B 的立场站 Claude
C 比较中立
D 是写文的
E 强烈支持 Gemini

A：试过 Gemini 4 Argon 的人，你有什么反馈吗？除了那些不值得信任的基准测试和粗糙的浏览器游戏

B：这是个很棒的模型，虽然还不到作品 5.5 的水平，但很容易就能和 Astra 媲美。有传言称谷歌已经准备好了另一个更棒的检查点，如果它能被纳入 Argon 或成为 Gemini 4.1，我们将拭目以待

It's a great model, not quite opus 5.5 level, but it easily matches Astra. There are rumors that Google already has another checkpoint ready that is even better, if that makes its way into Argon or becomes Gemini 4.1 we'll see

C：“还不到 Opus 5.5 级别”其实并不是我想要的这个词，毕竟这个前沿模型还没上市......

"Not quite Opus 5.5 level" is really not what I wanted to here for a frontier model that isn't even out yet...

D：我觉得 Opus 5.5 是更好的编程者，但 Argon 是更好的写手，仅供参考

I think Opus 5.5 is a better coder but Argon is a better writer FWIW

C 赞同 D：谢天谢地，因为 Opus 5.5 写得非常棒，短文创意是我目前用得最多的 AI 应用之一。现在我很兴奋。

Okay thank god because Opus 5.5 writes really amazing and short form creative is one of my most used AI applications right now. Now I'm excited.

E：为什么有人说 Opus 5.5 比 Astra 更好？或者第 6.1 个火星日？我发现复杂工作甚至比第 6.1 个火星日还晚。它留下了太多问题，或者没有有效考虑更宏观的全局。我有一个评估流程，包含大约 5 种不同的插件和技能工作流程，以及 3-4 种系统提示的排列组合，以及 [CLAUDE.md](http://claude.md/) 全局/仓库范围。没法让它做所有事情。即使上下文调整到 50 万上限、75 万等等。我让 Sol 6.1 复习，识别出直接的核心问题，而不仅仅是边缘情况。（我同时有 Claude 和 OpenAI 的最大配置）——还有，OpenAI = 作为 Claude 至少使用 5 次......

Why do people say Opus 5.5 is better than Astra? Or Sol 6.1? I find that its behind even Sol 6.1 for complex work. It leaves so many issues or doesnt consider the larger picture effectively. I have an eval pipeline with like 5 different plugin and skill workflows and 3-4 permutations of system prompts and [CLAUDE.md](http://claude.md/) global/repo scopes. Can't get it to do everything. Even with context adjustments down to 500k cap, 750k, etc. I have Sol 6.1 review and identify immediate core concern bugs not just edge cases. (I have both Claude and OpenAI max plans) - and also, OpenAI = at LEAST 5x usage as Claude ...

E 的补充：  

Many people validate models on their ability to create a game or a frontend UI.  
很多人验证模型是否能创建游戏或前端界面。  
Problems which are quite mechanical, I've stopped writing UIs because once the design is there and you have some years in and have defined how you use your framework it felt like all the hard work was done and I just had to put in tedious text. If you've developed games you also know that most of the hard problems are known mathematical problems, AI excels at this since it's essentially reproducing a known pattern.  
这些问题相当机械化，我已经停止写界面了，因为一旦设计完成，经过几年工作，定义了框架的使用方式，感觉所有艰苦的工作都完成了，我只需要写枯燥的文字。如果你开发过游戏，你也知道大多数难题其实都是已知的数学问题，AI 在这方面表现出色，因为它本质上是在重现一个已知的模式。

I'm currently dealing with complex data problems which don't have known frameworks to solve them. That means.. no code to steal. For these problems I have the same experience as you. Any Anthropic model does half a job and doesn't even look at the existing code or specs decently. It behaves like an overconfident developer. 
我目前正在处理复杂的数据问题，这些问题没有已知的解决方案框架。那就是说......没有代码可盗。对于这些问题，我和你有同样的经历。任何 Anthropic 模型都只做了一半的工作，甚至都不怎么看现有代码或规格。它的表现就像一个自信过头的开发者。

B：Tengo el plan pro, le hice una pregunta a Argon, con nivel de pensamiento bajo y se gasto un 5% del plan de uso 💀.  
我用的是专业套餐，我用很低的思考方式问了 Argon 一个问题，他花了套餐的💀 5%。

Y no fue un pregunta muy loca.  
这并不是个疯狂的问题。

F：Yo hice unas 3 preguntas nada de otro mundo plan pro y adiós capacidad 100% consumió  
我问了大约三个问题，没什么特别的，Pro Plan 和再见，容量 100%耗尽

G：For something that isn’t a browser game demo: I run a benchmark where models play Twenty Questions. The model has to find a hidden subject (Einstein, a bike pump, the moon...) by asking questions, and separate models answer and double-check each answer. The score is how many questions it needed.  
如果不是浏览器游戏演示：我跑了一个基准测试，模型们玩 Twenty Questions。模型需要通过提问找到隐藏的主题（爱因斯坦、自行车打气筒、月亮......），不同的模型会回答并反复核对每个答案。分数是需要问多少题。

To my own surprise, it was very good. Argon solved all 30 games and tied GPT-6 Astra for first, with Opus 5.5 a bit behind. It won’t tell you anything about Rust or working from specs, but you can read every game and see how it reasons: [https://deep20bench.com/runs/BX-20261014-B-0003-official-M0034-002/](https://deep20bench.com/runs/BX-20261014-B-0003-official-M0034-002/)  
令我自己惊讶的是，它非常好。Argon 解决了全部 30 个游戏，并与 GPT-6 Astra 并列第一，Opus 5.5 稍稍落后。它不会告诉你关于 Rust 或基于规格的操作，但你可以阅读每款游戏，看看它是如何推理的：[https://deep20bench.com/runs/BX-20261014-B-0003-official-M0034-002/](https://deep20bench.com/runs/BX-20261014-B-0003-official-M0034-002/)

E：I’m on the Enterprise Plan, so far I love the results of Argon compared to Pro 3.1 which was garbage compared to Sonnet 5.5  
我用的是企业套餐，到目前为止我很喜欢 Argon 的效果，相比 Pro 3.1，Pro 3.1 比 Sonnet 5.5 差很多

When I switched to Argon, I immediately noticed how well it can handle large codebases which may even require “third” codebases that are like a puzzle. Gemini 3.1 Pro had issues here with consistency across my codebases but Argon seemed to understand perfectly what I want.  
当我切换到 Argon 时，立刻注意到它能很好地处理大型代码库，甚至可能需要像拼图一样的“第三”代码库。Gemini 3.1 Pro 在我的代码库中存在不一致的问题，但 Argon 似乎完全理解我的需求。

D：For me it is the absolute GOAT in every day chat, HUGE parameters for sure becuse it is rich and fun to chat with in different languages, finally not feels like im interacting with a robot. For coding or scripts i am still using gpt6 and opus 5.5 still but their chat is just soulless.  
对我来说，它是日常聊天中的绝对最强，参数非常大，因为它丰富且有趣，用不同语言聊天，终于不再感觉像是在和机器人互动。编程或脚本方面我还在用 GPT6 和 Opus 5.5，但他们的聊天功能完全没有灵魂。

F：I'm a Pro tier subscriber and I've just asked it a couple regular text prompts in the Gemini app and it's burned through 52% of my 5-hour quota in seven different queries. Not even complex questions, mainly things where I'm asking basic questions like when will Argon come to Pro tier subscribers in the anti-gravity app, what model are you, and what's the information on the hallucination rate between 3.8 Flash and Argon. It burned through 52% of my quota just like that.  
我是专业版订阅者，刚刚在 Gemini 应用里问了几个普通文本提示，结果在七个不同的查询中，它已经用完了我 5 小时配额的 52%。甚至不是复杂的问题，主要是我问一些基础问题，比如 Argon 什么时候会在反重力应用的 Pro 级订阅用户中推出，你用的是什么型号，以及 3.8 Flash 和 Argon 之间的幻觉率信息。它就这样消耗了我 52%的配额。

I haven't even attempted more complex prompts where I normally use Gemini for legal research and legal drafting and document analysis, and I'm worried that I'll barely be able to start a basic legal task on Argon before running through my 5-hour limit.  
我甚至还没尝试过那些我通常用 Gemini 做法律研究、法律起草和文件分析的更复杂提示，我担心自己在 Argon 上几乎无法开始一个基本的法律任务，就要用完 5 小时的限制。

I also noticed that 3.1 Pro is burning through almost 7% per prompt now too, and I used to be able to use 3.1 Pro on extended thinking for long multi-hour sessions without burning through my 5-hour limit. Something is seriously different in how they're calculating quota usage when selecting the Pro model or the Argon model. 3.8 Flash I can use non-stop basically.  
我还注意到 3.1 Pro 现在每个提示几乎消耗了 7%的资源，而我以前能用 3.1 Pro 进行长时间的长时间思考而不用超过 5 小时的限制。他们在选择 Pro 型号或 Argon 型号时，计算配额使用的方式确实有很大不同。3.8 闪光灯我基本上可以不停使用。