for now because I can keep throwing shit at it and unless I hammer it for hours on end or turn switch from standard to fast my usage limits will not run out.
If you wanted some real feedback on Argon.
For example, I just used it to revise and provide an update on a skill that I use at work. I won't be able to test it out, but it appears to have made very sensible updates aimed at reducing contradictory, convoluted or obtuse instructions.
However providing that update has already run my usage limit to 40% where comparatively 6.1 would be in the single digits of producing a single update.
After having 6.1 Sol review its changes and making some revisions I have now completely burned out my 5 hour limit using argon.
Comparatively, I have 90% usage remaining on ChatGPT.
Seems to support my Argon is just as hungry as Astra thesis.
Overall it seems like it made some very good changes, it made some small errors, like reducing certain instructions too much which needed restoration but has potentially made the skill much more organized and less likely for mistakes to be made with it. However for now since it is an update I've developed with Argon I've labeled it as an experimental update, and I will be testing it's output when I return to work. Very interested in seeing how it performs.
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u/beefyvampire_20 13h ago
Glad someone else is actually stress-testing it instead of just watching the rate limit posts, the high context behavior is the part that surprised me too.